M S Ramaiah Institute
of Technology
Department
of
M.Tech in
CNE
Computer
2014
Network
Engineering
Scheme and Syllabi for
batch 2014-16
1 CSE/PG-14-16
PEOS AND POS – B.E (COMPUTER NETWORK AND ENGINEERING)
PROGRAMME EDUCATIONAL OBJECTIVES (PEOS)
Program Educational Objectives (PEO)
Graduates of this M.Tech Computer Network and Engineering will be able to
1.Apply the necessary mathematical tools and fundamental & advanced knowledge
of Computer Network and Engineering
2. Develop computer network systems by understanding the importance of social,
business, technical, environmental, and human context in which the systems
would work
3. Articulate fundamental concepts, design underpinnings of communication &
Network systems, and research findings to train professionals or to educate
engineering students
4. Contribute effectively as a team member/leader, using common tools and
environment, in computer networks based projects, research, or education
5. Pursue life-long learning and research in the area of computer networks
,distributed systems and contribute to the growth of that field and society at
large
Program Outcomes (M.Tech.):
M.Tech. (Computer Network and Engineering) graduate must demonstrate
PO (a) An ability to apply knowledge of mathematics, science, and engineering as it applies to
Computer Network and Engineering
PO (b) An ability to design and conduct experiments, as well as to analyze and interpret data
PO (c) An ability to design a system, component, or process to meet the desired needs subject to
feasibility and sustainability
PO (d) An ability to function on multi-disciplinary teams
PO (e) An ability to identify, formulate, and solve Computer Network & Engineering problems
PO (f) An understanding of professional and ethical responsibility
PO (g) An ability to communicate effectively
PO (h) An understanding of the impact of computer engineering on economic, social and
environmental aspects
PO (i) A recognition of the need for, and an ability to engage in life-long learning
PO (j) A knowledge of contemporary issues
PO (k) An ability to use the techniques, skills, and modern engineering tools necessary for
computer engineering practice
PO (l) An ability to apply design and development principles of software and/or hardware
systems of varying complexity
PO (m) An understanding of the need for information and network security
2 Mapping of POs with PEOs
Program
Outcomes
PO(a)
PO(b)
PO(c)
PO(d)
PO(e)
PO(f)
PO(g)
PO(h)
PO(i)
PO(j)
PO(k)
PO(l)
PO(m)
PEO1
Program Educational Objectives
PEO2
PEO3
PEO4
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
3 PEO5
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
M S Ramaiah Institute of Technology
(Autonomous Institute, Affiliated to VTU)
Department of Computer Network and Engineering
Revised Scheme of Studies of Master of Technology in Computer Network
Engineering
(2014-16)
Core Courses
Electives
Project Work
Seminar
Industrial Training
I Semester M.Tech in Computer Network Engineering
Code
Subject
MCNE 111
Advanced Operating Systems
MCNE 112
Computer Networks Engineering
MCNE 113
Random Variables, Stochastic Processes and
Queuing Theory
MCNE 114
Topics in Digital Communications
MCNE 115
Software Development for Portable Devices
MCNE 116
Network Programming Lab
MCNE E
Elective 1
Elective 2
MCNE S01
Seminar
3
0
0
3
3
0
0
0
0
0
0
0
II Semester M.Tech in Computer Network Engineering
Code
Subject
MCNE 211
Wireless Sensor Networks
MCNE 212
Computer Security
MCNE 213
Protocol Engineering
MCNE S02
Seminar
MCNE E
Elective 3
MCNE E
Elective 4
Elective 5
MCNE 214
Computer Networks Laboratory
MCNE 215
Digital Communication Laboratory
L
3
3
3
0
3
3
3
0
0
Total
T P
0 1
1 0
1 0
0 1
0 0
0 0
0 0
0 2
0 2
III Semester
Code
MCNE 301
MCNE 302
MCNE E
MCNE E
MCNE 303
M.Tech in Computer Network Engineering
Subject
Industrial Training and Seminar*
Project Phase I
Elective 5
Elective 6
Cloud Computing Laboratory
IV Semester M.Tech in Computer Network Engineering
Code
Subject
MCNE S03
Seminar
MCNE 401
Project Phase II
4 L
3
3
4
L
0
0
3
3
0
Practical
Total Credits: 25
T P Credit CIE
0 1
4
50
0 1
4
50
0 0
4
50
1
1
1
0
0
1
SEE
50
50
50
4
1
1
3
3
1
50
50
50
50
50
50
50
50
50
-
Credits:
Credit
4
4
4
1
3
3
3
2
1
25
CIE
50
50
50
50
50
50
SEE
50
50
50
50
50
50
50
Total Credits: 25
T P
Credit CIE
0 7
7
50
0 10 10
50
0 0
3
50
0 0
3
50
0 2
2
50
Total
L T
0 0
0 0
Total
Credits: 25
P
Credit
2
2
23 23
CIE
50
100
50
50
SEE
50
50
50
SEE
100
Electives
MCSE E11: Computer Systems Performance Analysis
MCNE E 12: Big Data and Data Science
MCNE E28: Multimedia Communications
MCNE E13: Advances in Artificial Intelligence
MCNE E32: Software Architecture
MCNE E14: Fault Tolerant Systems
MCNE E33: Metrics and Models in Software Quality
MCNE E15:Analytical Approach in Data Networks
MSCE E16:Software Defined Networks
MCNE E35: Soft Computing
MCNE E37: VLSI Design and Algorithms
MCNE E18: Data Structures & Algorithms
MCNE E38: Analysis of Computer Networks
MCNE E20: Stochastic Process
MCNE E42: Advances in Storage Area Networks
MCNE E21: Advanced Algorithms
MCNE E43: GPU Programming using CUDA
MCNE E23: Embedded Computing Systems
MCNE E26: Web Technologies
MCNE E44: Information Retrieval
MCNE E30: Cloud Computing
MCNE E 45: Topics in Software Testing
L- Lecture, T – Tutorial, P-Practical. CIE-Continuous Internal Evaluation,
Evaluation.
SEE-Semester End
NOTE:
1. The hours/week shown in the column Tutorial/Practical is the contact hours for students.
The teachers should provide guidance.
2. Faculty handling M.Tech classes should give a lesson plan including the topics to be
covered in Tutorial by first week of the class.
3. Industrial Training and Seminar: The students should devote minimum 6 weeks after
availing 2 weeks of vacation at the end of 2nd Semester and before the Start of 3rd
Semester. The Training can be in any of the advanced technology of Relevance to
Industry. The training can be either a Certification / Summer Training inside the
Institution / Technology Survey / Mini project on Industry Relevant Recent Technology.
At the end of the Training, Student should give a seminar and demo of the Technology
learnt. For Industrial Training each student will be assigned a Mentor.
4. Project Phase I: This is carried out in addition to regular courses in the 3rd Semester.
During this phase, problem identification, literature survey, test plan, formation of
detailed specifications (SRS document), higher level design should be completed. A
report on this work must be submitted and a presentation on the same must be given at
the end of 3rd Semester. This is to be evaluated for 10 credits by the Department
Committee constituted for the purpose.
5. Project phase II: Project to be completed with detailed design, implementation, test case
preparations, testing and demonstration.
6. During the final project viva, students have to submit all the reports. The project
evaluation and viva-voce will be conducted by a committee constituted for this purpose.
7. The student should prepare a consolidated report in IEEE format and should submit it for
possible publication in National/International Conferences/Journals before the submission
of the Thesis.
8. The students should periodically meet their guide and maintain a log book with periodic
milestones achieved.
9. Seminar in first, second and fourth semesters should be given on the topics taken from
Research Articles from reputed Journals/Conferences.
5 I. Rubrics for Assessment of Student Performance in Seminar
Seminars are used as course delivery modes to encourage students to gather current trends in
technology, research literature, and self-learn topics of their interest. Seminars require students
to research a technical topic, make presentations and write a detailed document on their
findings.
The student is expected to :
1. Identify seminar topics based on contemporary technical, societal and environmental
issues.
2. Conduct literature survey on complex issues in the selected domain
3. Learn or explore advanced technologies
4. Make good oral and written technical presentations
Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Literature survey
Few sources, aware of
Multiple sources of
Multiple sources of high
quality of resources and
high quality, good
quality, well researched
relevance to problem at
judgment of the
and analyzed,
hand
information,,
continuous efforts at
identification of gaps
acquiring information
in knowledge
Report Writing
Reasonably good
Sound organization
Excellent organization,
organization and lacks
and structure, clear,
no technical or grammar
clarity in few topics,
very few errors,
errors, concise and
complete, few omissions,
complete, reasonably
precise, complete
grammatically correct,
good style
documentation
lacks style
Presentation and
Reasonably good
Good , professional
Excellent professional
viva voce
communication and
communication, good
and technical
presentation, able to give
visual aids, able to
communication,
technical answers to
give technical
effective presentations,
some extent
answers
able to analyze
technically and clarify
views in viva-voce
6 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Technical paper
Accepted in any National
Accepted and
Accepted in any
presentation in
Level Conferences/
Presented in any
International
reputed Journals
Journals
International
Conferences/Journals
Conferences/Journals
held in outside India
held in India.
Standards as IEEE/ACM.
or Conferences
II. Rubrics for Assessment of Student Performance in Industrial Training
Industrial Training is used as course delivery mode to encourage students to gather current trends in
Industry and the usage of newer technology in any of the reputed Industries. In this way the student
is able to gather the knowledge of working environment at the Industry during his/her Course.
At the end of the Industrial Training the Student is required to prepare a presentation on the training
at the Industry and present the same in front of the committee constituted at the Department.
He will be then accessed on the new technology Learnt, the documentation skills of the same,
exposure and demonstration of any Tools and Techniques Learnt and his presentation Skills.
Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
7 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
ools and new
Few sources at the Industry,
Multiple sources of
Multiple sources of high
Technology Learnt
aware of quality of resources
high quality, good
quality, well
and relevance to tools and
judgment of the
researched and
Techniques at hand
information,,
analyzed, continuous
identification of gaps in
efforts at acquiring
knowledge at the
information.
Industry and
Academics.
Identification of the
application of the tools
and Technology learnt
to the present market.
Report Writing
Reasonably good
Sound organization
Excellent organization,
organization and lacks clarity
and structure, clear,
no technical or
in few topics, complete, few
very few errors,
grammar errors,
omissions, grammatically
complete, reasonably
concise and precise,
correct, lacks style
good style
complete
documentation
Demonstration of
Moderately be able to
Efficiently be able to
Excellent
the Tools Learnt
demonstrate the tools learnt
demonstrate the skills
demonstration of the
at the Industry
learnt and be able to
tools and techniques
propose an application
learnt and be able to
for the same.
apply it to any simple
case study.
8 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Presentation and
Reasonably good
Good , professional
Excellent professional
viva voce
communication and
communication, good
and technical
presentation, able to give
visual aids, able to
communication,
technical answers to some
give technical answers
effective presentations,
extent
able to analyze
technically and clarify
views in viva-voce
III. Rubrics for Assessment of Student Performance in Project Phase I
Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Problem definition
Moderately aware, clear
Problem domain well
Has investigated
description, broad idea about
understood, clear and
problem domain
relevance to current
specific description of
extensively,
technical and social context
problem, relevance well
identified
Literature survey
Few sources, aware of quality
Multiple sources of high
Multiple sources of
of resources and relevance to
quality, good judgment of
high quality, well
problem at hand
the information,,
researched and
identification of gaps in
analyzed, continuous
knowledge.
efforts at acquiring
information
9 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Requirements
Multiple, clear, specific,
Many, varied, clear,
Complete
Specification
functional requirements
measurable requirements,
requirements –
include some non-
functional, non-
functional requirements
functional,
performance,
security related
considered, clear and
measurable
High Level Design
Mostly correct, minor errors
Technically correct, meets
Technically correct
/ System
in applying theory and
requirements
with innovative
Architecture
techniques, meets
application of theory
requirements
and techniques
Report Writing
Reasonably good organization
Sound organization and
Excellent
and lacks clarity in few topics,
structure, clear, very few
organization, no
complete, few omissions,
errors, complete,
technical or
grammatically correct, lacks
reasonably good style
grammar errors,
style
concise and precise,
complete
documentation
Presentation and
Reasonably good
Good , professional
Excellent
viva voce
communication and
communication, good visual
professional and
presentation, able to give
aids, able to give technical
technical
technical answers to some
answers
communication,
extent
effective
presentations, able
to analyze
technically and
clarify views in vivavoce.
10 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Attitude and team
Acceptable posture and
Appropriate posture and
Professional
work
appearance, team roles
appearance, Well defined
approach, team
defined but members fulfill
team roles, individualistic
members work with
only minimally
members who focus only on
synergy in their
their roles, lacks synergy
roles, help other
( Applicable if
working in
Industry )
team members also
IV. Rubrics for Assessment of Student Performance in Project Phase II Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Detailed Design
Implementation
Mostly correct, minor errors in
Technically correct,
Technically correct
applying theory and
meets requirements
with innovative
techniques, meets
application of theory
requirements
and techniques
Few errors in algorithms and
Correct algorithmic
Technically sound
programming style, correct
approach and choice of
implementation with
choice of hardware and
tools, meets deadlines
excellent programming
software tools, misses some
and schedule
style, s, finishes well
deadlines
within deadline
Testing and
Poor planning and specification
Able to identify test
Clear test plans
results
of test cases, meets functional
plans for most
created in advance,
requirements
requirements, meets
meets all
requirements
requirements,
optimized solution
11 Assessment
Level C
Level B
Level A
50-75
75-90
90-100
Criteria
% Marks to be
awarded
Report Writing
Reasonably good organization
Sound organization
Excellent organization,
and lacks clarity in few topics,
and structure, clear,
no technical or
complete, few omissions,
very few errors,
grammar errors,
grammatically correct, lacks
complete, reasonably
concise and precise,
style
good style
complete
documentation
Presentation and
Reasonably good
Good , professional
Excellent professional
viva voce
communication and
communication, good
and technical
presentation, able to give
visual aids, able to give
communication,
technical answers to some
technical answers
effective presentations,
extent
able to analyze
technically and clarify
views in viva-voce
Attitude and team
Acceptable posture and
Appropriate posture
Professional approach,
work
appearance, team roles
and appearance, Well
team members work
defined but members fulfill
defined team roles,
with synergy in their
only minimally
individualistic members
roles, help other team
who focus only on their
members also
(Applicable if
working in
Industry )
roles, lacks synergy
Technical paper
Accepted in any National Level
Accepted and
Accepted in any
presentation in
Conferences/ Journals
Presented in any
International
reputed Journals
International
Conferences/Journals
or Conferences
Conferences/Journals
held in outside India
held in India.
Standards as
IEEE/ACM.
12 Suggested Timelines for Activities and Deliverables
for
Project Phase-I (3rd semester) & Project Phase- II (4th semester)
Component
Timeline
Assessment
Project phase-I
End of 3rd semester-12th week
Interim Progress Assessment
Project phase-II
Evaluation-1
Mid of 4th semester -8th week
Design Development and
Solution
Project phase-II
Evaluation-2
End of 4th semester -13th week
Written Report
Final Presentation
Typical Project Activities and Timelines:
Timeline
Start time +
6 weeks
Start time +
8 weeks
Start time +
10 weeks
Start time +
13 weeks
Activity
Write very clearly scope/objective set for the project.
The objectives must reflect as to what exactly is
proposed to implement. Freeze the title and scope/
objectives and this will not change under normal
situation.
System design: Understand the overall system
functioning, identify and draw a system level block
schematic identifying all identified subsystems and
their input/output need. Prepare a list of hardware
systems, computing and network environment.
Similarly identify the software – operating systems,
application
software,
case
tools,
simulators,
databases, etc. Highlight what is already available
and what will be newly created or required for the
project.
Detailed Design: Design from the conceptual level
block schematic, a detailed architectural layout,
indicate every subsystem and within that identify
input/output and design for every small entity. Draw
functional block schematics, data flow diagrams for
every small entity and subsystem.
Formulate a test plan: list the test data at the inputs,
type of tests to be performed during development
and making of subsystems, and tests required during
runtime or execution.
Deliverable
System design document
Write the algorithms, the pseudo code for every
function call, the subroutines and the recursions.
Implement the design; execute the programs step by
step for each module. Debug, evaluate the
performance and validate the design. Integrate all
modules/subsystems to realize the over system.
Perform all system level tests, evaluate the results
and compare the project scope/objects and the
requirements
Complete all documentation and make the project
report ready for submission
Implementation and
testing document,
demo of working code
13 Detailed design
document
Complete project
report
Deliverables Student Performance in Industrial Training:
Industrial Training Title: ________________________________________
Company Name : _________________________________
Name of Student: _________________________________
Name of Supervisor at Company: _______________________________
Name of Supervisor at College: _______________________________
Each supervisor on the project must fill a rubric for each student
Basic
(0-4 Pts)
Good
(5-7 Pts)
Very Good
(10 Pts)
Tools and new
Technology
Learnt
Few sources at
the Industry,
aware of quality
of resources
and relevance
to tools and
Techniques at
hand
Multiple sources
of high quality,
good judgment of
the information,,
identification of
gaps in
knowledge at the
Industry and
Academics.
Relevance of
the topic
chosen to the
current
market
Report Writing
Fairly Relevant
Moderately
Relevant
Multiple sources of
high quality, well
researched and
analyzed, continuous
efforts at acquiring
information.
Identification of the
application of the
tools and Technology
learnt to the present
market.
Highly Relevant
Reasonably
good
organization
and lacks clarity
in few topics,
complete, few
omissions,
grammatically
correct, lacks
style
Moderately be
able to
demonstrate
the tools learnt
at the Industry
Sound
organization and
structure, clear,
very few errors,
complete,
reasonably good
style
Excellent
organization, no
technical or grammar
errors, concise and
precise, complete
documentation
10
Efficiently be able
to demonstrate
the skills learnt
and be able to
propose an
application for
the same.
Good ,
professional
communication,
good visual aids,
able to give
technical answers
Excellent
demonstration of the
tools and techniques
learnt and be able to
apply it to any simple
case study.
10
Excellent professional
and technical
communication,
effective
presentations, able
to analyze technically
and clarify views in
viva-voce
10
Demonstration
of the Tools
Learnt
Presentation
and viva voce
Reasonably
good
communication
and
presentation,
able to give
technical
answers to
some extent
14 Total
Possible
10
10
Earned
Deliverables before final project presentation:
You should submit the following documents to your supervisors
Interim Progress Assessment Rubric
Project: ________________________________________
Name of Student: _________________________________
Name of Supervisor: _______________________________
Each supervisor on the project must fill a rubric for each student
Problem
formulation
Selfmotivation
and project
management
Barely
acceptable
( 0 – 2 Pts)
• Bare
formulation
• Bare
understanding
of the problem,
with scarce
knowledge of
relevant material
• Slow
progress,
with barely
satisfactory
result
• Unresponsive
to
supervisor
Basic
(3 Pts)
Good
(4 Pts)
Very Good
(5 Pts)
• Basic
formulation
• Basic
understanding
of
the problem,
but
lack
appropriate
study of
relevant
material
• Clear
formulation
• Good
understanding
of
the problem,
with
study of
relevant
material
• Good
system
analysis
• Clear
formulation
with well
defined
scope
• Very good
understanding
of
the problem
and
relevant
material
• Near
production
quality
system
analysis
• Steady
progress
• Highly selfmotivated
Good
project
management
• Slow
progress,
with basic
project
outcome
• Rely on
supervisor’s
push
to work
• Good
progress
• Need
reminder
sometimes
• Minor
problems
in project
management
15 Total
Possible
5
5
Earned
Design Development and Solution Asset Rubric
Project: ________________________________________
Name of Student: _________________________________
Name of Supervisor: _______________________________
Each supervisor on the project must fill a rubric for each student
Barely acceptable
( 0 – 2 Pts)
Analysis and
solving
skills
Obvious
solution,
sketchy
functionalities
Innovation
in
the Design
Solution and
self-study
• Basic concepts
used correctly
• Lack self-study,
but apply
previously taught
technique on a
satisfactory level
Selfmotivation
and project
managemen
t
•
• Slow progress,
with barely
satisfactory result
• Unresponsive to
Supervisor
Basic
(3 Pts)
Good
(4 Pts)
Very Good
(5 Pts)
• Simple,
yet mostly
complete
solution that
solves the
stated
problem
• Superficial
usage of
new
concepts
• Self-study
of new
technique,
with basic
understandin
g
• Complete
solution with
nontrivial
functionalities
that meet the
desired needs
• Provide
solution
to complex
problems;
Solution
optimize
desired needs
• New concepts
used frequently
• Self-study of
new
technique and
solve technical
difficulties;
• Innovative
work
with research
value
• Slow
progress,
with basic
project
outcome
• Rely on
supervisor’s
push to
work
16 • Self-study
of new
concepts /
technique,
with
good
understandin
g
• Minor
innovative
Work
•
Good
progress
• Need
reminder
sometimes
• Minor
problems
in project
management
• Good
progress
• Need
reminder
sometimes
• Minor
problems
in project
management
Total
Possible
5
5
5
Earned
Written Report Rubric:
Project: ________________________________________
Name of Student: _________________________________
Name of Supervisor: _______________________________
Content
Writing
Barely
acceptable
( 0 – 2 Pts)
• Important
points
covered only
superficially
• No major
errors
and
misconception
• Frequent
errors
in spelling and
grammar
• Mostly
readable, but
a
few points are
hard to
understand
Basic
(3 Pts)
• Covers
important
points
• A few
inaccurate
or
irrelevant
points
• Some
errors in
spelling and
grammar
•
Reada
ble
•
Follow
basic
written
report
structure
Good
(4 Pts)
• All major
points
covered and
explained
clearly and
correctly
• Major
points
strongly
supported
with
suitable
detail
• A few
errors in
spelling and
grammar
• Readable
and
easy to
understand
• Well
proofread
• Clear and
easy to
understand
• Graphs and
diagrams
used
appropriately
17 Very Good
(5 Pts)
Total
Possible
5
5
Earned
Final Presentation Rubric
Project: ________________________________________
Name of Student: _________________________________
Name of Supervisor: _______________________________
Content
Presentation
Skills
Communication
(Q/A)
Barely
acceptable
( 0 – 2 Pts)
• Important
points
covered only
superficially
• No major
errors
and
misconception
• Bare
organization
and
preparation
• Lack of
confidence
and familiarity
in
some parts of
the
presentation
• Answer at
least
one questions
correctly
• Need
clarification
Basic
(3 Pts)
Good
(4 Pts)
Very Good
(5 Pts)
• Covers
important
points
• A few
inaccurate
or
irrelevant
points
• Basic
organization
and
preparation
• Confident
in
only some
parts of the
presentation
• All
major
points
covered
and
explained
clearly and
correctly
• Good
organizatio
n
and
preparat
ion
• Confiden
t in
most parts
of
the
presenta
tion
Attractive
to
audience
• Answer
most
questions
correctly
and
concisely
• Major
points
strongly
supported
with
suitable detail
• Answer
most
questions
correctly
• Need
clarification
sometimes
18 • Excellent
organization
and
preparation
Confident and
relaxed in the
whole
• presentatio
n
Engaging to
audience
• Handle
difficult
questions with
ease and
confidence
• Illustrative
explanation
Total
Possible
5
5
5
Earned
STUDENT SEMINAR EVALUATION RUBRIC
Student Name _________________________Date ______________
Title/Topic _________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Evaluate the student’s presentation employing the following range-scored criteria
Speaking
Skills &
Elocution not
ability to
speak
English
language
Barely
acceptable
( 0 – 2 pts)
• Mumbles
and/or
Incorrectly
pronounces
some terms
Voice is low;
difficult to hear
Audience
interaction
Completely lost
audience
attention;
started
responding
before
questions
finished;
answers often
unrelated
to the question
asked
Subject
knowledge
Does not have
grasp of
information;
cannot
answer
questions
about
subject.
Organizatio
n of
presentatio
n
• Hard to follow;
sequence
of information
jumpy
Basic
(3 Pts)
Good
(4 Pts)
Very Good
(5 Pts)
• Incorrectly
pronounces
some terms
Voice
fluctuates
from low to
clear; difficult
to
hear at times
• Incorrectly
pronounces
few terms
Voice is clear
with few
fluctuations;
audience
can hear well
most of the
time
Held audience
attention
most of the
time; polite
in
answering
questions,
but
not as directly
• Correct,
precise
pronunciation of
all
terms Voice is
clear and
steady;
audience can
hear well at all
times
Held audience’s
attention
throughout,
points made
in creative way;
listened
carefully to
audience
questions and
responded
directly to
question asked
5
Adequate
knowledge of
most topics;
answers
questions, but
fails to
elaborate
Demonstrates
in depth
knowledge;
answers
questions with
explanations
and
elaboration
5
• Information
presented in
logical
sequence;
easy to
follow
• Information
presented as
interesting story
in logical,
easy to follow
sequence
5
Difficulty
holding
audience
attention, facts
presented
with little or no
imagination;
lengthy
answers,
sometimes
without
answering the
question asked
Superficial
knowledge of
topic; only able
to answer
basic questions
• Most of
information
presented in
sequence
19 Total
Possible
5
Earned
Evaluate the student’s presentation employing
Basic
Barely
(3 Pts)
acceptable
( 0 – 2 pts)
Material
Background Material not
sufficient for
content
clearly
clear
related to topic
understanding
OR
but not clearly
background
presented
dominated
seminar
Methods
Methods too brief Sufficient for
understanding
or
but not
insufficient for
clearly
adequate
presented
understanding
OR too
detailed
• Majority
• Some figures
Results
appropriately
hard to Read
(figures,
Formatted
• Some in
graphs,
• Reasonably
inappropriate
tables,
explained
Format
etc.)
• Significance
• Some
mentioned
explanations
lacking
Uses graphics
Uses graphics
Graphics
that
that rarely
(use
relate to text
support text and
of
and
presentation
Powerpoint
presentation
)
• Refers to
Eye contact
• Reads most
slides to make
and Length
slides; no or
points;
and Pace
just occasional
occasional eye
eye contact
contact
• Short; less
• Short 40 min
than 30 min
OR long
• Rushed OR
>50
dragging
• Rushed OR
throughout
dragging in
parts
conclusions
Conclusions conclusions not
could be
supported
supported by
by evidence; no
stronger
discussion of
evidence;
implications
minimal
and future work
discussion of
implications
and future work
the following range-scored criteria
Good
Very Good
Total
(4 Pts)
(5 Pts)
Possible Earned
Material
sufficient for
clear
understanding
AND
effectively
presented
Sufficient for
understanding
AND
effectively
presented
Material
sufficient for
clear
understanding
AND
exceptionally
presented
Sufficient for
understanding
AND
exceptionally
presented
5
• Most figures
clear
• Most
appropriately
Formatted
• Well
explained
• All figures clear
• All
appropriately
formatted
• Exceptionally
explained
5
Uses graphics
that
explain text
and
presentation
• Refers to
slides to
make points;
eye contact
majority of
time
• Adequate
40-45 min
• Most of the
seminar well
pace
conclusions
supported by
evidence;
some
discussion of
implications
and future
direction
Uses graphics
that explain
and reinforce
text and
presentation
• Refers to slides
to make
points;
engaged with
Audience
• Appropriate
(45-50 min)
• Well-paced
throughout
5
insightful
conclusions
supported by
evidence;
discusses
implications
and application;
recommends
future
directions for
research
5
20 5
5
Semester: I
Course Title: Advances in Operating Systems
Credits (L:T:P:SS) : 3:0:1
Type of Course: Lecture, Practicals
Year: 2014- 2016
Course Code: MCNE 111
Core/ Elective: Core
Total Contact Hours: 48 Hrs
Prerequisites: Operating Systems
Prerequisites: Operating Systems
Course Objectives:
The objective of this course is to make the students to
•
Identify the basic resource management responsibilities of an operating system
•
Understand the concept of a process, list the various process state transitions and distinguish
between a process from a thread
•
Design and implement a concurrent programming application using semaphores & monitors for
process control
•
Analyze the necessary conditions for deadlock, implement deadlock avoidance, prevention &
recovery, Understand virtual memory concepts, paging and segmentation
•
Implement process and disk scheduling algorithms, provide protection using access controls.
Course Contents:
Unit I
Process Synchronization: Overview, Synchronizations mechanisms – Introduction, concept of a process,
concurrent processes, critical section problem, other synchronization problems. Distributed Operating
Systems: Architectures of Distributed Systems - System Architecture types, issues in distributed operating
systems, communication networks - communication primitives Theoretical Foundations - inherent
limitations of a distributed system lamp ports logical clocks - vector clocks - casual ordering of messages global state -cuts of a distributed computation - termination detection.
Unit 2
Distributed Mutual Exclusion - introduction - the classification of mutual exclusion and associated
algorithms - a comparative performance analysis Distributed Deadlock Detection -Introduction - deadlock
handling strategies in distributed systems - issues in deadlock detection and resolution - control
organizations for distributed deadlock detection - centralized and distributed deadlock detection algorithms
hierarchical deadlock detection algorithms.
Unit 3
Agreement protocols - introduction-the system model, a classification of agreement problems, applications
of agreement algorithms. Distributed resource management: Distributed File Systems- introductionarchitecture - mechanism for building distributed file systems - design issues - log structured file systems.
Distributed shared memory-Architecture- algorithms for implementing DSM - memory coherence and
protocols - design issues.
Unit 4
Distributed Scheduling - introduction - issues in load distributing components of a load distributing
algorithm - stability load distributing algorithm - performance comparison selecting a suitable load sharing
algorithm requirements for load distributing -task migration and associated issues. Failure Recovery:
Introduction- basic concepts - classification of failures - backward and forward error recovery, backward
error recovery- recovery in concurrent systems - consistent set of check points - synchronous and
asynchronous check pointing and recovery check pointing for distributed database systems- recovery in
replicated distributed databases.
Unit 5
21 Protection and security Resource Security and Protection:-preliminaries, the access matrix model and its
implementations safety in matrix model- advanced models of protection Multiprocessor operating systems:
Multiprocessor System Architecturesbasic multiprocessor system architectures - inter connection
networks for multiprocessor systems - caching - hypercube architecture. Multiprocessor Operating System structures of multiprocessor operating system, operating system design issues- threads Process
synchronization issues related to instructions Process scheduling : issues, co-scheduling, Smart scheduling.
Laboratory Work:
(The following programs can be executed on any available and suitable platform)
1. Design, develop and execute a program using any thread library to create the number of threads
specified by the user; each thread independently generates a random integer as an upper limit, and
then computes and prints the number of primes less than or equal to that upper limit along with
that upper limit.
2. Rewrite above program such that the processes instead of threads are created and the number of
child processes created is fixed as two. The program should make use of kernel timer to measure
and print the real time, processor time, user space time and kernel space time for each process.
3. Design, develop and implement a process with a producer thread and a consumer thread which
make use of a bounded buffer (size can be prefixed at a suitable value) for communication. Use
any suitable synchronization construct.
4. Design, develop, and execute a program to solve a system of n linear equations using Successive
Over-relaxation method and n processes which use Shared Memory API.
5. Design, develop, and execute a program to demonstrate the use of RPC.
Text Book :
1. Mukesh Singhal, Niranjan G.Shivaratri, "Advanced concepts in operating systems: Distributed,
Database and multiprocessor operating systems", TMH, 2009
Reference Books:
1. Andrew S.Tanenbaum, "Modern operating system", PHI, 2003
2. Pradeep K.Sinha, "Distributed operating system-Concepts and design", PHI, 2003.
3. Andrew S.Tanenbaum, "Distributed operating system", Pearson education, 2003.
Course Delivery:
The course will be delivered through lectures, class room interaction, Practicals, Self-Study, online courses,
group discussion, and demonstrations.
Course Assessment and evaluation:
Indirect
Assessment
Methods
Direct Assessment
Methods
What
CIE
SE
E
Internal
Assessment
Tests
Practical, Mini
Project and
Certification
To
Whom
Students
Standard
Examination
Students
Feedback
Students
When/ Where
(Frequency in
the course)
Max
Marks
Evidence
Collected
Contribution
to Course
Outcomes
Thrice(Average of
the best two will
be computed)
25
Blue Books
1,2, 3,4 & 5
Once
25
Code
Reposition,
Certificates
2&4
End of Course
(Answering
5 of 10
questions)
100
Answer scripts
1,2, 3,4 & 5
Questionnaire
1,2, 3,4 & 5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
End of the course
22 -
Course Outcomes:
At
1.
2.
3.
4.
5.
the end of the course the students should be able to:
Understand and implement concurrent processes
Understand the basic concepts of Distributed Operating Systems and its architecture
Identify the Distributed resource management and design issues
Implementation of CPU scheduling, IPC memory management, recovery and concurrent algorithms
Understand the concepts of multiprocessor operating systems and to apply related algorithms
Mapping course outcomes with program outcomes:
Course Outcomes
Program Outcomes
PO
(a)
x
PO(
b)
x
PO
(c)
x
Understand the basic concepts of
Distributed Operating Systems
and its architecture.
x
x
x
x
x
Identify the Distributed resource
management and design issues.
Implementation of CPU
scheduling, IPC memory
management, recovery and
concurrent algorithms.
Understand the concepts of
multiprocessor operating systems
and to apply related algorithms
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Understand and implement
concurrent processes.
PO
(d)
x
23 PO(
e)
x
PO
(f)
PO
(g)
PO
(h)
PO(
i)
PO
(j)
x
PO(
k)
PO(
l)
PO
(m)
Semester: I
Year: 2014 - 2016
Course Title: Computer Networks Engineering
Course Code: MCNE 112
Credits (L:T:P:SS) : 3:0:1
Core/ Elective: Core
Type of course: Lecture/ Laboratory/ /Project/
study & Assignment
Self-
Total Contact Hours: 48
Prerequisites:
Data Communications
Course Objectives:
The objective of this course is to make the students to
•
•
•
•
•
Understand the networks architecture and quantitative performance metrics that drive network
design.
Discuss the basic model of switched networks and key elements of the Internet Protocol.
Examine the various concepts of Internet and principles of the TCP/IP protocol suite.
Analysis the how congestion control works and concepts of resource allocation.
Identify the significance of application layer and protocol they use.
Course Contents:
Unit 1
Review of Basic Concepts: Building a Network; Applications; Requirements; Network Architecture;
Implementing Network software; Performance; Physically connecting hosts; Hardware building blocks.
Unit 2
Packet Switching: Switching and forwarding; Bridges and LAN Switches; Cell Switching; Implementation
and Performance. Internetworking: Simple internetworking (IP); Routing; Global Internet; Multicast; MPLS.
Unit 3
End–to-End Protocols: Simple De-multiplexer (UDP); Reliable byte stream (TCP); RPC; RTP.
Unit 4
Congestion Control and Resource Allocation: Issues in resource allocation; Queuing discipline; TCP
Congestion Control; Congestion-Avoidance mechanisms; Quality of Service.
Unit 5
Applications: Traditional applications; Web services; Multimedia applications; Overlay Networks.
Text Book:
1. Larry L. Peterson and Bruce S. Davie: Computer Networks – A Systems Approach, 5th Edition,
Elsevier, 2011.
Reference Books:
1. Behrouz A. Forouzan: Data Communications and Networking, 4th Edition, Tata McGraw Hill, 2012.
2. William Stallings: Data and Computer Communication, 8th Edition, Pearson Education, 2012.
3. Alberto Leon-Garcia and Indra Widjaja: Communication Networks -Fundamental Concepts and Key
Architectures, 2nd Edition Tata McGraw-Hill, 2011.
Laboratory Work:
Using any Protocol Analyzer like Ethereal, perform the following experiments:
1. Capture the packets that are transmitted after clicking on the URL of the web site of your college.
Analyze the packets at the highest level and prepare a brief report of your analysis.
2. Analyze the data captured above at lower levels and demonstrate the layering of the protocols.
3. Capture the ARP packets and find the MAC addresses in the LAN in your laboratory.
Using either NS2/OPNET or any other suitable simulator, perform the following experiments:
1. Simulate a three nodes point – to – point network with duplex links between them. Set the queue
size and vary the bandwidth and find the number of packets dropped.
2. Simulate the transmission of ping messages over a network topology consisting of 6 nodes and find
the number of packets dropped due to congestion.
3. Simulate an Ethernet LAN using n nodes and set multiple traffic nodes and plot congestion window
for different source / destination.
24 Mini Project based on the concepts of Computer Networks Engineering.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion and lab exercises.
Course Assessment Methods:
Indirect
Assessment
Methods
Direct
Assessment
Methods
What
C
I
E
S
E
E
To
Whom
Internal
Assessment
Tests
Lab Tests
Student
s
Standard
Examination
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Thrice(Average of the
best two will be
computed)
30
Blue Books
1,2 ,3,4 & 5
Twice
20
Data sheet
1,2,3,4 &5
End of Course
(Answering 5 of 10
questions)
100
Answer
scripts
1,2,3,4&5
Questionna
ire
1,2,3,4&5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Students
Feedback
End of Course
Survey
Student
s
End of the course
-
a. CIE Scheme:
Questions for CIE and SEE will be designed to evaluate the various educational components (Bloom’s
taxonomy)
Course Outcomes:
1. Demonstrate knowledge of basic networking concepts and their performance metrics.
2. Understands the basic model of switched networks and key elements of the Internet Protocol.
3. Understands the various concepts of Internet and principles of the TCP/IP protocol suite.
4. Understanding the how congestion control works and concepts of resource allocation.
5. Understand the significance of application layer and protocol they use..
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Program Outcomes
PO PO PO PO PO PO PO PO PO PO PO PO
(a) (b) (c) (d) (e) (f) (g) (h) (i)
(j)
(k) (l)
X
X
X
X
X
X
X
X
X
Demonstrate knowledge of basic
networking concepts and their
performance metrics.
Understands the basic model of
X
X
X
X
X
X
X
X
switched networks and key
elements of the Internet Protocol
X
X
X
X
X
X
X
X
Understands the various
concepts of Internet and
principles of the TCP/IP protocol
suite
X
X
X
X
X
X
X
X
Understanding the how
congestion control works and
concepts of resource allocation
Understand the significance of
X
X
X
X
X
X
X
X
application layer and protocol
they use
25 PO
(m)
X
X
X
X
X
Semester: I
Year: 2014 - 2016
Course Title: Random Variables, Stochastic Processes
and Queuing Theory
Course Code: MCNE 113
Credits (L:T:P:SS) : 4:0:0
Core/ Elective: Core
Type of course:
assignment
Lecture/
Laboratory/
/Project/
Total Contact Hours: 48
Prerequisites:
Undergraduate degree
Course Objectives:
•
Learn the concept of discrete and continuous random variable
•
Understand the Probability mass and density function and probability distributions
•
Learn the theory of Random process and special classes of random variable
•
Learn the concept of continuous time Markov Chains, pure birth , pure death, birth and death process
•
Learn the concept of different queuing models such as M/M/1, M/G/1.
Course Contents:
UNIT -1
Discrete Random Variables: Introduction, Random variables and their event spaces, Probability Mass
function. Distribution functions, Special Distribution functions, Independent Variables.
UNIT -2
Continuous Random Variables: Introduction, Exponential Distribution, Functions of a random variable,
Jointly distributed random variables, Functions of normal random variables.
UNIT -3
Random Processes: Introduction, Binomial Process, Poisson Process, Ergodic Process, Special Classes of
Random Process.
UNIT -4
Markov Process: Markov chain and Transition Probabilities, Continuous Parameter Markov chain, Pure
birth and pure Death Process, Birth and Death Process, Renewal Process.
UNIT -5
Introduction to Queuing theory and applications: Single server with infinite system capacity, queuing
Modes (M/M/1):( ∞ /FIFO),(M/M/1):(k/FIFO),(M/M/s):( ∞ /FIFO),
(M/M/s):(k/FIFO),M/G/1 Queuing system characteristics, Case Studies
Text Books:
1. Sheldon M Ross – Introduction to Probability Models, 10th Edition, Elsevier, 2010
2. T Veera Rajan – Probability, Statistics and Random Process, 3rd Edition, Tata Mc Graw Hill, 2008
Reference Books:
1. Kishore S Trivedi – Probability & Statistics with Reliability, Queuing and Computer Network
Applications, 2nd edition, John Wiley & Sons, 2008.
2. Athanasios Papoulis. S, Unni Krishna Pillai – Probability, Random Variables and Stochastic
Processes, 4th edition, Tata Mc. Graw Hill, 2002.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion and lab exercises.
26 Course Assessment Methods:
Indirect
Assessment
Methods
Direct
Assessment
Methods
What
C
I
E
S
E
E
Internal
Assessment
Tests
Lab Tests
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Thrice(Average of the
best two will be
computed)
30
Blue Books
1, 2, 3, 4 & 5
Twice
20
Data sheet
1, 2, 3, 4 & 5
End of Course
(Answering 5 of 10
questions)
100
Answer
scripts
1, 2, 3, 4 & 5
Questionna
ire
1, 2, 3, 4 & 5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
To
Whom
Student
s
Standard
Examination
Students
Feedback
End of Course
Survey
Student
s
End of the course
-
b. CIE Scheme:
Questions for CIE and SEE will be designed to evaluate the various educational components (Bloom’s
taxonomy)
Course Outcomes:
The students will be able to
1. Recognize the random variable governing the problem
2. Discuss the different features of the probability distribution which is used in statistical models.
3. Uses of knowledge random processes, evaluate various measures of the system effectiveness such as
response time, reliability, etc,
4. Recognize the types of queues and discusses its queuing system characteristics.
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Recognize the random
variable governing the
problem
Discuss the different features
of the probability distribution
which is used in statistical
models
Uses of knowledge random
processes, evaluate various
measures of the system
effectiveness such as
response time, reliability, etc
Recognize the types of
queues and discusses its
queuing system
characteristics
PO
(a)
X
PO
(b)
PO
(c)
X
PO
(d)
X
Program Outcomes
PO PO PO PO PO
(e) (f) (g) (h) (i)
X
X
X
X
PO
(k)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
27 PO
(j)
X
PO
(l)
PO
(m)
X
Semester: I
Year: 2014-15
Course Title: Topics on Digital Communication
Course Code: MCNE 114
Credits (L:T:P) : 4:0:0
Core
Type of Course: Lecture/Seminar
Total Contact Hours: 48
Prerequisites:
The student should have undergone the course on Analog and Digital circuits
Course Objectives :
Objectives of this course is to :
•
Understanding of the why digital communication and its fundamentals
•
Understanding different characteristics of a digital channel that effect capacity and various conducting media of
transmission
•
Understanding the error detection and correction and channel capacity.
•
A brief understanding of waveform coding techniques.
•
Understanding the base band shaping and the various algorithms employed.
Course Contents:
UNIT 1
Digital Transmission Fundamentals: Digital Representation of Information: Block-Oriented Information,
Stream Information; Why Digital Communications? Comparison of Analog and Digital Transmission , Basic
properties of Digital Transmission Systems; Digital Representation of Analog Signals: Bandwidth of Analog
Signals, Sampling of an Analog Signal, Digital Transmission of Analog Signals; Characterization of
Communication Channels: Frequency Domain Characterization, Time Domain Characterization;
Fundamental Limits in Digital Transmission:
UNIT 2
The Nyquist Signaling Rate, The Shannon Channel Capacity; Line Coding ; Modems and DigitalModulation:
Binary Phase Modulation, QAM and Signal Constellations, Telephone Modem Standards; Properties of Media
and Digital Transmission Systems: Twisted Pair, Coaxial CableOptical Fiber, Radio Transmission, Infrared
Light; Error Detection and Correction: Error Detection, Two Dimensional Parity Checks, Internet Checksum,
Polynomial Codes, Standardized Polynomial Codes, Error Detecting Capability of a Polynomial Code.
UNIT 3
Brief Review of digital communication systems: Elements of Digital communication systems;
Communication channels and their characteristics; Historical perspective in the development of digital
communication; Review of the features of a decreases memory less channel and the channel capacity
theorem.
UNIT 4
Wave form Coding Techniques: PCM, Channel. Noise and error probability, DPCM, DM, coding speech at
low bit rates, Applications.
UNIT 5
Base band Shaping for data transmission: Discrete PAM signals, Inter-symbol interference (ISI)
Nyquist criterion for distortion-less Base band binary transmission, correlative coding, Eypattern,
transmission, correlative coding, Eypatterns Based and M-ary PAM system, Adoptive Equalization, The zero
forcing algorithm, The LMA algorithm
Text Books:
1. Alberto Leon – Garcia and Indra Widjaja: Communication Networks - Fundamental Concepts
and Key architectures, 2nd Edition, Tata McGrawHill, 2006.
2. Simon Haykin: Digital Communication, Wiley India, 2007.
Reference Books:
1. John G Proakis: Digital Communications, 3rd Edition, McGraw Hill, 2008.
2. Leon W Couch: Analog / Digital Communication, 5th Edition, PHI, 2008.
Course Assessment and evaluation:
28 To
Whom
Indirect
Assessment
Methods
Direct Assessment
Methods
What
CIE
Internal
Assessment
Tests
Class-room
Surprise Quiz
SEE
Students
When/ Where
(Frequency in
the course)
Thrice(Average of
the best two will be
computed)
Twice(Summation
of the two will be
computed)
End of Course
(Answering
5 of 10 questions)
Standard
Examination
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,4 & 5
20
Class-room
Surprise Quiz
2&3
100
Answer scripts
1,2, 3,4 & 5
Questionnaire
1, 2 & 3,
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Students
Feedback
Students
End of the course
-
End of Course
Survey
Course Outcomes:
At the end of the course students should be able to:
1) Explain necessity of digital communication and its fundamentals
2) Must have an understanding of various characteristics of a digital channel
3) Examine error detection, correction and channel capacity for a given scenario
4) Various waveform coding techniques advantages and disadvantages
5) Analyze the base band shaping and the various algorithms employed.
Mapping Course Outcomes with Program Outcomes:
Program Outcomes
Course Outcomes
PO
(b)
PO
(c)
Explain nessacity of digital
communication
and
its
fundamentals
x
x
Must have an understanding
of various characteristics of a
digital channel
x
Examine
error detection,
correction
and
channel
capacity for a given scenario
Various
waveform coding
techniques advantages and
disadvantages
x
Analyse
the base band
shaping and the various
algorithms employed.
PO
(a)
x
x
x
x
x
PO
(d)
PO
(e)
PO
(g)
PO
(h)
x
x
x
x
PO
(i)
PO
(j)
PO
(k)
x
x
x
x
x
x
x
x
x
x
x
29 PO
(f)
x
x
x
x
x
x
x
PO
(l)
Semester: I
Year: 2014 - 2016
Course Title: Software Development for Portable Devices
Credits (L:T:P) : 0:0:1
Type of course: Practical
Course Code: MCNE 115
Core/ Elective: Core
Total Contact Hours: 24
Prerequisites:
Basics of Java
Course Objectives:
The objective of this course is to make the students to
1. Introduction to Mobile Computing and Emerging mobile application and Hardware Platforms.
2. Developing and accessing mobile applications.
3. Software Lifecycle for Mobile Application - Design and Architecture, Development – Tools,
Techniques, Frameworks, Deployment.
4. Human factors and emerging HCI interfaces (tangible, immersive, attentive, gesture, zero-input).
5. Select Application domains such as Pervasive Health Care, m-Health. Mobile Web browsing, Gaming
and Social Networking.
Course Contents:
Experiments that are to be conducted as a part of the course:
1. Developing simple android applications for mobile devices & introducing to android development
tools.
2. Simple programs to investigate the android life cycle.
3. Programs on Android activities.
4. Programs to create user interfaces, layouts and views.
5. Program to create and use menus.
6. Programs on using intents, adapters and dialogs.
7. Programs on working with data storage.
8. Programs on saving and loading files.
9. Programs on introducing content providers.
10. Programs on location based services and Google maps.
11. Developing applications to work with threads.
12. Introducing Toasts and Notifications.
13. Developing applications to work with messaging.
14. Programs to work with hardware of mobile devices.
Course Contents:
The student will execute a mini project using Android, write a report and demonstrate to the examiner.
Prescribed Text Book
T1
Professional Android 4 Application Development, by Reto Meier, WROX Press, Wiley Publishing.
Reference Book (S)
R1.
Android Application Development, Programming with the Google SDK, by, Rick Rogers, John
Lombardo, Zigurd Mednieks, Blake Meike, SPD, Oreilly, ISBN10: 81-8404-733-9, ISBN13:978-818404-733-2
R2.
Hello Android, Introducing Google’s Mobile Development Platform, 3rd Edition, by Ed
Burnette, Pragmatic Programmers, LLC.ISBN-10: 1-934356-56-5, ISBN-13: 978-1934356-56-2
30 Course Assessment Methods:
Indirect
Assessment
Methods
Direct
Assessme
nt
Methods
What
CI
E
S
E
E
Mini
Project
Standard
Examinati
on
Students
Feedback
End of Course
Survey
To
Whom
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Once
50
Project
Documentation
1,2,3,4 &5
Project demo
50
Answer scripts
1,2,3,4&5
Questionnaire
1,2,3,4&5
Effectiveness
of Delivery of
instructions &
Assessment
Methods
Student
s
Student
s
End of the course
-
Course Outcomes:
1. Demonstrate knowledge of android platform and different android developer tools.
2. Understands the android application life cycle and creating UI and activities.
3. Demonstrating the concept of sending messages between application components and also data
storage, retrieval, and sharing.
4. Demonstrate the knowledge of using location-based services, Threads, and Notifications in
applications.
5. Understands the Android’s communication abilities using SMS and ability to work with mobile
hardware like camera, accelerometers, and compass sensors.
.
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Demonstrate knowledge of
android platform and different
android developer tools
Understands the android
application life cycle and
creating UI and activities
Demonstrating the concept of
sending messages between
application components and
also data storage, retrieval, and
sharing
Demonstrate the knowledge of
using location-based services,
Threads, and Notifications in
applications
Understands the Android’s
communication abilities using
SMS and ability to work with
mobile hardware like camera,
accelerometers, and compass
sensors
PO
(a)
X
PO
(b)
PO
(c)
X
PO
(d)
X
X
X
X
X
X
X
X
X
X
31 Program Outcomes
PO PO PO PO PO
(e) (f) (g) (h) (i)
X
X
PO
(j)
PO
(l)
PO
(a)
X
X
X
X
PO
(k)
X
X
X
X
X
X
X
X
Rubrics for Assessment of Student Performance:
Trait Barely Acceptable Basic Good Very Good The program
produces correct
results but does not
display them
correctly.
The program works
and produces the
correct results and
displays them
correctly. It also
meets most of the
other specifications.
The program works
and meets all of the
specifications.
Specifications
The program is
producing
incorrect results.
Readability
The code is poorly
organized and very
difficult to read.
The code is
readable only by
someone who
knows what it is
supposed to be
doing.
The code is fairly
easy to read.
The code is
exceptionally well
organized and very
easy to follow.
Reusability
The code is not
organized for
reusability.
Some parts of the
code could be
reused in other
programs.
Most of the code
could be reused in
other programs.
The code could be
reused as a whole or
each routine could be
reused.
Documentation
The documentation
is simply comments
embedded in the
code and does not
help the reader
understand the
code.
The documentation
is simply comments
embedded in the
code with some
simple header
comments
separating routines.
The documentation
consists of
embedded comment
and some simple
header
documentation that
is somewhat useful
in understanding the
code.
The documentation is
well written and
clearly explains what
the code is
accomplishing and
how.
Delivery
The code was more
than 2 weeks
overdue.
The code was
within 2 weeks of
the due date.
The program was
delivered within a
week of the due
date.
The program was
delivered on time.
Efficiency
The code is huge
and appears to be
patched together.
The code is brute
force and
unnecessarily long.
The code is fairly
efficient without
sacrificing
readability and
understanding.
The code is extremely
efficient without
sacrificing readability
and understanding.
32 STUDENT EVALUATION RUBRIC
Student Name _________________________Date ______________
Question:
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
________________________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Sl
No.
Components
Marks
Allotment
Sub - Components
Marks
Scored
sub
Total
sub
Total
Marks
Marks
Scored
1
6
Program Write up
Lab Test 1
4
Viva
3
20
10
Program Execution
25
Project Implementation
30
Mini Project
5
Project report
50
Total Marks
33 Semester: I
Course Title: Network Programming Lab
Credits (L:T:P) : 0:0:1
Type of course: Practical
Year: 2014-2016
Course Code: MCNE 116
Core/ Elective: Core
Total Contact Hours:2 hours
per week
Prerequisites:
Undergraduate degree
Course Objectives:
•
To learn the basics of socket programming using TCP Sockets.
•
To learn basics of UDP sockets.
•
To learn about raw sockets.
•
To implement few standard network protocols
•
To develop knowledge of threads for developing high performance scalable applications.
Course Content:
Write programs using C/C++ or java to implement the following
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Echo Server using TCP and UDP socket programming.
HTTP Client and HTTP server using TCP socket programming.
DNS Client and DNS server using UDP socket programming.
Capture and to block the packets over the network using raw sockets.
Sliding Window protocols (stop and wait, Go back N, Selective Repeat)
TRACE ROUTE command.
Telnet Client.
File Transfer Protocol.
IPC - Pipes, FIFO and Message Queue.
Chat client and Chat server using Java socket programming.
Multicasting Program using Java socket programming.
Remote Procedure Call for sorting any given set of numbers.
RPC to perform String Conversion from Lower case to Upper Case and vice versa.
Generate SIGPIPE Error with Socket.
Restart server by capturing SIGHUP signal .
Text Books:
1. W. Richard Stevens:Unix Network Programming, PHI
2. W. Richard Stevens, Bill Fenner, Andrew M. Rudoff : Unix Network Programming The Sockets
Networking API , Volume 1, Third Edition, PHI.
3. W. Richard Stevens:Unix Network Programming Interprocess Communications Volume 2, Second
edition, PHI.
4. Elliotte Rusty Harold: Java Network Programming, 3rd Edition, Shroff Publishers.
REFERENCE BOOKS:
1. W. Richard Stevens: TCP/IP Illustrated, Volumes 1, 2, and 3, Pearson, 2000.
Course Delivery:
The course will be delivered through lab exercises.
34 Course Assessment Methods:
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Lab Tests
Twice
40
Answer
Sheets
1,2 ,3,4 & 5
Practical
assignments
Once
10
Data
sheets
1,2,3,4 &5
End of Course
100
Answer
scripts
1,2,3,4&5
Questionna
ire
1,2,3,4&5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Direct Assessment Methods
What
C
I
E
S
E
E
Standard
Examination
To
Whom
Student
s
End of Course
Survey
End of the course
-
Course Outcomes
1.
Write both connection-oriented and connectionless servers and clients using the Sockets API.
2.
Understand the Unix process model, standard Unix input/output, and their system calls
3.
Use signals and their associated system calls in Unix
4.
Understand and implement the most widely used network application protocols such as ftp, telnet,
ping, etc to develop distributed applications
5.
Understand multithreading and implement it in Unix.
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Write either connectionoriented or connectionless
servers and clients using the
Sockets API
Understand the Unix process
model, standard Unix
input/output, and their
system calls
Use signals and their
associated system calls in
Unix
Understand and implement
the most widely used network
application protocols such as
ftp, telnet, ping, etc to
develop distributed
applications
Understand multithreading
and implement it in Unix.
PO
(a)
X
PO
(b)
X
PO
(c)
X
X
X
X
X
X
X
X
PO
(d)
X
X
PO
(f)
X
PO
(g)
PO
(h)
PO
(i)
PO
(j)
PO
(k)
PO
(l)
x
X
X
x
X
X
x
X
X
X
X
35 PO
(e)
X
x
x
PO
(m)
x
Rubrics for Assessment of Student Performance
Trait
Unsatisfactory
Satisfactory
Good
Excellent
The program works
and produces the
correct results and
displays them
correctly. It also
meets most of the
other
specifications.
The program works
and meets all of the
specifications.
The code is
readable only by
someone who
knows what it is
supposed to be
doing.
The code is fairly
easy to read.
The code is
exceptionally well
organized and very
easy to follow.
The code is not
organized for
reusability.
Some parts of the
code could be
reused in other
programs.
Most of the code
could be reused in
other programs.
The code could be
reused as a whole
or each routine
could be reused.
Documentation
The documentation
is simply
comments
embedded in the
code and does not
help the reader
understand the
code.
The
documentation is
simply comments
embedded in the
code with some
simple header
comments
separating
routines.
The documentation
consists of
embedded
comment and
some simple
header
documentation that
is somewhat useful
in understanding
the code.
The documentation
is well written and
clearly explains
what the code is
accomplishing and
how.
Delivery
The code was
more than 2 weeks
overdue.
The code was
within 2 weeks of
the due date.
The program was
delivered within a
week of the due
date.
The program was
delivered on time.
Efficiency
The code is huge
and appears to be
patched together.
The code is brute
force and
unnecessarily
long.
The code is fairly
efficient without
sacrificing
readability and
understanding.
The code is
extremely efficient
without sacrificing
readability and
understanding.
Specifications
The program is
producing
incorrect results.
Readability
The code is poorly
organized and very
difficult to read.
Reusability
The
program
produces correct
results but does
not display them
correctly.
36 STUDENT EVALUATION RUBRIC
Student Name _________________________Date ______________
Question:
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
________________________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Sl
No.
Components
Marks
Allotted
Sub - Components
Marks
Scored
sub
Total
sub
Total
Marks
Marks
Scored
1
6
Program Write up
Lab Test 1
4
Viva
2
6
Program Write up
Lab Test 2
Practical
Assignment
20
10
Program Execution
4
Viva
3
20
10
Program Execution
7
Implementation
10
3
Viva
50
Total Marks
37 Semester: II
Year: 2014 - 2016
Course Title: Wireless Sensor Networks
Course Code: MCNE 211
Credits L:T:P:SS : 3:0:1
Core/ Elective: Core
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours: 48
Prerequisites: Computer Networks
Course Objectives:
The objective of this course is to make the students
•
To Understand the basic WSN technology and supporting protocols, with emphasis placed on
standardization basic sensor systems and provide a survey of sensor technology
•
Understand the medium access control protocols and address physical layer issues
•
Learn key routing protocols for sensor networks and main design issues
•
Learn transport layer protocols for sensor networks, and design requirements
• Understand the Sensor management ,sensor network middleware, operating systems.
Unit 1
Introduction and Overview of Wireless Sensor Networks : Introduction, Background of Sensor
Network Technology, Applications of Sensor Networks, Basic Overview of the Technology,Basic Sensor
Network Architectural Elements, Brief Historical Survey of Sensor Networks, Challenges and Hurdles, Applications of Wireless Sensor Networks, Basic Wireless Sensor Technology- Introduction, Sensor Node
Technology-Overview,Hardware and Software,Sensor Taxonomy, WN Operating Environment, WN Trends
Unit 2
Wireless Transmission Technology and Systems , Introduction, Radio Technology Primer, Propagation
and Propagation Impairments, Modulation, Available Wireless Technologies, Campus Applications,
MAN/WAN Applications, Medium Access Control Protocols for Wireless Sensor Networks - Introduction,
Background, Fundamentals of MAC Protocols, Performance Requirements, Common Protocols, MAC
Protocols for WSNs, Schedule-Based Protocols, Random Access-Based Protocols, Sensor-MAC Case Study,
IEEE 802.1, LR-WPANs Standard Case Study.
Unit 3
Routing Protocols for Wireless Sensor Networks: Data Dissemination and Gathering, Routing
Challenges and Design Issues in Wireless Sensor Networks, Routing Strategies in Wireless Sensor
Networks, Transport Control Protocols for Wireless Sensor Networks, Traditional Transport Control
Protocols, Transport Protocol Design Issues, Performance of Transport Control Protocols, Middleware for
Wireless Sensor Networks, WSN Middleware Principles, Middleware Architecture, Existing Middleware.
Unit 4
Network Management for Wireless Sensor Networks:
Introduction, Network Management
Requirements, Traditional Network Management Models, Simple Network Management Protocol, Telecom
Operation Map, Network Management Design Issues, Example of Management Architecture: MANNA, Other
Issues Related to Network Management.
Unit 5
Operating Systems for Wireless Sensor Networks: Operating System Design Issues, Examples of
Operating Systems, Performance and Traffic Management - Introduction, Background, WSN Design Issues,
MAC Protocols, Routing Protocols, Transport Protocols, Performance Modeling of WSNs, Performance
Metrics, Basic Models, Network Models, Case Study: Simple Computation of the System Life Span,
Analysis.
38 Laboratory Work: 1. Using simulator like NS-2, TinyOS etc implement the various concepts of WSN
TEXT BOOKS:
1. KAZEM SOHRABY, DANIEL MINOLI, TAIEB ZNATI: WIRELESS SENSOR NETWORKS Technology, Protocols, and
Applications -John Wiley & Sons, 2007.
REFERENCE BOOKS:
1. William C Y Lee: Mobile Communications Engineering Theory and Applications, 2nd Edition, McGraw Hill
Telecommunications 1998.
2. William Stallings: Wireless Communications and Networks, Pearson Education Asia, 2002.
Course Assessment and Evaluation:
Internal
Assessment Tests
CIE
Practicals, Mini
Project and
Certification
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
25
Blue Books
1,2, 3,4 & 5
Once
25
Code
Reposition,
Certificates
2&4
End of Course
(Answering
5 of 10 questions)
100
Answer
scripts
1,2, 3,4 & 5
Questionnaire
1,2, 3,4 & 5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Students
Standard
Examination
SEE
Students
Feedback
Indirect
Assessment
Methods
Direct
Assessment
Methods
When/ Where
(Frequency in
the course)
Thrice(Average of
the best two will
be computed)
To
Whom
What
Students
End of the course
-
End of Course
Survey
Course Outcomes:
1. Have knowledge and understanding of basic WSN technology and supporting protocols and
Technology
2. Have knowledge and to Identify medium access control protocols and address physical layer
issues
3. Have knowledge routing protocols for sensor networks and main design issues
4. Have knowledge of transport layer protocols for sensor networks, and design requirements
5. Understand Sensor management, sensor network middleware, operating systems
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Program Outcomes
PO
(a)
Have
knowledge
and
understanding of basic WSN
technology
and
supporting
protocols and Technology
x
Have knowledge and to Identify
x
PO
(b)
PO
(c)
PO
(d)
PO
(e)
x
x
x
PO
(f)
PO
(g)
PO
(h)
PO
(i)
PO
(j)
PO
(k)
PO
(l)
x
x
x
x
x
x
x
x
x
x
x
x
medium access control protocols
and
address
physical
layer
issues
Have
knowledge
routing
x
x
x
protocols for sensor networks
and main design issues
Have
knowledge
layer
protocols
networks,
of
and
for
transport
x
x
x
sensor
design
requirements
Understand
Sensor
management, sensor network
middleware, operating systems
x
x
x
39 x
x
x
x
x
x
PO
(m)
Semester: II
Year: 2014 - 2016
Course Title: Computer Security
Course Code: MCNE 212
Credits L:T:P:SS : 3:1:0
Core/ Elective: Core
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours: 48
Prerequisites: Knowledge of Computer Networks.
Course Objectives:
•
•
•
•
•
Provide deeper understanding of security goals , type of possible attacks and how
security mechanisms provide services and meet the goals at various levels
Present Private Key Cryptosystems DES, AES structure.
Identify the need of cryptographic hash function and Digital Signature and Public Key
Cryptosystems
Identify the need of Key Management and Identification Management
Identify the need for application level security, transport layer, and network layer
Course Contents:
Unit 1
Introduction: Security Goals, Cryptographic Attacks, Services and Mechanism, Techniques.
Mathematics of Cryptography: Integer Arithmetic, Modular Arithmetic, Matrices, Linear Congruence.
Unit 2
Private Key Cryptosystems: Classical Ciphers, DES Family, Modern Private-Key Cryptographic
Algorithms( FEAL), IDEA, RC6
Advanced Encryption Standard: Introduction, Transformations, Key Expansion, Examples, Analysis of
AES.
Unit 3
Public Key Cryptosystems: Concept of public key cryptosystem, RSA Cryptosystem.Hashing: Properties
of Hashing, Birthday Paradox, MD Family .Digital Signature: Properties of Digital Signature, Generic
Signature Scheme, RSA Signature.
Unit 4
Identification: Basic Identification, User Identification, Passwords, Challenge-Response Identification.Key
Management: Symmetric-Key Distribution, Kerberos, Symmetric-Key Agreement, Public-Key Distribution,
Hijacking.
Unit 5
Security at the Application Layer: PGP and S/MIME,Email, PGP, S/MIME. Internet Protocol
Security(IPsec): Security Associations, Authentication Header Protocol, Encapsulating Security Payload
protocol, Internet Key Exchange, Virtual Private Network.
Secure Sockets Layer: States of SSL, SSL Record Protocol, Handshake Protocol, Change Cipher Spec and
Alert Protocols, Transport-Layer Security.
Text Book:
1. Josef Pieprzyk, Thomas Hardjono, Jennifer Serberry Fundamentals of Computer Security,
Springer.
2. Behrouz A. Forouzan, Debdeep Mukhopadhyay: Cryptography and Network Security, 2nd
Edition, Special
Indian Edition, Tata McGraw-Hill, 2011.
Reference Books:
1. Michael E. Whitman and Herbert J. Mattord: Principles of Information Security, 2nd
Edition, Thomson, Cengage Delmar Learning India Pvt., 2012.
40 2. William Stallings: Network Security Essentials: Applications and Standards, 4th Edition,
Pearson Education, 2012.
Tutorial:
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
Implementing Elliptic Curve Cryptography (ECC) base authentication and authorization in a network.
Implement a Program to Measure the Amount of Randomness Available in a System
Security of Wireless Networks and Mobile Devices
Proactive Cryptography Applications in Smart Cards
Digital Signatures for Physical Mail
Intrusion Detection Systems
Secure Instant Messengers
Cookie authentication
Steganography in TCP timestamps
Variable Size Block Encryption and Generate the Key of Variable Length
Security of Network Attached Storage
Steganography in Spam
A Secure Media Distribution Framework
Course Delivery: The course will be delivered through lectures, class room interaction, group discussion
and exercises and self-study cases.
Course Assessment and evaluation:
Direct Assessment
Methods
What
CIE
Internal
Assessment Tests
Project
Indirect
Assessment
Methods
SEE
To
Whom
Students
Standard Examination
Students
Feedback
Students
When/ Where
(Frequency in
the course)
Max
Marks
Evidence
Collected
Contribution to
Course
Outcomes
Thrice(Average of
the best two will
be computed)
30
Blue Books
1,2,3,4 &5
Once
20
Project and
Report
2,3 & 4
End of Course
(Answering
5 of 10
questions)
100
Answer
scripts
1,2,3,4 &5
Questionnaire
4, 5 &
Effectiveness of
Delivery of
instructions &
Assessment
Methods
End of the course
-
End of Course
Survey
Course Outcomes:At the end of the course students should be able to:
1.
2.
3.
4.
5.
Understand the security goals and the threats to security
Understand Private Key Cryptosystems and Identify and formulate the type of encryption method
DES or AES depending on the need and security threat perception
Demonstrate the implementation of hash function and Digital Signatures and its utility
Describe the fundamentals of Key Management and Identity Management
Understand different ways in which security goal is achieved at application layer, transport layer
and network layer.
41 Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Program Outcomes
PO
(a)
Understand the security goals and
the threats to security
Understand
Private
Key
Cryptosystems and Identify and
formulate the type of encryption
method DES or AES depending on
the need and security threat
perception
Demonstrate the implementation
of hash function and Digital
Describe the fundamentals of Key
Management
and
Identity
Management
x
Understand different ways in which
security goal is achieved at
application layer, transport layer
and network layer.
x
PO
(b)
PO
(c)
PO
(d)
PO
(e)
x
x
x
x
x
PO
(g)
PO
(h)
PO
(i)
PO
(j)
PO
(k)
PO
(l)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
42 PO
(f)
x
x
x
x
x
PO
(m)
Semester: II
Year: 2014 - 2016
Course Title: PROTOCOLS ENGINEERING
Course Code: MCNE 213
Credits L:T:P:SS : 3:1:0
Core/ Elective: Core
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours: 48
Prerequisites: Computer Networks
Objective:
•
•
•
•
•
Understand the TCP/IP suite protocol structure
Identify Protocol specification
Distinguish the different Protocol Specification Language like SDL, SPIN, Estelle, E-Lotos,
CPN, UML, etc
Understand Protocol Verification / Validation techniques like semantic models and
reachability analysis
Understand Generating Conformance test suite and its application to a running protocol
implementation.
Unit 1
Introduction: Communication model, Communication Software, Communication Subsystems,
Communication Protocol Definition/Representation, Formal and Informal Protocol Development Methods,
Protocol Engineering Phases . Error Control, Flow Control: Type of Transmission Errors, Linear Block
Code, Cyclic Redundancy Checks, Introduction to Flow Control, Window Protocols, Sequence Numbers,
Negative Acknowledgments, Congestion Avoidance.
Unit 2
Network Reference Model: Layered Architecture, Network Services and Interfaces, Protocol Functions:
Encapsulation, Segmentation, Reassembly, Multiplexing, Addressing, OSI Model Layer Functions, TCP/IP
Protocol Suite, Application Protocols.4. Protocol Specification: Components of specification, Service
specification, Communication Service Specification Protocol entity specification: Sender, Receiver and
Channel specification, Interface specifications, Interactions, Multimedia specifications, Alternating Bit
Protocol Specification, RSVP specification.
Unit 3
Protocol Specification Language (SDL): Salient Features. Communication System Description using
SDL, Structure of SDL. Data types and communication paths, Examples of SDL based Protocol
Specifications: Question and answer protocol, X-on-X-off protocol, Alternating bit protocol, Sliding window
protocol specification, TCP protocol specification, SDL based platform for network, OSPF, BGP Multi Protocol
Label Switching SDL components. Protocol Verification / Validation: Protocol Verification using FSM,
ABP Verification, Protocol Design Errors, Deadlocks, Unspecified Reception, Non-executable Interactions,
State Ambiguities, Protocol Validation Approaches: Perturbation Technique, Reachability Analysis, Fair
Reachability Graphs, Process Algebra based Validation, SDL Based Protocol Verification: ABP Verification,
Liveness Properties, SDL Based Protocol Validation: ABP Validation.
Unit 4
Protocol Conformance and Performance Testing: Conformance Testing Methodology and Framework,
Local and Distributed Conformance Test Architectures, Test Sequence Generation Methods: T, U, D and W
methods, Distributed Architecture by Local Methods, Synchronizable Test Sequence, Conformance testing
with Tree and Tabular Combined Notation (TTCN), Conformance Testing of RIP, Testing Multimedia
Systems, quality of service test architecture(QOS), Performance Test methods, SDL Based
Performance Testing of TCP, OSPF, Interoperability testing, Scalability testing protocol synthesis
problem
Unit 5
Protocol Synthesis and Implementation: Synthesis methods, Interactive Synthesis Algorithm,
Automatic Synthesis Algorithm, Automatic Synthesis of SDL from MSC, Protocol Re-synthesis,
Requirements of Protocol Implementation, Objects Based Approach To Protocol Implementation, Protocol
Compilers, Code generation from Estelle, LOTOS, SDL and CVOPS.
43 TEXT BOOKS:
1. Pallapa Venkataram and Sunilkumar S. Manvi: Communication Protocol Engineering, PHI,
2004.
REFERENCE BOOKS:
1. Mohammed G. Gouda: Elements of Protocol Design, Wiley Student Edition, 2004.
Laboratory:
The student learns the basics of protocol engineering and how to implement typical Internet protocols with
various kinds of protocol implementation frameworks
Protocol implementation and validation in the laboratory
.1: Slotted-Aloha protocol medium access,
Understand the theoretical basis and performance of the Slotted Aloha protocol.
2: Distributed Queue Dual Bus (DQDB) protocol medium access
Understand the theoretical basis and performance of the DQDB protocol.
Course Assessment and Evaluation Scheme:
Indirect
Assessment
Methods
Direct Assessment
Methods
What
CIE
Internal
Assessment
Tests
Quiz/
Case study
SEE
To
Whom
Students
Standard
Examination
End of Course
Survey
Students
When/
Where
(Frequency in
the course)
Thrice(Average
of the best two
will be
computed)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1,2,3,4 &5
Once
20
Quiz Answers/
Reports
1-5
End of Course
(Answering
5 of 10
questions)
100
Answer scripts
1,2,3,4 &5
Questionnaire
1, 2, 3, 4, 5 & 6
Effectiveness of
Delivery of
instructions &
Assessment Methods
End of the
course
-
Course Outcome:
1. Students learn the TCP/IP suite protocol structure
2. Know how to create protocols with various kinds of Protocol specification
3. Learn to use the Specification and Description Languages of different Protocol
Specification Language like SDL, SPIN, Estelle, E-Lotos, CPN, UML, etc
4. Know how to apply acquired concepts of: protocol specification, modeling, compliance
test, synthesize and validation.
5. Learn to Generate Conformance test suite and its application to a running protocol
implementation.
44 Semester: II
Year: 2014 - 2016
Course Title: Computer Networks Laboratory
Course Code: MCNE 214
Credits L:T:P:SS : 0:0:2
Core/ Elective: Core
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours: 12
Prerequisites: Computer Networks
Laboratory Work:
Course Objectives:
1.
Understand the concepts of network, transport and application layer by practical
implementation.
2.
Implement various concepts related to wireless sensor networks.
3.
Analyze the Performance of handoffs and routing functions in wireless networks.
4.
Simulate wired and wireless networks using Qualnet.
5.
Write programs for wireless sensor networks using a modular hardware and software
platform - iSense
Simulate the following using Qualnet.
1.
Connection Oriented and Connectionless Transport layer protocols
2.
Wired Networks with different connecting devices and wireless networks with different
antenna models.
3.
Bellman Ford Routing Protocol, routers with queruing mechanisms.
4.
AODV routing protocol for adhoc networks
5.
Wired and Wireless VOIP application
6.
Wi-Fi Power Saving mode, Infrastructure mode, Adhoc mode and mixed mode networks
7.
Handoffs in WIMAX 802.16d, WIMAX802.16e networks.
8.
Exchange date between wired, Wi-Fi and Wimax network.
9.
ZigBee Battery Model and ZigBee Energy Model.
Write programs for the following using iSense.
1.
Power saving mechanism by switching the module between ACTIVE and SLEEP state.
2.
Setup wireless adhoc network and perform range tests for LOS and NLOS
3.
Broadcast messages with and without Acknowledgement among each other.
4.
Display the temperature and luminance in iShell.
5.
Route the sensed temperature and luminance value to a sink node using bi-directional
quality routing and store the sensed value to the SD card present in the sink node.
6.
Wirelessly change the running application on a sensor mote to a different application and
display the changes in iShell.
7.
Assign NET10 module an IPv4 address and IPv6 adress and perform a ping operation to
the PC.
8.
Create a router – host application using IPv6 and communicate between the sensor
devices via IP stack .
Course Outcomes:
1.
Analyze network performance of different connecting devices, routing protocols, and
different types of services provided at datalink, network and transport layer respectively.
45 2. Implement wired, wireless and mixed mode networks and run VOIP applications on
them.
3. Differentiate between handoffs in wimax networks and analyze ZigBee battery and energy
model.
4. Setup wireless adhoc networks, sense different parameters and route the information to sink
nodes.
5. Implement wireless sensor networks using both IPv4 and IPv6 addresses.
Indirect
Assessment
Methods
Direct Assessment
Methods
What
C
I
E
S
E
E
To
Whom
When/ Where
(Frequency in the
course)
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
Twice
40
Answer
Sheets
1,2 ,3,4 & 5
Once
10
Data sheets
1,2,3,4 &5
End of Course
100
Answer
scripts
1,2,3,4&5
Questionnaire
1,2,3,4&5
Effectiveness of
Delivery of
instructions &
Assessment
Methods
Lab Tests
Practical
assignments
Students
Standard
Examination
End of Course
Survey
Students
End of the course
-
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Program Outcomes
Analyze network performance
of different connecting devices,
routing protocols, and different
types of services provided at
datalink, network and transport
layer respectively.
Implement wired, wireless and
mixed mode networks and run
VOIP applications on them.
Differentiate between handoffs
in wimax networks and analyze
ZigBee battery and energy
model.
Setup wireless adhoc networks,
sense different parameters and
route the information to sink
nodes.
Implement
wireless
sensor
networks using both IPv4 and
IPv6 addresses.
P
O
(
a
)
P
O
(b
)
P
O
(c
)
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
P
O
(d
)
46 P
O
(e
)
PO
(f)
PO
(g
)
PO
(h
)
PO
(i)
PO
(j)
PO
(k)
P
O
(l)
PO
(m
)
Rubrics for Assessment of Student Performance
Trait
Unsatisfactory
Satisfactory
Good
Excellent
The program works
and produces the
correct results and
displays them
correctly. It also
meets most of the
other
specifications.
The program works
and meets all of the
specifications.
The code is
readable only by
someone who
knows what it is
supposed to be
doing.
The code is fairly
easy to read.
The code is
exceptionally well
organized and very
easy to follow.
The code is not
organized for
reusability.
Some parts of the
code could be
reused in other
programs.
Most of the code
could be reused in
other programs.
The code could be
reused as a whole
or each routine
could be reused.
Documentation
The documentation
is simply
comments
embedded in the
code and does not
help the reader
understand the
code.
The
documentation is
simply comments
embedded in the
code with some
simple header
comments
separating
routines.
The documentation
consists of
embedded
comment and
some simple
header
documentation that
is somewhat useful
in understanding
the code.
The documentation
is well written and
clearly explains
what the code is
accomplishing and
how.
Delivery
The code was
more than 2 weeks
overdue.
The code was
within 2 weeks of
the due date.
The program was
delivered within a
week of the due
date.
The program was
delivered on time.
Efficiency
The code is huge
and appears to be
patched together.
The code is brute
force and
unnecessarily
long.
The code is fairly
efficient without
sacrificing
readability and
understanding.
The code is
extremely efficient
without sacrificing
readability and
understanding.
Specifications
The program is
producing
incorrect results.
Readability
The code is poorly
organized and very
difficult to read.
Reusability
The
program
produces correct
results but does
not display them
correctly.
47 STUDENT EVALUATION RUBRIC
Student Name _________________________Date ______________
Question:
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
________________________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Sl
No.
Components
Marks
Allotted
Sub - Components
Marks
Scored
sub
Total
sub
Total
Marks
Marks
Scored
1
6
Program Write up
Lab Test 1
4
Viva
2
6
Program Write up
Lab Test 2
Practical
Assignment
20
10
Program Execution
4
Viva
3
20
10
Program Execution
7
Implementation
10
3
Viva
50
Total Marks
48 Year: 2014-16
Semester: II
Course Title:
Topics on Digital Communication
lab
Course Code: MCNE 215
Credits (L:T:P) : 0:0:2
Core
Type of Course: Practical Sessions
Total Contact Hours: 12
Prerequisites:
The student should have undergone the course on Analog and Digital circuits
Course Objectives :
Objectives of this course is to :
•
•
•
•
•
Study various aspects of digital communication through use of Matlab
Fourier transform and inverse fourier transform of image.
Edge detection of image and bluring and deblurring of image
Sampling of the image
Frequency modulation and demodulation of a signal
Course Contents:
List of Experiments
Histogram equalization of an image
1. Fourier Transform and its inverse Fourier Transform of an image
2. Blurring and Deblurring of an image
3. Dilation and Erosion of an image
4. Edge detection of an image ( Sobel , canny edge detector )
5. Sampling of an image
6. Frequency Modulation and demodulation of a signal
Text Books:
1. Alberto Leon – Garcia and Indra Widjaja: Communication Networks - Fundamental Concepts and Key architectures,
2nd Edition, Tata McGrawHill, 2006.
2. Simon Haykin: Digital Communication, Wiley India, 2007.
Reference Books:
1. John G Proakis: Digital Communications, 3rd Edition, McGraw Hill, 2008.
2. Leon W Couch: Analog / Digital Communication, 5th Edition, PHI, 2008.
Mapping Course Outcomes with Program Outcomes:
Practical sessions are conducted through Matlab image processing tool box
Course Outcomes
Program Outcomes
PO
(a)
PO
(b)
PO
(c)
x
x
PO
(d)
PO
(e)
PO
(f)
PO
(g)
PO
(h)
x
x
PO(i)
PO
(j)
PO
(k)
PO
(l)
x
Understand various aspects of digital
communication through use of Matlab
Should be able to Fourier transform and
inverse fourier transform of image
x
Implement Edge detection of image and
bluring and deblurring of image
x
Must be able conduct Sampling of an
image
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
Frequency modulation and demodulation
of a signal
49 x
x
x
x
x
x
x
x
PO
(m)
Course Outcomes:
At the end of the course students should be able to:
1.
2.
3.
4.
Understand various aspects of digital communication through use of Matlab
Should be able to Fourier transform and inverse fourier transform of image.
Implement Edge detection of image and bluring and deblurring of image
Must be able conduct Sampling of an image
5. Frequency modulation and demodulation of a signal
Course Assessment and evaluation:
Indirect
Assessment
Methods
Direct
Assessment
Methods
What
CIE
Internal
Assessment Tests
Class-room Surprise
Quiz
SEE
To
Whom
Students
Standard Examination
When/
Where
(Frequency
in the
course)
One internal
test be
computed
Quiz/online
course
External lab
exam
Evidence
Collected
Contribution
to Course
Outcomes
30
Observation
sheets
1,2 3,4,5
20
50
Answer
scripts
Answer
scripts
Students
Feedback
End of Course
Survey
Students
End of the
course
50 Max
Marks
-
Questionnaire
1,2,3,4,5
1,2,3,4 &5
1, 2 & 3,
Effectiveness
of Delivery of
instructions &
Assessment
Methods
Rubrics for Assessment of Student Performance
Trait
Unsatisfactory
Satisfactory
Good
Excellent
The program works
and produces the
correct results and
displays them
correctly. It also
meets most of the
other
specifications.
The program works
and meets all of the
specifications.
The code is
readable only by
someone who
knows what it is
supposed to be
doing.
The code is fairly
easy to read.
The code is
exceptionally well
organized and very
easy to follow.
The code is not
organized for
reusability.
Some parts of the
code could be
reused in other
programs.
Most of the code
could be reused in
other programs.
The code could be
reused as a whole
or each routine
could be reused.
Documentation
The documentation
is simply
comments
embedded in the
code and does not
help the reader
understand the
code.
The
documentation is
simply comments
embedded in the
code with some
simple header
comments
separating
routines.
The documentation
consists of
embedded
comment and
some simple
header
documentation that
is somewhat useful
in understanding
the code.
The documentation
is well written and
clearly explains
what the code is
accomplishing and
how.
Delivery
The code was
more than 2 weeks
overdue.
The code was
within 2 weeks of
the due date.
The program was
delivered within a
week of the due
date.
The program was
delivered on time.
Efficiency
The code is huge
and appears to be
patched together.
The code is brute
force and
unnecessarily
long.
The code is fairly
efficient without
sacrificing
readability and
understanding.
The code is
extremely efficient
without sacrificing
readability and
understanding.
Specifications
The program is
producing
incorrect results.
Readability
The code is poorly
organized and very
difficult to read.
Reusability
The
program
produces correct
results but does
not display them
correctly.
51 STUDENT EVALUATION RUBRIC
Student Name _________________________Date ______________
Question:
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
________________________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Sl
No.
Components
Marks
Allotted
Sub - Components
Marks
Scored
sub
Total
sub
Total
Marks
Marks
Scored
1
6
Program Write up
Lab Test 1
4
Viva
2
6
Program Write up
Lab Test 2
Practical
Assignment
20
10
Program Execution
4
Viva
3
20
10
Program Execution
7
Implementation
10
3
Viva
50
Total Marks
52 Semester: III
Year: 2014 - 2016
Course Title: Cloud Computing Laboratory
Credits (L:T:P) : 0:0:1
Type of course: Practical
Course Code: MCNE
Core/ Elective: Core
Total Contact Hours: 28
Prerequisites: Basics of Java
Course Objectives:
The objective of this course is to make the students to
•
•
•
•
•
Recognize Cloud Resource Virtualization, Layering, Para virtualization and Optimization
Understand Cloud Computing: Applications & Paradigms.
Recognize Cloud Resource Virtualization, Layering, Para virtualization and Optimization
Design Cloud Resource Management and Scheduling.
Learn and Classify Cloud storage systems, security issues and develop cloud application..
Course Contents:
Experiments that are to be conducted as a part of the course:
1.
Design Virtual Machine using VM player and test Client server application using Virtual
Machine
2.
Design Virtual Machine using VM player and test Client server application using Virtual Box
3.
Compare the pros and cons of VM player and Virtual Box
4.
Paas – Deploy Applications to google App Engine - simple web applications
5.
Paas – Deploy Applications to google App Engine - web applications with database
6.
Deploy Applications to cloud foundry using VMC
7.
Lab Test - I
8.
Deploy Applications to cloud foundry using Micro cloud foundry
9.
Deploy Applications to cloud foundry using Eclipse
10. To Set up a Hadoop Cluster – Single Node
11. To Set up a Hadoop Cluster – Multi Node
12. Execute Map Reduce Programs in Hadoop Cluster
13. Study of Future Grid
14. Demo of Mini Project
Course Contents:
The student will execute a mini project using cloud tools , write a report and demonstrate to the examiner.
Prescribed Text Book
T1. Cloud Computing Theory and Practice Dan.C Marinescu MK Morgan Kaufhann Elsevier.
T2. Cloud Computing a Hands-on Approach by Arshdeep Bagha, Vijay Madisetti Universities Press.
Reference Book (S)
53 R1. Cloud Computing : Web-Based Applications That Change the Way You Work and Collaborate Online (English) 1st
Edition Miller Pearson India
R2. Enterprise Cloud Computing: Technology, Architecture, Applications Dr Gautam Shroff Kindle Edition
Course Assessment Methods:
Indirect
Assessment
Methods
Direct
Assessme
nt
Methods
What
CI
E
S
E
E
Mini
Project
Standard
Examinati
on
Students
Feedback
End of Course
Survey
To
Whom
When/ Where
(Frequency in the
course)
Max
Mark
s
Evidence
Collected
Contribution
to Course
Outcomes
Once
50
Project
Documentation
1,2,3,4 &5
Project demo
50
Answer scripts
1,2,3,4&5
Questionnaire
1,2,3,4&5
Effectiveness
of Delivery of
instructions &
Assessment
Methods
Student
s
Student
s
End of the course
-
Course Outcomes:
1. Ability to programme using the APIs of Cloud Computing
2. Ability to create Virtual Machine images and to deploy them on a Cloud.
3. Compose services in a distributed computing environment to achieve tasks relevant to a
knowledge-based business or public service.
4. Ability to deploy applications using the Unicore Grid middleware
5. Identify problems, and explain, analyze, and evaluate various cloud computing solutions.
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Program Outcomes
PO
(a)
Ability to programme using the
APIs of Cloud Computing
X
Ability to create Virtual Machine
images and to deploy them on a
Cloud
Compose services in a
distributed computing
environment to achieve tasks
relevant to a knowledgebased business or public
service.
X
Ability to deploy applications
using the Unicore Grid
middleware
X
Identify problems, and
explain, analyze, and
evaluate various cloud
computing solutions.
X
PO
(b)
PO
(c)
X
PO
(d)
PO
(f)
PO
(g)
PO
(h)
X
PO
(i)
X
X
X
X
X
PO
(j)
X
X
54 PO
(e)
X
PO
(l)
PO
(m
)
X
X
X
X
X
X
X
PO
(k)
X
X
X
X
X
Rubrics for Assessment of Student Performance
Trait
Unsatisfactory
Satisfactory
Good
Excellent
The program works
and produces the
correct results and
displays them
correctly. It also
meets most of the
other
specifications.
The program works
and meets all of the
specifications.
The code is
readable only by
someone who
knows what it is
supposed to be
doing.
The code is fairly
easy to read.
The code is
exceptionally well
organized and very
easy to follow.
The code is not
organized for
reusability.
Some parts of the
code could be
reused in other
programs.
Most of the code
could be reused in
other programs.
The code could be
reused as a whole
or each routine
could be reused.
Documentation
The documentation
is simply
comments
embedded in the
code and does not
help the reader
understand the
code.
The
documentation is
simply comments
embedded in the
code with some
simple header
comments
separating
routines.
The documentation
consists of
embedded
comment and
some simple
header
documentation that
is somewhat useful
in understanding
the code.
The documentation
is well written and
clearly explains
what the code is
accomplishing and
how.
Delivery
The code was
more than 2 weeks
overdue.
The code was
within 2 weeks of
the due date.
The program was
delivered within a
week of the due
date.
The program was
delivered on time.
Efficiency
The code is huge
and appears to be
patched together.
The code is brute
force and
unnecessarily
long.
The code is fairly
efficient without
sacrificing
readability and
understanding.
The code is
extremely efficient
without sacrificing
readability and
understanding.
Specifications
The program is
producing
incorrect results.
Readability
The code is poorly
organized and very
difficult to read.
Reusability
The
program
produces correct
results but does
not display them
correctly.
55 STUDENT EVALUATION RUBRIC
Student Name _________________________Date ______________
Question:
____________________________________________________________________________
____________________________________________________________________________
____________________________________________________________________________
________________________________________________________________
Name of Supervisors: 1._______________________________
2._______________________________
Sl
No.
Components
Marks
Allotted
Sub - Components
Marks
Scored
sub
Total
sub
Total
Marks
Marks
Scored
1
6
Program Write up
Lab Test 1
4
Viva
2
6
Program Write up
Lab Test 2
Practical
Assignment
20
10
Program Execution
4
Viva
3
20
10
Program Execution
7
Implementation
10
3
Viva
50
Total Marks
56 Semester I
Course Title: Computer Systems Performance
Analysis
Credits (L:T:P:SS): 3:0:0
Type of Course: Lectures
Year 2014-16
Course Code: MCNE E11
Core/Elective: Elective
Total Contact Hours: 36
Prerequisites:
Students should have undergone a course on probability theory, matrices, software engineering aspects and queuing
theory
Course Objectives:
The objectives of this course are:
•
•
•
•
•
•
•
•
Learn techniques to approach performance problems
Compare two systems and determine the optimal value of a parameter
Identify performance bottlenecks and characterize the load on a system
Select the number and size of system components and predict the
Performance of future workloads
Understand the use of different analysis strategies like measurement, simulation,
Analytical modeling
Learn mathematical techniques for performance analysis.
Course Contents:
Unit 1
Introduction: The art of Performance Evaluation; Common Mistakes in Performance Evaluation,
A Systematic Approach to Performance Evaluation, Selecting an Evaluation Technique, Selecting
Performance Metrics, Commonly used Performance Metrics, Utility Classification of Performance
Metrics, Setting Performance Requirements.
Unit 2
Workloads, Workload Selection and Characterization: Types of Workloads, addition
instructions, Instruction mixes, Kernels; Synthetic programs, Application benchmarks, Popular
benchmarks. Work load Selection: Services exercised, level of detail; Representativeness;
Timeliness, Other considerations in workload selection. Work load characterization Techniques:
Terminology; Averaging, Specifying dispersion, Single Parameter Histograms, Multi Parameter
Histograms, Principle Component Analysis, Markov Models, Clustering.
Unit 3
Monitors, Program Execution Monitors and Accounting Logs: Monitors: Terminology and
classification; Software and hardware monitors, Software versus hardware monitors, Firmware
and hybrid monitors, Distributed System Monitors, Program Execution Monitors and Accounting
Logs, Program Execution Monitors, Techniques for Improving Program Performance, Accounting
Logs, Analysis and Interpretation of Accounting log data, Using accounting logs to answer
commonly asked questions.
Unit 4
Capacity Planning and Benchmarking: Steps in capacity planning and management;
Problems in Capacity Planning; Common Mistakes in Benchmarking; Benchmarking Games; Load
Drivers; Remote-Terminal Emulation; Components of an RTE; Limitations of RTEs.
Experimental Design and and Analysis: Introduction: Terminology, Common mistakes in
experiments, Types of experimental designs, 2k Factorial Designs, Concepts, Computation of
effects, Sign table method for computing effects; Allocation of variance; General 2k Factorial
Designs, General full factorial designs with k factors: Model, Analysis of a General Design,
Informal Methods.
Unit 5
57 Queuing Models: Introduction: Queuing Notation; Rules for all Queues; Little’s Law, Types of
Stochastic Process. Analysis of Single Queue: Birth-Death Processes; M/M/1 Queue; M/M/m
Queue; M/M/m/B Queue with finite buffers; Results for other M/M/1 Queuing Systems. Queuing
Networks: Open and Closed Queuing Networks; Product form networks, queuing Network
models of Computer Systems. Operational Laws: Utilization Law; Forced Flow Law; Little’s Law;
General Response Time Law; Interactive Response Time Law; Bottleneck Analysis; Mean Value
Analysis and Related Techniques; Analysis of Open Queuing Networks; Mean Value Analysis;
Approximate MVA; Balanced Job Bounds; Convolution Algorithm, Distribution of Jobs in a
System, Convolution Algorithm for Computing G(N), Computing Performance using G(N),
Timesharing Systems, Hierarchical Decomposition of Large Queuing Networks: Load Dependent
Service Centres, Hierarchichal Decomposition, Limitations of Queuing Theory.
Text Book:
1. Raj Jain: The Art of Computer Systems Performance Analysis, 1st edition, John Wiley and
Sons, 2012.
Reference Books:
1. Paul J Fortier, howard E Michel: computer Systems Performance Evaluation and
prediction, 1st edition, Elsevier, 2009.
2. Trivedi K S: Probability and Statistics with Reliability, Queuing and Computer Network
Applications, 1st edition, PHI, 2011.
Course Delivery: The course will be delivered through lectures, presentations, classroom
discussions, and case studies. Questions for CIE and SEE are designed in accordance with the
Bloom’s taxonomy
Indirect
Assessme
nt
Methods
Direct
Assessment Methods
What
To Whom
Internal
Assessment
Tests
C
I
E
S
E
E
Quiz and
Home Work
Students
When/Where
(Frequency in
the course)
Max
Mark
Contribution
to course
outcomes
Thrice (Average of
the best two will be
computed)
25
Blue Books
1,2,3,4
2 quizzes and 2
home work
assignments
25
Quiz and
Home Work
Marks
1,2,3,4
Answer scripts
1,2,3,4
Questionnaire
1,2,3,4,Releva
nce of the
course
Semester
End
Examination
End of Course
(Answering 5 of 10
questions)
100
Students
Feedback
End of the course
-
End of
Course
Survey
Evidence
Collected
Students
Course Outcomes:
1. Develop an understanding of the issues of reliability and its evaluation in the design of
computer systems, and to emphasize their importance.
2. Examine the concepts and techniques for redundant designs which can make a system
fault tolerant, i.e. still functioning correctly in the presence of failures, in hardware,
software and communications.
3. Discuss techniques for developing distributed reliable hardware and software systems.
4. Examine testing techniques and algorithms in hardware, software and communications.
58 Mapping Course Outcomes with program Outcomes:
Course Outcomes
Develop an understanding of the
issues of reliability and its
evaluation in the design of
computer
systems,
and
to
emphasize their importance
Examine
the
concepts
and
techniques for redundant designs
which can make a system fault
tolerant, i.e. still functioning
correctly in the
presence of
failures, in hardware, software
and communications
Discuss techniques for developing
distributed reliable hardware and
software systems.
Examine testing techniques and
algorithms in hardware, software
and communications.
Program Outcomes
PO
(a)
PO
(b)
X
X
X
X
X
X
PO
(c)
PO
(d)
X
X
X
PO
(e)
PO
(f)
PO
(g)
PO
(h)
PO
(i)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Elective
X
PO
(j)
PO
(k)
X
PO
(l)
PO
(m
)
X
X
X
X
X
X
X
X
X
X
Year: 2014-2016
Course Title: Big Data and Data Science
Course Code: MSCE12
Credits (L:T:P) : 3:0:1
Core/ Elective: Elective
Type of Course: Lecture, Practical
Total Contact Hours: 56
Prerequisites: Nil
Course Objectives
The objectives of this course are to
• Understand how organizations these days use their data a decision supporting tool and to
build data - intensive products and services.
• Understand the collection of skills required by organizations to support these functions
has been grouped under the term “Data Sciences”
• Understand the basic concepts of big data, methodologies for analyzing structured and
unstructured data
• Understand the relationship between the Data Scientist and the business needs
Unit 1
Introduction: Data Processing Architectures, components and processes; Data Stores and Data kind,
Challenges " Big Data" and otherwise Special Considerations in Big Data Analysis: Background, Theory
in Search of Data, Data in Search of Theory, Overfitting, Bigness Bias, Too Much Data, Fixing Data, Data
Subsets in Big Data: Neither Additive nor Transitive, Additional Big Data Pitfalls. Providing Structure to
Unstructured Data: Background, Machine Tanslation, Autocoding, Indexing and Term Extraction
59 Unit 2
Component Parts of Data Science: Data Types, Classes of Analytic Techniques, Learning Models,
Execution Models; Fractal Analytic Model, Analytic Selection Process: Implementation Constraints
Feature Engineering: Feature Selection, Data Veracity, Application of Domain Knowledge, Curse of
Dimensionality
Unit 3
Simple Analytic Techniques: Background, Look at the Data, Data Range, Denominator, Frequency
Distributions, Mean and Standard Deviation, EstimationOnly Analyses Deep Dive into Analysis:
Background, Analytic Tasks, Clustering, Cassifying, Recommending, and Modelling, Data Reduction,
Normalising and Adjusting Data, Find RelationshipsNot Similarities
Unit 4
Applying Nuances of Data Science to Text Processing And Information Retrieval *Assignment Driven; Will
involve building a tool using many principles learnt in the previous units; Study Material for SEE will be
provided during the class room sessions*
Unit 5
Big nature of Data Case study MapReduce, The Paper: Programming model, Types and Examples;
Implementation and Execution Architecture; Partitioning, types, Combiners, Data Locality Hadoop:
Challenges at Large Scale and the Hadoop Approach; HDFS; MapReduce in Hadoop
Reading Material: (In no particular order of precedence)
1. Principles of Big Data: Preparing, Sharing and Analyzing Complex Information, Jules J
Berman, First Edition, MK Publishers, 2013.
2. The Field Guide to Data
Science:http://www.boozallen.com/media/file/TheFieldGuidetoDataScience.pdf
3. Understanding Big Data:
ftp://129.35.224.12/software/tw/Defining_Big_Data_through_3V_v.pdf
4. Ghemawat et.al Google, MapReduce: Simplied Data Processing on Large Clusters
http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce
osdi04.pdf
5. Hadoop Tutorial: http://developer.yahoo.com/hadoop/tutorial/
Course Delivery
The course will be delivered through lectures, presentations, classroom discussions, practice
exercises and practical sessions.
60 Course Assessment and evaluation:
Indirec
t
Assess
ment
Method
s
Direct
Assessment
Methods
What
CIE
SEE
Internal
Assessment
Tests
Project and
lab Exercise
evaluation
To
Whom
Students
Semester End
Examination
Students
Feedback
End of Course
Survey
Students
When/ Where
(Frequency in
the course)
Thrice (Average
of the best two
will be computed)
Summation of lab
and project
demonstration
End of Course
(Answering
5 of 10 questions)
End of the course
Max
Marks
Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1-5
20
Soft copies
of the work
1-5
100
Answer
scripts
1-5
-
Questionnair
e
3-5, Relevance
of the course
Course Outcomes
At the end of the course students should be able to:
1. Identify the differences between Big Data and Small Data
2. Design the programs to analyze big data
3. Demonstrate the analysis of big data
4. Analyze the Ontologies, Semantics, Introspection, Data Integration and Measurement
techniques of big data
5. Illustrate the stepwise approach to big data analysis and understand the legalities and
societal issues involved
Mapping Course Outcomes with Programme Outcomes:
Programme Outcomes
Course Outcomes
PO
(a)
Identify
the
differences
between Big Data and Small
Data
Design the programs to analyze
big data
Demonstrate the analysis of big
data
Analyze
the
Ontologies,
Semantics, Introspection, Data
Integration and Measurement
techniques of big data
Illustrate
the
stepwise
approach to big data analysis
and understand the legalities
and societal issues involved
PO
(b)
PO
(c)
PO
(d)
PO
(f)
PO
(g)
X
PO
(h)
PO
(i)
PO
(j)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
61 PO
(e)
PO
(k)
PO
(l)
X
X
X
X
X
X
X
X
X
PO
(m)
Elective
Year: 2014-2016
Course Title: :Advances in Artificial Intelligence
Course Code: MCNE E13
Credits (L:T:P:SS) :3:0:0
Core/ Elective: Elective
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours: 56
Prerequisites:
Knowledge of any advance programming language
Course Objectives:
The objectives of this course are to:
Present to the students an understanding of the basic concepts and the modern view of Artificial
Intelligence and its applications and the agent approach to AI, agent types, environments and their
applications
•
•
•
•
Identify the problem solving techniques that use different search methods and provide an understanding of
different search methods as informed and uninformed search and their applications.
Provide an ability to assess the applicability, strengths, and weaknesses of the different knowledge
representation and inference methods.
Present an understanding of Game Playing and statistical reasoning techniques and to Identify the importance
of Genetic/ Biological inspired algorithms to solve computational problems.
Analyze the concepts of Natural Language Processing, different learning methods and planning techniques in
solving computational problems and to develop an interest in the field of AI, sufficient to take more advanced
and related subjects.
Course Contents:
Unit 1
Introduction: AI problems, AI technique, Problem as state space search, problem characteristics,
production systems, types of production systems, Design of Search programs.
Heuristic search techniques: Generate and test, Hill climbing, Best first search, Problem reduction,
Constraint satisfaction, Means-Ends Analysis.
Unit 2
Informed search, exploration, constraint satisfaction, adversial search, logical agents: Heuristic
functions, on-line search agents and unknown environment, backtracking search for CSPs, adversial
search, knowledge-based agents, propositional logic reasoning patterns in propositional logic, effective
propositional inference, agents based on propositional logic.
Unit 3
Knowledge Representation: Theorem proving using Predicate logic, Resolution, Natural Deduction,
Knowledge representation using Rules, Forward versus Backward Reasoning, Matching, Control Artificial
Knowledge, Knowledge Structures: Semantic Networks, Frames, Conceptual Dependency diagrams,
Scripts.
Unit 4
Game Playing: Minimax search procedure, adding alpha-beta cutoffs, additional refinements, Iterative
deepening. Statistical Reasoning: Probability and Bayes theorem, Certainty factors and Rules based
systems, Bayesian Networks, Dempster Shafer theorem.
Genetic Algorithms: Survival of the fittest principle in Biology, Genetic Algorithms, Significance of Genetic
operators, termination parameters, Evolving Neural nets, Ant Algorithms.
Unit 5
Planning: Components of planning system, goal stack planning, nonlinear planning using constraint
posting, Hierarchical planning, and Reactive systems.
Natural Language Processing: Steps in NLP, Syntactic processing, Semantic analysis, Discourse and
Pragmatic processing, Statistical NLP, Spell checking.
Learning: Rote learning, learning by taking advice, learning in problem solving, Learning from examples,
Explanation based learning, Discovery, Analogy, Formal learning theory, NN learning and Genetic learning.
62 Text Books:
1. E.Rich, Kevin Knight, shivashankar B Nair, Artificial Intelligence, 3rd edition, TMH Companies
2011.
2. Stuart Russel, Peter Norvig, Artificial Intelligence, A modern Approach, 2 edition, Pearson
education, 2012.
Reference Books:
1. Nils J. Nilsson: “Principles of Artificial Intelligence”, 1st edition, Elsevier, 2002.
2. George F Luger “Artificial Intelligence Structures & Strategies for Complex Problem Solving”,
5th edition, pearson education, 2011.
Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion, lab
exercises and projects.
Course Assessment and Evaluation:
CIE Scheme:
Questions for CIE and SEE will be designed to evaluate the various educational components
(Bloom’s taxonomy)
Indirect
Assessment
Methods
Direct
Assessment Methods
What
To Whom
Internal
Assessment
Tests
C
I
E
S
E
E
Quiz and
Home Work
Students
When/Where
(Frequency in
the course)
Max
Mark
Contribution
to course
outcomes
Thrice (Average of
the best two will be
computed)
25
Blue Books
1,2,3,4
2 quizzes and 2
home work
assignments
25
Quiz and
Home Work
Marks
1,2,3,4
Answer scripts
1,2,3,4
Questionnaire
1,2,3,4,Releva
nce of the
course
Semester
End
Examination
End of Course
(Answering 5 of 10
questions)
100
Students
Feedback
End of the course
-
End of
Course
Survey
Evidence
Collected
Students
Course Outcomes:
At the end of the course students will be able to:
1. Understand the modern view of AI and its application based on agent philosophy and to analyze the
heuristic search techniques.
2. Demonstrate an understanding of various search algorithms commonly used in AI and their
applications, also to identify the importance of Constraint satisfaction problems and the use of logical
agents for inferring knowledge.
3. Understand the various knowledge representation and inference techniques and implement the same in
any form.
4. Demonstrate an understanding of game playing techniques, Statistical reasoning methods and the
application of genetic algorithms in building AI systems.
5. Recognize the components of planning system, steps involved in NLP and the different learning
methods in AI and to use them in building intelligent systems.
63 Mapping Course Outcomes with Program Outcomes:
Program Outcomes*
Course Outcomes
Understand the modern view of AI
and its application based on agent
philosophy and to analyze the
heuristic search techniques.
Demonstrate an understanding of
various search algorithms commonly
used in AI and their applications, also
to
identify
the
importance
of
Constraint satisfaction problems and
the use of logical agents for inferring
knowledge.
Understand the various knowledge
representation
and
inference
techniques and implement the same
in any form.
Demonstrate an understanding of
game playing techniques, Statistical
reasoning
methods
and
the
application of genetic algorithms in
building AI systems.
Recognize the components of
planning system, steps involved in
NLP and the different learning
methods in AI and to use them in
building intelligent systems
PO
(a)
PO
(b)
X
X
X
X
PO
(c)
PO
(d)
PO
(f)
PO
(g)
X
PO
(h)
PO
(k)
PO
(l)
X
X
X
X
PO(i)
PO
(j)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
64 PO
(e)
X
X
X
X
X
X
PO
(m)
Elective
Year: 2014-2016
Course Title: : Fault Tolerant Systems
Course Code: MCNE E14
Credits (L:T:P:SS) :3:0:0
Core/ Elective: Elective
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours:
Hours per week
3
Course Objectives:
• Provide the basic skill for the design of fault tolerant systems. Concepts of reliability,
availability, safety, maintainability, testability, dependability
• Understand Software testing and fault tolerant systems
• Information redundancy, using some types of error detecting/correcting codes
• Study of quantitative methods for reliability evaluation
• Case studies of practical fault tolerant systems
Detailed Syllabus:
Unit 1
Introduction: Fault classification; Types of Redundancy; Basic measures of Fault Tolerance.
Hardware Fault Tolerance: The rate of hardware failures; Failure rate, Reliability, and Mean Time To
Failure; Canonical and Resilient Structures; Other Reliability Evaluation Techniques; Fault-Tolerance –
Processor-Level techniques; Byzantine Failures.
Unit 2
Information Redundancy: Coding; Resilient Disk Systems; Data Replication; Algorithm-Based Fault
Tolerance.Fault-Tolerant Networks: Measures of Resilience; Common Network Topologies and Their
Resilience; Fault-Tolerant Routing.
Unit 3
Software Fault Tolerance: Acceptance Tests; Single-Version Fault Tolerance; N-Version Programming;
Recovery Block Approach; Preconditions, Postconditions, and Assertions; Exception Handling; Software
Reliability Models; Fault-Tolerant Remote Procedure Calls.
Unit 4
Checkpointing: What is Checkpointing? Checkpoint Level; Optimal Checkpointing – An Analytical Model;
Cache-Aided Rollback Error Recovery; Checkpointing in Distributed Systems; Checkpointing in Shared
Memory Systems; Checkpointing in Real-Time Systems; Other uses of Checkpointing.Defect Tolerance in
VLSI Circuits: Manufacturing Defects and Circuit Faults; Probability of Failure and Critical Areas; Basic
Yield Models; Yield Enhancement through Redundancy.
Unit 5
Fault Detection in Cryptographic Systems: Overview of Ciphers; Security Attacks through Fault
Injection; Countermeasures.Case Studies: Non-Stop Systems; Stratus Systems; Cassini Command and
Data Sub-System; IBM G5; IBM Sysplex; Itanium.
Text Book:
1.
Israel Koren, C. Mani Krishna: “Fault-Tolerant Systems”, 1st edition, Elsevier, 2009.
Reference Books:
1.
2.
D. K. Pradhan (Ed):”Fault Tolerant Computer Systems Design”, Prentice Hall, 1996.
K. S. Trivedi: “Probability, Statistics with Reliability, Queuing and Computer Network Applications”, 1st edition, John
Wiley, 2011.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: (i)
develop an understanding of the issues of reliability and its evaluation in the design of computer
systems, and to emphasize their importance.(ii) Examine the concepts and techniques for
redundant designs which can make a system fault tolerant, i.e. still functioning correctly in the
presence of failures, in hardware, software and communications. (iii) Discuss techniques for
developing distributed reliable hardware and software systems. (iv) Examine testing techniques
and algorithms in hardware, software and communications.
65 Elective
Year: 2014-2016
Course Title: Analytical Approach in Data
Networks
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E15
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objective:
This course will introduce the concept of analytical methods to evaluate the performance of the different
aspects of a Computer Network. While the students have gone through a complete account of theoretical
study in Data Communications and Computer Networks, they may not have exposed to mathematical
techniques with respect to design and analysis. This will enable the students not only to get a thorough
picture of Computer System performance but also pave the way for pursuing research towards higher
degree.
Unit 1
Introduction to Digital Transmission, Error Detection, Effectiveness of Error Detection codes, Twodimensional Parity checks, Polynomial codes. Standardized Polynomial Codes, Error Detecting Capability of
a Polynomial code, Linear Codes, Performance of Linear Codes, Error Correction
(Garcia’s Book pages 166 to 190)
Unit 2
Introduction to Data Link Layer, Framing, Character – oriented Framing, Bit- oriented, Framing, Length
fields, Framing with errors, Maximum Frame size-Variable Frame Length, Fixed Frame Length, Little’s
theorem, Probabilistic Form of Little’s theorem, Applications of Little’s theorem, Occupancy distribution
upon arrival, Occupancy Distribution upon, Occupancy distribution upto departure, Brief review of
Queuing models
(Gallager’s book, pages 86-96, 152-162, 171-173)
Unit 3
Delay Models in Data Networks – Introduction, Multiplexing of Traffic on a Communication Link, Queuing
Models. Statistical Multiplexing, Poisson arrival process and packet loss probability, M/M/m/m: The mserver loss system, M/G/1 system, Priority Queuing, nonpreemptive priority, Preemptive resume priority,
Network of queues, Jackson’s theorem, Multiple class of customers,
(Garcia’s book pages 340-348, Gallager’s book pages 178, 179, 186-190, 203-209, 221-233)
Unit 4
Multiaccess communication-Review of Aloha Networks, Idealized Slotted Multiaccess Model, Stabilized
Slotted Aloha, Splitting algorithms, Tree Algorithms, FCFS Splitting Algorithms, Improvements in the FCFS
splitting Algorithm, CSMA Slotted Aloha, FCFS Splitting Algorithms for CSMA. (Gallager’s book pages 275277, 282 – 283, 289 – 302, 305 – 307, 310, 311)
Unit 5
Topological Design of Networks: Flow Models, An overview of Topological Design Problems, Subnet Design
Problem, Capacity Assignment Problem, Heuristic Methods for Capacity Assignment, Network Reliability
Issues, Spanning Tree Topology Design, Local Area Network Design Problem,
(Gallager’s book Pages
433-451)
Text Book:
1. Alberto Leon-Garcia and Indra Widjaja, Communication Networks, Fundmental Concepts and Key
Architectures, Second Reprint, Tata McGraw-Hill, 2004
2. Dimitri Bertsekas and Robert Gallager, Data Networks, Second Edition, Prentics Hall of India, 2000
Reference Book:
Anurag Kumar, Manjunath and Joy Kuri: Communication NetworkingReprint, Morgan Kaufman Publishers, 2006
66 An Analytical Approach, Indian
Elective
Year: 2014-2016
Course Title: Software Defined Network
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E16
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
•
•
•
•
•
Explore the current state of the OpenFlow model and centralized network control
Delve into distributed and central control, including data plane generation and examine the
structure and capabilities of commercial and open source controllers
Survey the available technologies for network programmability and trace the modern data center
from desktop-centric to highly distributed models
Discover new ways to connect instances of network-function virtualization and service chaining and
get detailed information on constructing and maintaining an SDN network topology
Examine an idealized SDN framework for controllers, applications, and ecosystems
Unit 1
Introduction: Centralized and Distributed Control and Data Planes, Evolution versus Revolution, The
Control Plane, Data Plane, Moving Information Between Planes, Why Can Separation Be Important? ,
Distributed Control Planes, IP and MPLS, Creating the IP Underlay, Convergence Time, Load Balancing,
High Availability, Creating the MPLS Overlay, Replication, Centralized Control Planes, Logical Versus Literal,
ATM/LANE, Route Servers,
OpenFlow: Introduction, Wire Protocol, Replication, FAWG, Config and Extensibility, Architecture, Hybrid
Approaches, Dual Function Switches.
Unit 2
SDN Controllers: Introduction, General Concepts, VMware, Nicira, VMware/Nicira, OpenFlow-Related,
Mininet, NOX/POX, Trema, Ryu, Big Switch Networks/Floodlight, Layer 3 Centric, L3VPN, Path Computation
Element Server, Plexxi, Plexxi Affinity, Cisco OnePK, Relationship to the Idealized SDN Framework.Network
Programmability: Introduction, The Management Interface, The Application-Network Divide, The
Command-Line Interface, NETCONF and NETMOD, SNMP,Modern Programmatic Interfaces, Publish and
Subscribe Interfaces,XMPP , Thrift, JSON, I2RS, Modern Orchestration, OpenStack, CloudStack, Puppet.
Unit 3
Data Center Concepts and Constructs: Introduction, The Multitenant Data Center, The Virtualized
Multitenant Data Center, Orchestration, Connecting a Tenant to the Internet/VPN 168, Virtual Machine
Migration and Elasticity, Data Center Interconnect (DCI), Fallacies of Data Center Distributed Computing,
Data Center Distributed Computing Pitfalls to Consider, SDN Solutions for the Data Center Network, The
Network Underlay, VLANs, EVPN, Locator ID Split (LISP), VxLan, NVGRE, OpenFlow, Network Overlays,
Network Overlay Types.
Network Function Virtualization: Introduction, Virtualization and Data Plane, Data Plane I/O, Services
Engineered Path, Service Locations and Chaining, Metadata, An Application Level Approach, Scale, NonETSI NFV Work, Middlebox Studies, Embrane/LineRate, Platform Virtualization
Unit 4
Network Topology and Topological Information Abstraction: Introduction, Network Topology, Traditional
Methods, LLDP, BGP-TE/LS, BGP-LS with PCE, ALTO, BGP-LS and PCE Interaction with ALTO, I2RS
Topology Building an SDN FrameworkIntroduction: The Juniper SDN Framework, IETF SDN Framework(s),
SDN(P), ABNO, Open Daylight Controller/Framework, API, High Availability and State Storage, Analytics,
Policy Use Cases for Bandwidth Scheduling, Manipulation, and Calendaring : Introduction, Bandwidth
Calendaring, Base Topology and Fundamental Concepts, OpenFlow and PCE Topologies, Example
Configuration, OpenFlow Provisioned Example, Enhancing the Controller, Overlay Example Using PCE
Provisioning, Expanding Your Reach, Big Data and Application Hyper-Virtualization for Instant CSPF,
Expanding Topology.
Unit 5
Use Cases for Data Center Overlays, Big Data, and Network Function Virtualization: Introduction, Data
Center Orchestration, Creating Tenant and Virtual Machine State, Forwarding State, Data-Driven Learning,
Control-Plane Signaling, Scaling and Performance Considerations, Puppet (DevOps Solution), Network
67 Function Virtualization (NFV), NFV in Mobility, Optimized Big Data,Use Cases for Input Traffic Monitoring,
Classification, and Triggered Actions: Introduction, The Firewall, Firewalls as a Service, Network Access
Control Replacement, Extending the Use Case with a Virtual Firewall, Feedback and Optimization, Intrusion
Detection/Threat Mitigation
Text Book:
Thomas D.Nadeau & Ken Gray: SDN Software Defined Networks O'Reilly publishers, First edition, 2013.
Reference Books:
1. Paul Goransson: Software Defined Networks A Comprehensive Approach , Elsevier, 2014.
Elective
Year: 2014-2016
Course Title: : Data Structures and Algorithms
Course Code: MCNE E18
Credits (L:T:P:SS) :3:0:0
Core/ Elective: Elective
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours:
hours per week
3
Course Objectives:
When developing software it is important to know how to solve problems in a computationally
efficient way. Algorithms describe methods for solving problems under the constraints of the
computers resources. Often the goal is to compute a solution as fast as possible, using as few
resources as possible. To solve a problem efficiently it may be necessary to use data structures
tailored for the particular problem(s) at hand. A data structure is a specific way of organizing
data that supports efficient performance of the relevant operations on that data. For instance
there are data structures for organizing large numbers of records where records already present
can be quickly found and/or deleted, and new records can be inserted and found fast. This
course will help students to achieve the following objectives: (i) Assess how the choice of data
structures and algorithm design methods impacts the performance of programs. (ii) Choose the
appropriate data structure and algorithm design method for a specified application. (iii) Write
programs using object-oriented design principles. (iv) Solve problems using data structures such
as linear lists, stacks, queues, hash tables, binary trees, heaps, tournament trees, binary search
trees, and graphs and writing programs for these solutions (v) Solve problems using algorithm
design methods such as the greedy method, divide and conquer, dynamic programming,
backtracking, and branch and bound and writing programs for these solutions.
Topics:
Algorithm Analysis, List, Stacks, and Queues, Trees, Hashing, Priority Queues (Heaps), Sorting,
Graph Algorithms, Algorithm Design Techniques.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: (i)
Understand the concept of arrays, structures pointers, recursion, stack, queue linked list (ii)
Basic ability to analyze algorithms and to determine algorithm correctness and time efficiency
class. (iii) Master a variety of advanced abstract data type (ADT) and data structures and their
implementations. (iv) Master different algorithm design techniques (brute force, divide and
conquer, greedy,etc.) (v) Ability to apply and implement learned algorithm design techniques
and data structures to solve problems.
Detailed Syllabus:
Unit 1
Algorithm Analysis: Mathematical Background, Model, What to Analyze, Running Time
Calculations.
List, Stacks, and Queues: Abstract Data Types (ADTs), The List ADT, Vector and List in the
STL, Implementation of Vector, Implementation of List, The Stack ADT, The Queue ADT.
68 Unit 2
Trees: Preliminaries, Binary Trees, The Search Tree ADT – Binary Search Trees.
Hashing: General Idea, Hash Function, Separate Chaining, Hash Tables Without Linked Lists,
Rehashing, Hash Tables in the Standard Library.
Unit 3
Priority Queues (Heaps): Model, Simple Implementation, Binary Heap, Applications of Priority
Queues, Priority Queues in the standard Library.
Unit 4
Sorting: Preliminaries, Insertion Sort, Merge sort, Quicksort.
Graph Algorithms: Definitions, Topological Sort, Shortest-Path Algorithms, Minimum Spanning
Tree, Applications of Depth-First Search approaches.
Unit 5
Algorithm Design Techniques: Greedy Algorithms,
Programming, Backtracking Algorithms.
Divide
and
Conquer,
Dynamic
Text Book:
1. Mark Allen Weiss: Data Structures and Algorithm Analysis in C++, 3rd Edition, Pearson
Education, 2011.
Reference Books:
1. Yedidyah, Augenstein, Tannenbaum: Data Structures Using C and C++, 1st Edition,
Pearson Education, 2012.
2. Sartaj Sahni: Data Structures, Algorithms and Applications in C++, 2nd Edition,
Universities press, 2011.
Elective
Year: 2014-2016
Course Title: Stochastic Processes
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E20
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives (i) to understand discrete
time, discrete-state Markov chains, renewal processes, and the Poisson process, (ii) to
understand continuous-time, discrete-state Markov processes; queuing theory; Brownian
motion; martingales; and possibly some other topics, and (iii) to develop that may cover topics
such as convergence of sequences of stochastic processes, and stochastic calculus.
Topics:
Discrete-time Markov Chains, Renewal Processes, and Poisson Process
Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: (i) to
learn to develop an ability to model dynamical processes with noise as stochastic processes; (ii)
to learn to develop an understanding of important qualitative characteristics of stochastic
processes; (iii) to learn to develop an ability to analyze some basic stochastic processes
Detailed Syllabus:
Unit 1
69 Preliminaries: Probability, Random Variables, Expected Value, Moment Generating,
Characteristic Functions, and Laplace Transforms, Conditional Expectation, The Exponential
Distribution, Lack of Memory, and Hazard Rate Functions, Some Probability Inequalities, Limit
Theorems, Stochastic Process.
The Poisson Process: Interarrival and waiting Time Distributions, Conditional Distribution of
Arrival Times, Nonhomogeneous Poisson Process, Compound Poisson Random Variables and
processes, Conditional Poisson Processes.
Unit 2
Renewal Theory: Introduction and Preliminaries, Distribution of N(t), Some Limit Theorems,
The Key Renewal Theorem and Applications, Delayed Renewal Processes, Renewal Reward
Processes, Regenerative Processes, Stationary Point Processes.
Unit 3
Markov Chains: Introduction and Examples, Chapman-Kolmogorov Equations and Classification
of States, Limit Theorems, Transitions among Classes, the Gambler’s Ruin Problem, and Mean
Times in Transient States, Branching Processes, Applications of Markov Chains, Time Reversible
Markov Chains, Semi Markov Processes.
Unit 4
Continuous-Time Markov Chains: Introduction, Continuous-Time Markov Chains, Birth and
death Processes, The Kolmogorov Differential Equations, Limiting Probabilities, Time
Reversibility, Applications of the Reversed Chain to queuing Theory, uniformization.
Martingales: Introduction, Stopping Times, Azuma’s Inequality for Martingales, Sub
martingales, Super martingales and the Martingale Convergence Theorem, A Generalized Azuma
Inequality.
Random Walks: Duality in Random Walks, Some Remarks concerning Exchangeable Random
Variables, Using Martingales to Analyze Random Walks, Applications to G/G/1 Queues and Ruin
Problems, Blackwell’s Theorem on the Line.
Unit 5
Brownian Motion and Other Markov Processes: Introduction and Preliminaries, Hitting
Times, Maximum Variable, and Arc Sine Laws, Variations on Brownian Motion, Brownian Motion
with Drift, Backward and Forward Diffusion Equations, Applications of the Kolmogorov Equations
to Obtaining Limiting Distributions, A Markov Shot Noise Process, Stationary Process.
Text Books:
1. Sheldon M Ross: Stochastic Process, 4th Edition, Wiley, 2012.
2. Kishor S Trivedi: Probability and Statistics with Reliability Queuing and Computer Network
Applications, 1st Edition, Wiley, 2011.
Reference Books:
1. R.W. Wolff: Stochastic Modeling and Queuing Theory, Prentice Hall, 1989.
2. B. R. Bhat: Stochastic Models Analysis and Applications, 1st edition, New Age
International, 2010.
Elective
Year: 2014-2016
Course Title: : Advanced Algorithms
Course Code: MCNE E21
Credits (L:T:P:SS) :3:0:0
Core/ Elective: Elective
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours:
hours per week
70 3
Course Objectives:
This is a first course in the design and analysis of algorithms. The main focus is on techniques
for constructing correct and efficient algorithms, and on tools to reason about them. The course
forms a foundation for all areas of Computer Network. The particular computational problems
discussed have applications in artificial intelligence, computational biology, compiler
construction, hardware and network protocols, and optimization. This course will help students
to achieve the following objectives: (i) Discuss and use fundamental algorithms and algorithmic
techniques. (ii) Decide which algorithm among a set of possible choices is best for a given
application. (iii) Prove correctness and analyze the running time of a given algorithm. (iv)
Design efficient algorithms for new situations, using as building blocks the techniques learned.
(v) Prove a problem is NP-complete using reduction and understand the implications.
Topics:
Analysis Techniques, Graph Algorithms, Polynomials and the FFT, Number -Theoretic Algorithms,
String-Matching Algorithms, Approximation Algorithms, Introduction Parallel Algorithms,
Introduction to Amortization
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: (i) learn
good principles of algorithm design (ii) learn how to analyze algorithms and estimate their
worst-case and average-case behavior (iii) Become familiar with fundamental data structures
and with the manner in which these data structures can best be implemented (iv) Become
accustomed to the description of algorithms in both functional and procedural styles (v) learn
how to apply their theoretical knowledge in practice
Detailed Syllabus:
Unit 1
Analysis Techniques: Growth of Functions: Asymptotic notations; Standard notations and common
functions; Recurrences and Solution of Recurrence equations- The substitution method, The recurrence –
tree method, The master method; Amortized Analysis: Aggregate, Accounting and Potential Methods.
Unit 2
Graph Algorithms: Bellman - Ford Algorithm; Single source shortest paths in a DAG; Johnson’s Algorithm
for sparse graphs; Flow networks and Ford-Fulkerson method; Maximum bipartite matching.
Unit 3
Polynomials and the FFT: Representation of polynomials; The DFT and FFT; Efficient implementation of
FFT.
Number -Theoretic Algorithms: Elementary notions; GCD; Modular Arithmetic; Solving modular linear
equations; The Chinese remainder theorem; Powers of an element; RSA cryptosystem; Primality testing;
Integer factorization.
Unit 4
String-Matching Algorithms: Naïve string Matching; Rabin - Karp algorithm; String matching with finite
automata; Knuth-Morris-Pratt algorithm Boyer – Moore algorithms. Approximation Algorithms: The
vertex-cover problem; The traveling-sales-person problem; The setcovering problem;The subset-sum
problem.
Unit 5
Introduction Parallel Algorithms: Parallel Sorting Algorithms, Parallel Search Algorithms. Introduction
to Amortization.
Text Books:
1. T. H Cormen, C E Leiserson, R L Rivest and C Stein: “Introduction to Algorithms”, 3rd
Edition, Prentice-Hall of India, 2011.
71 2. Mark Allen Weiss, Data Structures and Algorithm analysis in C++, 3rd edition, PEA, 2011.
Reference Book:
Ellis Horowitz, Sartaj Sahni, S.Rajasekharan: “Fundamentals of Computer Algorithms”,1st
edition, University Press, 2012.
Elective
Year: 2014-2016
Course Title: : Embedded Computing Systems
Course Code: MCNE E23
Credits (L:T:P:SS) :3:0:0
Core/ Elective: Elective
Type of course: Lecture/ Laboratory/ /Project/
assignment
Total Contact Hours:
Prerequisites: Knowledge of Microprocessor / Microcontroller
Course Objectives:
The Objective of the course is to:
• Help students with the concept and applications of embedded systems in our day today
lives.
• Provide students with the knowledge of what makes a system a real time one.
• Explain the characteristics of latency in real-time systems
• Summarize and make the students understand the major issues concerning the real time
systems and how these issues are addressed.
• Help students to understand embedded software development and Implementation .
Course Contents
Unit 1
Embedded Computing: Introduction, Complex Systems and Microprocessors
Embedded Systems Design Process, design metrics, flow and optimization, Specifications and
modeling, Cortex-M0 Technical Overview
Unit 2
Implementation Features System Features , Debug Features, Operation Modes and States ,
Architecture, Registers and Special Registers Behaviors of the Application Program Status
Register (APSR), Memory System Overview, Stack Memory , Operations Introduction to CortexM0 Programming, Instruction Set, ,
Unit 3
Instruction Usage Examples, Memory System, Exceptions and Interrupts, Interrupt Control and
System Control, Operating System Support Features
Unit 4
Low-Power Features, Review of Sleep Modes in the Cortex-M0 Processor Using WFE and WFI in
Programming Using the Send-Event-on-Pend , Event Communication Interface Feature Fault
Handling, Debug Features, Debug Features Overview Debug Interface Debug System Simple
Application Programming, Using CMSIS, Using the SysTick Timer as a Single Shot Timer, UART
Examples, Simple Interrupt Programming
Unit 5
Embedded/ Real Time Operating System Concepts: Architecture of the kernel. Task and task
scheduler. ISR. Semaphores. Mutex. Mailboxes. Message queues. Message queues Pipes.
Signals. Memory management. Priority inversion problem. Programming in RTLinux.
Text Books:
72 1.
2.
3.
4.
Wayne Wolf: Computers as Components Principles of Embedded Computer System Design, Second
Edition, Elsevier, 2008.
Frank Wahid, “Embedded System Design
The Definitive Guide to the ARM Cortex-M0 by Joseph Yiu
Dr. K.V.K.K. Prasad: Embedded/Real-Time Systems: Concepts, Design and
Programming – Black
Book” New Edition, Dreamtech. Press, 2009.
Reference Book:
1. Phillip A. Laplante Real-Time Systems Design and Analysis Third Edition, Wiley 2009
Course Delivery:
The course will be delivered through lectures, presentations and classroom discussions. Questions for CIE and SEE are
designed in accordance with the Bloom’s Taxonomy.
Course Assessment and Evaluation:
To Whom
Internal
Assessment
Tests
C
I
E
S
E
E
Indirect
Assessment
Methods
Direct
Assessment Methods
What
Quiz and
Home Work
Students
Max
Mark
When/Where
(Frequency in
the course)
Evidence
Collected
Contribution
to course
outcomes
Thrice (Average of
the best two will be
computed)
25
Blue Books
1,2,3,4
2 quizzes and 2
home work
assignments
25
Quiz and
Home Work
Marks
1,2,3,4
Answer scripts
1,2,3,4
Questionnaire
1,2,3,4,Releva
nce of the
course
Semester
End
Examination
End of Course
(Answering 5 of 10
questions)
100
Students
Feedback
End of the course
-
Students
End of
Course
Survey
Course Outcomes:
As the end of the course the student will be able to:
1. Understand the usage of tiny processors in real time.
2. Design a process to integrate different processors to achieve certain goals.
3. Learn the skills required for an embedded system engineer.
4. Demonstrate and implement various tools and techniques to develop, test and validate embedded systems.
5. Understand the working process of various real time operating systems.
Mapping Course Outcomes with Program Outcomes:
Course Outcomes
Understand the usage of tiny
processors in real time.
Program Outcomes
PO
(a)
PO
(b)
PO
(c)
X
X
X
PO
(d)
PO
(e)
X
73 PO
(f)
PO
(g)
X
PO
(h)
PO
(i)
X
PO
(j)
PO
(k)
PO
(l)
X
PO
(m
)
Design a process to integrate
different
processors
to
achieve certain goals
Learn the skills required for
an
embedded
system
engineer
Demonstrate and implement
various tools and techniques
to develop, test and validate
embedded systems
Understand
the
working
process of various real time
operating systems
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Elective
Course Title: Web Technologies
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
X
X
X
X
X
X
X
X
X
X
X
X
Year: 2014-2016
Course Code: MCNE E26
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Prerequisites: NIL
Course Objectives :
At the end of the course the students should be able to:
•
Implement Perl scripts to parse and manipulate structured data.
•
Design and build robust and maintainable web applications using a server-side scripting language and servlets
and JSP.
•
Design simple database driven web applications using a server-side scripting language.
•
Design web applications using the powerful Rails framework.
•
Use AJAX to develop web applications for a dynamic user experience
Course Contents:
Unit 1
Perl, CGI Programming: Origins and uses of Perl; Scalars and their operations; Assignment statements
and simple input and output; Control statements; Fundamentals of arrays; Hashes; References; Functions;
Pattern matching; File input and output; Examples. The Common Gateway Interface; CGI linkage; Query
string format; CGI.pm module; A survey example; Cookies.
Unit 2
Servlets and Java Server Pages: Overview of Servlets; Servlet details; A survey example; Storing
information on Clients; Java Server Pages.PHP: Origins and uses of PHP; Overview of PHP; General
syntactic characteristics; Primitives, operations and expressions; Output; Control statements; Arrays;
Functions; Pattern matching; Form handling; Files; Cookies; Session tracking.
Unit 3
Database Access through the Web: Relational Databases; An introduction to SQL; Architectures for
Database access; The MySQL Database system; Database access with PERL and MySQL; Database access
with PHP and MySQL; Database access with JDBC and MySQL.
Unit 4
Introduction to Ruby, Rails: Origins and uses of Ruby; Scalar types and their operations; Simple input
and output; Control statements; Fundamentals of arrays; Hashes; Methods; Classes; Code blocks and
iterators; Pattern matching. Overview of Rails; Document requests; Processing forms; Rails applications
with Databases; Layouts.
Unit 5
Introduction to Ajax: Overview of Ajax; The basics of Ajax; Rails with Ajax.
Text Book:
1. Robert W. Sebesta: “Programming the World Wide Web”, 4th Edition, Pearson Education, 2012.
Reference Books:
1. M. Deitel, P.J. Deitel, A. B. Goldberg: “Internet & World Wide Web How to program”, 3rd Edition,
Pearson Education, 4th edition, PHI, 2011.
2. Chris Bates: “Web Programming Building Internet Applications”, 3rd Edition, Wiley India, 2011.
3. Joyce Farrell, Xue Bai, Michael Ekedahl: “The Web Warrior Guide to Web Programming”, 1st edition,
Thomson, 2010.
74 Course Delivery:
The course will be delivered through lectures, class room interaction, group discussion, lab exercises and
projects.
Course Assessment Methods:
CIE Scheme:
Direct
Assessment
Methods
What
CIE
Indirect Assessment
Methods
Internal
Assessment
Tests
Project and lab
test
SEE
When/ Where
(Frequency in the
course)
Thrice (Average of the
best two will be
computed)
To
Whom
Students
Max
Marks
Evidence
Collected
Contribution to
Course Outcomes
30
Blue Books
1-4
Summation of lab and
project
20
Data Sheets
1-5
End of Course
(Answering
5 of 10 questions)
100
Answer scripts
1-5
End of the course
-
Questionnaire
3-5, Relevance of
the course
Semester End
Examination
Students
Feedback
Students
End of Course
Survey
Questions for CIE and SEE will be designed to evaluate the various educational components (Bloom’s taxonomy)
Course Outcomes :
1. Apply perl for text and file processing and write CGI applications.
2. Create dynamic HTML content with Servlets and JavaServer Pages and PHP.
3. Design dynamic data-driven Web sites using MySQL and PHP and perl
4. Apply MVC design pattern to write database-backed Web Applications using the Ruby on Rails
Framework .
5. Understand the relationship between the different protocols that comprise AJAX.
Mapping Course Outcomes with Program Outcomes:
Program Outcomes*
Course Outcomes
Apply perl for text and file
processing
and
write
CGI
applications
Create dynamic HTML content
with Servlets and JavaServer
Pages and PHP
Design dynamic
data-driven
Web sites using MySQL and PHP
and perl
Apply MVC design pattern to
write
database-backed
Web
Applications using the Ruby on
Rails Framework
Understand
the
relationship between the
different protocols that
comprise AJAX
PO
(a)
PO
(b)
X
X
PO
(c)
PO
(d)
PO
(e)
X
X
PO
(g)
PO
(h)
X
X
PO
(i)
PO
(j)
PO
(k)
PO
(l)
PO
(m)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
75 PO
(f)
X
X
Elective
Year: 2014-2016
Course Title: Multimedia Communications
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E28
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives: This course will help students to achieve the following objectives:
understand the basics of analog and digital video: video representation and transmission,
analyze analog and digital video signals and systems, know the fundamental video processing
techniques, acquire the basic skill of designing video compression, familiarize himself/herself
with video compression standards and know the basic techniques in designing video
transmission systems: error control and rate control.
Topics:
Introduction to Multimedia Communications, Framework for Multimedia Standardization,
Application Layer, Middleware Layer, Network Layer.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: (i) Learn
the basics of analog and digital video: color video formation and specification, analog TV system,
video raster, digital video formats, (ii) Frequency domain analysis of video signals, spatial and
temporal frequency response of the human visual system, (iii) Scene, camera, and motion
modeling, 3D motion and projected 2D motion, models for typical camera/object motions and
(iv) 2D motion estimation: optical flow equation, different motion estimation methods (pelbased, block-based, mesh-based, global motion estimation, multi-resolution approach).
Detailed Syllabus:
Unit 1
Introduction to Multimedia Communications: Introduction, Human communication model,
Evolution and convergence, Technology framework, Standardization framework.
Unit 2
Framework for Multimedia Standardization: Introduction, Standardization activities,
Standards to build a new global information infrastructure, Standardization processes on
multimedia communications, ITU-T mediacom2004 framework for multimedia, ISO/IEC MPEG21 multimedia framework, IETF multimedia Internet standards.
Unit 3
Application Layer: Introduction, ITU applications, MPEG applications, Mobile servers and
applications, Universal multimedia access.
Unit 4
Middleware Layer: Introduction to middleware for multimedia, Media coding, Media Streaming,
Infrastructure for multimedia content distribution.
Unit 5
Network Layer: Introduction, QoS in Network Multimedia Systems.
Text Books:
1. K.R. Rao, Zoran S. Bojkovic, Dragorad A. Milovanovic: Introduction to Multimedia
Communications – Applications, Middleware, Networking, 1st edition, Wiley India, 2011.
76 Reference Books:
1. Fred Halsall: Multimedia Communications – Applications, Networks, Protocols, and
Standards, 1st edition, Pearson, 2012.
2. Nalin K Sharad: Multimedia information Networking, 1st edition, PHI, 2010.
3. Ralf Steinmetz, Klara Narstedt: Multimedia Fundamentals: Volume 1-Media Coding and
Content Processing, 1st Edition, Pearson, 2010.
4. Prabhat K. Andleigh, Kiran Thakrar: Multimedia Systems Design, 1st edition, PHI, 2011.
Elective
Year: 2014-2016
Course Title: Cloud Computing
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E30
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Prerequisites: NIL
Course Objectives
The objectives of this course are to
• Provide an understanding cloud computing delivery models.
• Analyze the features cloud applications and Paradigms
• Provide understanding of Virtualization
• Identify policies and mechanisms for resource management
• Analyze scheduling algorithms for cloud computing systems and cloud security
Course Contents:
Unit 1
Introduction: Network centric computing and network centric content, Peer-to-peer systems,
Cloud Computing: an old idea whose time has come, Cloud Computing delivery models &
Services, Ethical issues, Cloud vulnerabilities, Challenges, Cloud Infrastructure: Amazon,
Google, Azure & online services, open source private clouds. Storage diversity and vendor lockin, intercloud, Energy use & ecological impact of data centers, service level and compliance level
agreement, Responsibility sharing, user experience, Software licensing.
Unit 2
Cloud Computing Applications & Paradigms: Challenges, existing and new application
opportunities, Architectural styles of cloud applications, Workflows coordination of multiple
activities, Coordination based on a state machine model -the Zoo Keeper, The Map Reduce
programming model, Apache Hadoop, A case study: the GrepTheWeb application, Clouds for
science and engineering, High performance computing on a cloud, Social computing, digital
content, and cloud computing.
Unit 3
Cloud Resource Virtualization: Layering and virtualization, Virtual machine monitors, Virtual
machines Performance and security isolation, Full virtualization and paravirtualization, Hardware
support for virtualization Case study: Xen -a VMM based on paravirtualization, Optimization of
network virtualization in Xen 2.0, vBlades -paravirtualization targeting a x86-64 Itanium
processor, A performance comparison of virtual machines, Virtual machine security, The darker
side of virtualization, Software fault isolation.
Unit 4
Cloud Resource Management and Scheduling: Policies and mechanisms for resource
management, Applications of control theory to task scheduling on a cloud, Stability of a two-
77 level resource allocation architecture, Feedback control based on dynamic thresholds,
Coordination of specialized autonomic performance managers, A utility-based model for cloudbased web services, Resource bundling.
Unit 5
Storage systems: Evolution, Storage models, file systems, databases, DFS, General parallel
File system, GFS, Hadoop, Locks & Chubby, TPS, NOSQL, Bigdata, Mega store. Cloud security:
Risks, privacy and privacy impacts assessments.
Cloud Application Development: Amazon web Services:EC2 Instances, Connecting Clients,
Security Rules,Launch an EC2 Linux Instance and connect it, create EC2 placement Group,to use
S3 in java, to manage SQS services in C#,to install simple notification service on Ubuntu 10.04,
Cloud based simulation of a didtributed trust Algorithm,A Trust Management Service,A Cloud
service for Adaptive Data Streaming,Cloud- based Optimal FPGA syntesis.
Text Book:
1. Cloud Computing: Theory and Practice, Dan Marinescu, 1st edition, MK Publishers, 2013.
Reference Books:
1. Cloud Computing: Theory and Practice, Dan Marinescu, 1st edition, MK Publishers, 2013.
2. Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, Kai
Hwang, Jack Dongarra, Geoffrey Fox. MK Publishers.
3. Cloud Computing: A Practical Approach, Anthony T. Velte, Toby J. Velte, Robert Elsenpeter,
McGraw Fill, 2010.
Course Delivery
The course will be delivered through lectures, presentations, classroom discussions, practice
exercises and practical sessions.
Course Assessment and evaluation:
Indirect
Assess
ment
Methods
Direct
Assessment Methods
What
To
Whom
Internal
Assessment
Tests
CIE
Mini Project/
Assignment
Submitted as
a Team Work
SEE
Students
Semester End
Examination
Students
Feedback
End of Course
Survey
Students
When/
Where
(Frequency
in the
course)
Thrice
(Average of
the best two
will be
computed)
Max
Marks
Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1-5
Review 1
Review 2
20
Soft Copy of
the Work
Executed
1-5
End of Course
(Answering
5 of 10
questions)
50
Answer scripts
1-5
End of the
course
-
Questionnaire
1-5, Relevance
of the course
Course Outcomes
At the end of the course students should be able to:
1. Analyze the transformation let to the evolution of Cloud computing, it's impact on the
enterprises and list the different services offered by service providers.
2. Design different workflows according to requirements applying map reduce model.
78 3. Make performance comparison of virtual machines, Virtual machine security.
4. Create combinatorial auctions for cloud scheduling algorithms for computing clouds.
5. Assess the Cloud security, the risks involved, its impact and cloud service providers.
Mapping Course Outcomes with Programme Outcomes:
Course Outcomes
Analyze the transformation let to the
evolution of Cloud computing, it's
impact on the enterprises and list
the different services offered by
service providers
Design different workflows according
to requirements applying map
reduce model.
Make performance comparison of
virtual machines, Virtual machine
security.
Create combinatorial auctions for
cloud scheduling algorithms for
computing clouds.
Assess the Cloud security, the risks
involved, its impact and cloud
service providers.
Program Outcomes
PO
1
PO
2
PO
3
X
X
X
X
X
P
O
10
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
PO
4
PO
5
X
Elective
PO
6
PO
7
PO
8
PO
9
PO
11
P
O
12
Year: 2014-2016
Course Title: Software Architecture
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E32
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives: The objective of the course is
to provide a sound technical exposure to the concepts, principles, methods, and best practices in
software architecture and software design. Principal topics that will be covered include object
oriented analysis and design, UML (Unified Modeling Language) modeling, architectural patterns,
analysis of architectures, formal descriptions of software architectures, design patterns, extreme
programming, refactoring, distributed objects, component technology, and object oriented
frameworks. Case studies and programming assignments will be an integral part of the course.
Topics:
The Architecture Business Cycle, Architectural Styles and Case Studies, Quality, Architectural
Patterns, Some Design Patterns, Designing and Documenting Software Architecture.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: produce
"software architects" with sound knowledge and superior competence in building robust,
scalable, and reliable software intensive systems in an extremely effective way. They would have
a clear appreciation of the role of abstraction, modeling, architecture, and design patterns in the
development of a software product. They would be able to make optimal architectural choices
79 P
O
13
X
and employ the most relevant methods, best practices, and technologies for architecting and
implementing a software product, regardless of its complexity and scale.
Detailed Syllabus:
Unit 1
Introduction: The Architecture Business Cycle: Where do architectures come from? Software
processes and the architecture business cycle; What makes a “good” architecture? What
software architecture is and what it is not; Other points of view; Architectural patterns,
reference models and reference architectures; Importance of software architecture; Architectural
structures and views.
Unit 2
Architectural Styles and Case Studies: Architectural styles; Pipes and filters; Data
abstraction and object-oriented organization; Event-based, implicit invocation; Layered systems;
Repositories; Interpreters; Process control; Other familiar architectures; Heterogeneous
architectures. Case Studies: Keyword in Context; Instrumentation software; Mobile robotics;
Cruise control; Three vignettes in mixed style.
Unit 3
Quality: Functionality and architecture; Architecture and quality attributes; System quality
attributes; Quality attribute scenarios in practice; Other system quality attributes; Business
qualities; Architecture qualities. Achieving Quality: Introducing tactics; Availability tactics;
Modifiability tactics; Performance tactics; Security tactics; Testability tactics; Usability tactics;
Relationship of tactics to architectural patterns; Architectural patterns and styles.
Unit 4
Architectural Patterns: Introduction; From mud to structure: Layers, Pipes and Filters,
Blackboard. Distributed Systems: Broker; Interactive Systems: MVC, Presentation-AbstractionControl. Adaptable Systems: Microkernel; Reflection.
Unit 5
Some Design Patterns: Structural decomposition: Whole – Part; Organization of work: Master
– ave; Access Control: Proxy.
Designing and Documenting Software Architecture: Architecture in the life cycle;
Designing the architecture; Forming the team structure; Creating a skeletal system. Uses of
architectural documentation; Views; Choosing the relevant views; Documenting a view;
Documentation across views.
Text Books:
1. Len Bass, Paul Clements, Rick Kazman: “Software Architecture in Practice, 2nd Edition”,
Pearson Education, 2010.
2. Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal:
“Pattern-Oriented Software Architecture, A System of Patterns, Volume 1”, John Wiley
and Sons, 2007.
3. Mary Shaw and David Garlan: “Software Architecture-Perspectives on an Emerging
Discipline”, 1st edition, Prentice-Hall of India, 2011.
Reference Books:
1. E. Gamma, R. Helm, R. Johnson, J. Vlissides: “Design Patterns-Elements of Reusable
Object-Oriented Software”,1st edition, Addison-Wesley, 2006.
2. Web site for Patterns: http://www.hillside.net/patterns/
80 Elective
Year: 2014-2016
Course Title: Metrics and models in
Software Quality Engineering
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E33
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives: This course will help students to achieve the following objectives:
Understand the basics of software quality engineering, including its benefits, related models and
standards, and quality team tools, Plan, implement and audit a Software Quality Management
program for their organization, Assist in defining and tailoring software engineering life cycles
and processes, Understand the basic software project management principles and techniques as
they relate to software project planning, tracking, control and risk management, Select, define,
and apply software measurement, metrics, and analytical techniques to their software products,
processes and services, Participate in peer reviews, and assist in the planning, implementation
and evaluation of software testing activities and Understand the fundamentals of the
configuration management process to include configuration identification, configuration control,
status accounting, and audits.
Topics:
Introduction, QA in Context and Quality Engineering, Testing Concepts, Issues and Techniques,
Test Activities, Management, and Automation, Coverage and Usage Testing Based on Checklists
and Partitions, Input Domain Partitioning and Boundary Testing, Coverage and Usage Testing
Based On Finite-State Machines and Markov Chains, Control Flow, Data Dependency, and
Interaction Testing, Testing Techniques: Adaptation, Specialization and Integration, Defect
Prevention and Process Improvement, Software Inspection, Formal Verification.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to:
discusses basic software project management principles and techniques as they relate to
software project planning, monitoring and control, and risk management. Attendees will learn
how to select, define and implement software metrics to understand, evaluate, control and
predict their software process, product and services. This course covers the basics of software
verification and validation planning with an emphasis on software peer reviews and software
testing techniques. The course ends with an overview of software configuration management,
including configuration identification, control, status accounting and auditing.
Detailed Syllabus:
Unit 1
Introduction: Meeting people’s quality expectations, Software Quality: Perspective and
Expectations, Quality frameworks and ISO, Correctness and defects, Historical perspective of
Quality, Classification: Quality Assurance as dealing with defects, Defect prevention, Defect
reduction, Defect Containment.
QA in Context and Quality Engineering: Handling discovered defects during QA activities, QA
Activates in Software Processes, Verification and Validation Perspectives, Reconciling the Two
Views. Quality Engineering: Activities and Process, Quality Planning: Goal Setting and Strategy
Formation, Quality Assessment and Improvement, Quality Engineering in Software Processes.
81 Unit 2
Testing Concepts, Issues and Techniques: Purpose, Activities, Processes and Context,
Questions about Testing, Functional vs Structural Testing, Coverage - based vs Usage - based
Testing.
Test Activities, Management, and Automation: Test Planning and Preparation, Test
Execution, Result Checking, and Measurement, Analysis and Follow-up, Activates, People, and
Management, Test Automation
Coverage and Usage Testing Based on Checklists and Partitions: Checklist-Based Testing
and its Limitations, Testing for partition Coverage, Usage-Based Statistical Testing with Musa’s
Operational Profiles, Constructing Operational Profiles, A case study.
Unit 3
Input Domain Partitioning and Boundary Testing: Input Domain Partitioning and Testing,
ISimple domain analysis and the Extreme Point Combination Strategy, Testing strategies based
on boundary analysis, Other Boundary Test Strategies and Applications.
Coverage and Usage Testing Based On Finite-State Machines and Markov Chains: FiniteState machines and testing, FSM testing: State and transition coverage, A Case study, Markov
chains and unified Markov models for testing, Using UMMs for Usage-Baseed statistical testing,
Case study continued.
Unit 4
Control Flow, Data Dependency, and Interaction Testing: Basic Control Flow Testing, Loop
Testing, CFT Usage, and Other Issues, Data Dependency and Data flow Testing, DFT: Coverage
and Applications.
Testing Techniques: Adaptation, Specialization and Integration: Testing Sub-Phases and
Applicable Testing Techniques, Specialized Test Tasks and Techniques, Test Integration, Case
Study: Hierarchical Web Testing.
Unit 5
Defect Prevention and Process Improvement: Basic concepts and Generic Approaches, Root
cause Analysis for Defect Prevention, Education and training for defect prevention, Other
Techniques for Defect Prevention, Focusing on Software Processes.
Software Inspection: Basic concepts and Generic Process, Fagan inspection, Other Inspections
and Related Activities, Defect Detection Techniques, Tool / Process Support, and
Effectiveness.
Formal Verification: Basic Concepts: Formal Verification and Formal Specification, Formal
Verification: Axiomatic Approach, Other approaches, Applications, effectiveness and integration
issues.
Text Book:
1. Stephan H. Kan: “Metrics and Models in Software Quality Engineering”, 2nd Edition,
Pearson Education, 2012.
Reference Book:
1. Jeff Tian: “Software Quality Engineering: Testing, Quality Assurance, and Quantifiable
Improvement”, 1st edition, John Wiley and Sons Inc., 2006.
82 Elective
Year: 2014-2016
Course Title: Soft Computing
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E35
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives: To become able to apply
Genetic Algorithms and Artificial Neural Networks as computational tools to solve a variety of
problems in their area of interest ranging from Optimization problems to Pattern recognition and
control tasks.
Detailed Syllabus:
Unit 1
Neural Networks: History, overview of biological Neuro-system, Mathematical Models of
Neurons, ANN architecture, Learning rules, Learning Paradigms-Supervised, Unsupervised and
reinforcement Learning, ANN training Algorithm sperceptions, Training rules, Delta, Back Propagation
Algorithm, Multilayer Perceptron Model, Hopfield Networks, Associative Memories, Applications of Artificial
Neural Networks.
Unit 2
Fuzzy Logic: Introduction to Fuzzy Logic, Classical and Fuzzy Sets: Overview of Classical
Sets, Membership Function, Fuzzy rule generation.
Unit 3
Operations on Fuzzy Sets, Fuzzy Arithmetic, Fuzzy Logic, Uncertainty based Information:
Compliment, Intersections, Unions, Combinations of Operations, Aggregation Operations. Fuzzy Numbers,
Linguistic Variables, Arithmetic Operations on Intervals & Numbers, Lattice of Fuzzy Numbers, Fuzzy
Equations. Classical Logic, Multivalued Logics, Fuzzy Propositions, Fuzzy Qualifiers, Linguistic Hedges.
Information & Uncertainty, Nonspecificity of Fuzzy & Crisp Sets, Fuzziness of Fuzzy Sets.
Unit 4
Introduction of Neuro-Fuzzy Systems: Architecture of Neuro Fuzzy Networks, Applications of Fuzzy
Logic: Medicine, Economics etc.
Unit 5
Genetic Algorithms: An Overview, GA in problem solving, Implementation of GA.
Text Books:
1.
Anderson J.A.: “An Introduction to Neural Networks”, 1st edition, PHI, 2012.
2.
Hertz J. Krogh, R.G. Palmer: “Introduction to the Theory of Neural Computation”, Addison-Wesley,
1991.
3. G.J. Klir & B. Yuan: “Fuzzy Sets & Fuzzy Logic Theory & applications”, 1st edition,PHI, 2012.
4. Melanie Mitchell: “An Introduction to Genetic Algorithm”, 1st edition, PHI, 1998.
Reference Books:
2.
Tettamanzi, Andrea, Tomassini, and Marco: “ Soft Computing: Integrating Evolutionary, Neural and
GFuzzy Systems”, Springer, 2001.
3.
Naresh K. Sinha and madan M gupta:” Soft Computing and Intelligent systems – Theory and
Application”, Academic press, 2000.
.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to:
computational techniques like Genetic/ Evolutionary algorithms, Artificial Neural Networks, Fuzzy
Systems, Machine learning and probabilistic reasoning etc. It also discusses Genetic Algorithms,
Artificial Neural Networks (major topologies and learning algorithms) and Fuzzy Logic.
83 Elective
Year: 2014-2016
Course Title: VLSI Design and Algorithms
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E37
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives: Study the fundamental
structures of VLSI Systems at the lowest levels of system abstraction, namely those associated
with the direct application of VLSI devices to particular problems of interest. VLSI design is
concerned with the set of principles governing MOS (metal oxide semiconductor) devices and
their behaviors. The CMOS transistors (n-channel and p-channel) and the ways in which we can
use them to create the most basic structure—the digital switch and to build a range of VLSI
structures from this switch, including NAND/NOR gates, Multiplexers, Latches and Registers.
Topics:
Introduction to Digital systems and VLSI, Fabrication and Devices, Sequential Machines,
Subsystem Design, Architecture Design, Simulations
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: Be able
to use mathematical methods and circuit analysis models in analysis of CMOS digital electronics
circuits, including logic components and their interconnect, Be able to create models of
moderately sized CMOS circuits that realize specified digital functions, Be able to apply CMOS
technology-specific layout rules in the placement and routing of transistors and interconnect,
and to verify the functionality, timing, power, and parasitic effects, Have an understanding of
the characteristics of CMOS circuit construction and the comparison between state-of-the-art
CMOS 2.5 micron process and emerging nanometer-scale electronic circuit technologies and
processes and Be able to complete a significant VLSI design project having a set of objective
criteria and design constraints.
Detailed Syllabus:
Unit 1
Introduction to Digital systems and VLSI: Why Design Integrated Circuits? Integrated
Circuits manufacturing, Integrated Circuit Design Techniques; IP-Based Design.
Fabrication and Devices: Introduction; Fabrication processes; Fabrication theory and
practice; Reliability.
Unit 2
Sequential Machines: Introduction; Latches and Flip-flops; Sequential systems and clocking
disciplines; Performance analysis; Clock generators;
Sequential systems design, Power
optimization, Design validation, Sequential testing.
Unit 3
Subsystem Design: Introduction; Combinational shifters; Adders; ALUs; Multipliers; Highdensity memory; Image sensors; FPGAs; PLA; Buses and networks on chips; Data paths;
Subsystems as IP.
84 Unit 4
Architecture Design: Introduction; Hardware description languages; Register Transfer design;
Pipelining; High-level synthesis; Architecture for low power; GALS systems; Architecture
testing; IP components; Design methodologies; Multiprocessor system-on-Chip design.
Unit 5
Simulations: General remarks; Gate-level modeling and simulations; Switch-level modeling
and simulation.
Text Books:
1. Wayne Wolf: Modern VLSI design IP Based Design, 4th edition, Pearson Education, 2011.
2. Sabih H Gerez: Algorithms for VLSI Design Automation, 2nd edition, Wiley India, 2011.
Reference Books:
1. Naveed Shervani: Algorithms for VLSI Physical Design Automation, Kluwer Academic
Publisher, 3rd Edition, 2010.
2. Christophher Meinel, Thorsten Theobold: Algorithm and Data Structures for VLSI Design,
KAP, 2002.
Elective
Year: 2014-2016
Course Title: Analysis of Computer
Networks
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E38
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives :
1. Understand the fundamentals of social networks
2. Identify the relationship between groups of peers over social networks
3. Analyze various components of social networks
4. Understand the importance in measuring the components of social networks.
5. Use the social network components measured for social benefit
Unit 1
Fundamentals: Social Structure : Sociometry and Sociogram, Exploratory Social Network Analysis,
Assembling a Social Network. Attributes and Relations: The World System, Partitions, Reduction of a
Network, Vectors and Coordinates, Network Analysis and Statistics
Unit 2
Cohesion:Cohesive Subgroups: Density and Degree, Components, Cores, Cores. Sentiments and
Friendship: Balance Theory, Detecting Structural Balance and Clusterability, Development in Time.
Affiliations: Two-Mode and One-Mode Networks, m-Slices, The Third Dimension
Unit 3
Brokerage: Center and Periphery: Distance, Betweenness. Brokers and Bridges: Bridges and BiComponents, Ego-Networks and Constraint, Affiliations and Brokerage Roles. Diffusion: Contagion,Exposure
and Thresholds, Critical Mass.
Unit 4
Ranking:Prestige: Popularity and Indegree, Correlation, Domains, Proximity Prestige. Ranking: Triadic
Analysis, Acyclic Networks, Symmetric-Acyclic Decomposition, Genealogies and Citations: Genealogy of the
Ragusan Nobility, Family Trees, Family Trees, Citations among Papers on Network Centrality, Citations
Unit 5
Roles :Blockmodels: Matrices and Permutation, Roles and Positions: Equivalence, Blockmodeling. Pajek:
Creating Network Files for Pajek, Exporting Visualizations
85 Text Books:
1.
2.
3.
Exploratory social Network Analysis with Pajek, WOUTER DE NOOY, ANDREJ MRVAR, VLADIMIR BATAGELJ,
Cambridge University press, 2005
Introduction to social network methods, Hanneman, Robert A. and Mark Riddle. 2005. University of California,
Riverside ( published in digital form at http://faculty.ucr.edu/~hanneman/ )
Social Network Analysis for Startups, Maksim Tsvetovat and Alexander Kouznetsov, Oreilly,2011
Mapping Course Outcomes with Program Outcomes:
Program Outcomes
Course Outcomes
PO
(a)
PO
(b)
PO
(c)
CO1
×
CO2
×
×
×
CO3
×
×
×
CO4
×
CO5
×
PO
(d)
PO
(e)
PO
(f)
×
×
×
PO
(g)
PO
(h)
PO(
i)
×
×
×
PO
(j)
PO
(k)
PO
(l)
×
×
PO
(m)
×
×
×
×
×
×
×
×
×
×
×
×
×
Course Assessment and Evaluation:
Indirect
Assessment
Methods
Direct
Assessment
Methods
What
Internal
Assessment
Tests
Project and
lab Exercise
evaluation
CIE
To
Whom
Students
Semester End
Examination
SEE
When/ Where
(Frequency in
the course)
Thrice (Average
of the best two
will be computed)
Summation of lab
and project
demonstration
End of Course
(Answering
5 of 10 questions)
Students
End of the course
End of Course
Survey
At the end of the course students should be able to:
Understand the fundamentals of social networks
Able to measure the relationship between groups of peers over networks
Demonstrate the measuring of social networks components
Recognize the importance of the components of social networks.
Identify the social benefit by measuring the social network components.
86 Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1-5
20
Soft copies
of the work
1-5
100
Answer
scripts
1-5
-
Questionnair
e
3-5, Relevance
of the course
Students
Feedback
Course Outcomes:
1.
2.
3.
4.
5.
Max
Marks
Elective
Year: 2014-2016
Course Title: Advances in Storage Area
Networks
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E42
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives: ability for aligning the
appropriate technologies to specific business problems, ability to evaluate design alternatives
according to standard practice, specifications, performance analysis, an ability for preparing a
guidelines for aligning technical solutions with the business objectives of data availability and
preservation, an ability to propose solutions for the support of business processes with the aid of
storage networks, will be able to design data networks with built –in redundancy and high
availability and will be able to design a storage model and storage networks.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to:
Understand computer systems, especially server Centric and storage centric IT architecture, the
memory hierarchy and its impact on performance; access to stored information via file systems,
and access to other computer systems via networks, Be able to compare large and small internal
hard disks that is, active and passive I/O channels ,virtualization by RAID and All levels of
RAIDs, Understand the details of data flow, internal working of fiber channel transmission,
protocols, latency, communication between arbitrated loop and fabric, connection via SCSI and
be able to develop a program to test the architecture provision intended operations, Be able to
Design, implement, test, debug, and document storage network problems, Understand the
techniques that might be useful in designing and implementing the different architectures and
Understand the problems of consistent and reliable storage networks production and the goals
of architecture.
Detailed Syllabus:
Unit 1
Introduction: Server Centric IT Architecture and its Limitations; Storage – Centric IT
Architecture and its advantages. Case study: Replacing a server with Storage Networks The
Data Storage and Data Access problem; The Battle for size and access.
Intelligent Disk Subsystems: Architecture of Intelligent Disk Subsystems; Hard disks and
Internal I/O Channels; JBOD, Storage virtualization using RAID and different RAID levels;
Caching: Acceleration of Hard Disk Access; Intelligent disk subsystems, Availability of disk
subsystems.
Unit 2
I/O Techniques: The Physical I/O path from the CPU to the Storage System; SCSI; Fibre
Channel Protocol Stack; Fibre Channel SAN; IP Storage.
Unit 3
Network Attached Storage: The NAS Architecture, The NAS hardware Architecture, The NAS
Sotfware Architecture, Network connectivity, NAS as a storage system.
File System and NAS: Local File Systems; Network file Systems and file servers; Shared Disk
file systems; Comparison of fibre Channel and NAS.
Unit 4
Storage Virtualization: Definition of Storage virtualization ; Implementation Considerations;
Storage virtualization on Block or file level; Storage virtualization on various levels of the
storage Network; Symmetric and Asymmetric storage virtualization in the Network.
Unit 5
87 SAN Architecture and Hardware devices: Overview, Creating a Network for storage; SAN
Hardware devices; The fibre channel switch; Host Bus Adaptors; Putting the storage in SAN;
Fabric operation from a Hardware perspective.
Software Components of SAN: The switch’s Operating system; Device Drivers; Supporting
the switch’s components; Configuration options for SANs.
Management: Planning Business Continuity; Managing availability; Managing Serviceability;
Capacity planning; Security considerations.
Text Book:
1. Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, 1st
edition, Wiley India, 2012.
Reference Books:
1. Marc Farley: Storage Networking Fundamentals – An Introduction to Storage Devices,
Subsystems, Applications, Management, and File Systems, Cisco Press, 2005.
2. Robert Spalding: “Storage Networks The Complete Reference”, 1st edition, Tata McGrawHill, 2011.
3. Richard Barker and Paul Massiglia: “Storage Area Network Essentials A CompleteGuide to
understanding and Implementing SANs”, 1st edition, John Wiley India, 2011.
Elective
Year: 2014-2016
Course Title: GPU Programming using
CUDA
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E43
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Prerequisites: NIL
Course Objectives
The objectives of this course are to
• Provide an understanding Graphical Processing Units and their architecture.
• Analyze the features GPUs and their functionalities
• Provide understanding of using GPUs as accelerators
• Design parallel applications using CUDA-C
• Analyze parallel algorithms implemented on heterogeneous computing environments with
sequential versions
Course Contents:
Unit 1
Introduction: GPUs as Parallel Computers, Architecture of a Model GPU, Why More Speed or
Parallelism? Parallel Programming Languages and Models, Overarching Goals.
History of GPU Computing: Evolution of Graphics Pipelines, GPU Computing.
Introduction to CUDA: Data Parallelism, CUDA Program Structure, A Matrix-Matrix
Multiplication Example, Device Memories and Data Transfer, Kernel Functions and Threading.
Unit 2
CUDA Threads: CUDA Thread Organization, Using blockIdx and threadIdx, Synchronization and
Transparent Scalability, Thread Assignment, Thread Scheduling and Latency Tolerance.
CUDA Memories: Importance of Memory Access Efficiency, CUDA Device Memory Types, A
Strategy for Reducing Global Memory Traffic, Memory as a limiting Factor to Parallelism.
Performance Considerations: More on Thread Execution, Global Memory Bandwidth, Dynamic
Partitioning of SM Resources, Data Perfecting, Instruction Mix, Thread Granularity, Measured
Performance and Summary.
88 Unit 3
Floating Point Considerations: Floating Point Format, Representable Numbers, Special Bit
Patterns and Precision, Arithmetic Accuracy and Rounding, Algorithm Considerations.
Parallel Programming and Computational Thinking: Goals of Parallel Programming,
Problem Decomposition, Algorithm Selection, Computational Thinking.
Unit 4
Introduction to OPENCL: Background, Data Parallelism Model, Device Architecture, Kernel
Functions, Device Management and Kernel Launch, Electrostatic Potential Map in OpenCL.
Goals of Programming GPUs, Memory Architecture Evolution, Kernel Execution Control Evolution,
Core Performance, Programming Environment
Unit 5
Application Case Study - Advanced MRI Reconstruction: Application Background, Iterative
Reconstruction, Computing FHd, Final Evaluation.
Application Case Study – Molecular Visualization and Analysis: Application Background, A
Simple Kernel Implementation, Instruction Execution Efficiency, Memory Coalescing, Additional
Performance Comparisons, Using Multiple GPUs.
Text Book:
1. David B Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors – A Handson Approach”, First Edition, Elsevier and nvidia Publishers, 2010.
Reference Books:
1.
Kai Hwang and Naresh Jotwani “Advanced Computer Architecture – Parallelism,
Scalability, and Programmability, Second Edition, TMH, 2011.
2.
Mattson, Sanders, Massingill: Patterns for Parallel Programming, Addison Wesley,2005,
ISBN0-321-22811-1.
Course Delivery
The course will be delivered through lectures, presentations, classroom discussions, practice
exercises and practical sessions. The course is basically learnt using Project based Learning
Method.
Course Assessment and evaluation:
Indirect
Assessment
Methods
Direct
Assessment
Methods
What
CIE
SEE
Internal
Assessment
Tests
Project and
lab Exercise
evaluation
To
Whom
Students
Semester End
Examination
When/ Where
(Frequency in
the course)
Thrice (Average
of the best two
will be computed)
Summation of lab
and project
demonstration
End of Course
(Answering
5 of 10 questions)
Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1-5
20
Soft copies
of the work
1-5
100
Answer
scripts
1-5
-
Questionnair
e
3-5, Relevance
of the course
Students
Feedback
Students
End of the course
End of Course
Survey
89 Max
Marks
Course Outcomes
At the end of the course students should be able to:
1. Identify the advantages and need of GPUs as an emerging technology
2. Design the programs using CUDA-C/OPENCL
3. Demonstrate Heterogeneous Computing on CPUs and GPUs
4. Analyze the speedup of programs on GPUs when compared to CPUs
5. Illustrate the usage of different programming abstractions using CUDA-C on GPUs
Mapping Course Outcomes with Program Outcomes:
Program Outcomes
Course Outcomes
Identify the advantages and
need
of
GPUs
as
an
emerging technology
Design the programs using
CUDA-C/OPENCL
Demonstrate Heterogeneous
Computing on CPUs and
GPUs
Analyze the speedup of
programs on GPUs when
compared to CPUs
Illustrate
the usage of
different
programming
abstractions using CUDA-C
on GPUs
PO
(a)
PO
(b)
PO
(c)
PO
(d)
PO
(e)
PO
(f)
PO
(g)
PO
(h)
PO(i)
PO
(j)
X
PO
(k)
PO
(l)
PO
(m)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Elective
Year: 2014-2016
Course Title: Information Retrieval
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E44
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Prerequisites: There are no prerequisites to this course.
Course Objectives: The objectives of this course are to
• Present the differences between data and information retrieval systems and different
classical IR models
• Present methods to evaluate the performance of information retrieval systems and
introduce different querying languages and protocols.
• Provide the concepts of query operations and the use of metadata and markup
languages.
• Present the various text operations, indexing and search techniques, and the basics of
parallel and distributed IR.
• Discuss the concepts of user interfaces for IR applications and web search techniques.
Course Contents:
Unit 1
Introduction: Motivation, Basic concepts, Past, present, and future, The retrieval process.
Modeling: Introduction, A taxonomy of information retrieval models, Retrieval: Adhoc and
filtering, A formal characterization of IR models, Classic information retrieval, Alternative set
90 theoretic models, Alternative algebraic models, Alternative probabilistic models, Structured text
retrieval models, Models for browsing.
Unit 2
Retrieval Evaluation: Introduction, Retrieval performance evaluation, Reference collections.
Query Languages: Introduction, keyword-based querying, Pattern matching, Structural
queries, Query protocols.
Unit 3
Query Operations: Introduction, User relevance feedback, Automatic local analysis, Automatic
global analysis.
Text and Multimedia Languages and Properties: Introduction, Metadata, Text, Markup
languages, Multimedia.
Unit 4
Text Operations: Introduction, Document preprocessing, Document clustering, Text
compression, Comparing text compression techniques.
Indexing and Searching: Introduction; Inverted Files; Other indices for text; Boolean
queries; Sequential searching; Pattern matching; Structural queries; Compression.
Parallel and Distributed IR: Introduction, Parallel IR, Distributed IR.
Unit 5
User Interfaces and Visualization: Introduction, Human-Computer interaction, The
information access process, Starting pints, Query specification, Context, Using relevance
judgments, Interface support for the search process.
Searching the Web: Introduction, Challenges, Characterizing the web, Search engines,
Browsing, Metasearchers, Finding the needle in the haystack, Searching using hyperlinks.
Text Book:
1. Ricardo Baeza-Yates, Berthier Ribeiro-Neto: Modern Information Retrieval, 1st edition,
Pearson, 2011.
2.
Reference Books:
1. David A. Grossman, Ophir Frieder: Information Retrieval Algorithms and Heuristics, 2nd
Edition, Springer, 2009.
2. William B. Frakes, Ricardo Baeza-Yates (Editors): Information Retrieval Data Structures
and Algorithms, 1st edition, Prentice Hall PTR, 2009.
Course Delivery: The course will be delivered through lectures, presentations, classroom
discussions, and practical implementations. Questions for CIE and SEE are designed in
accordance with the Bloom’s taxonomy.
91 Course Assessment and Evaluation Scheme:
To
Whom
Indirec
t
Assess
ment
Method
s
Direct
Assessment
Methods
What
CIE
SEE
Internal
Assessment
Tests
Project and
lab Exercise
evaluation
Students
Semester End
Examination
Students
Feedback
Students
End of Course
Survey
When/ Where
(Frequency in
the course)
Thrice (Average
of the best two
will be computed)
Summation of lab
and project
demonstration
End of Course
(Answering
5 of 10 questions)
Max
Marks
Evidence
Collected
Contribution
to Course
Outcomes
30
Blue Books
1-5
20
Soft copies
of the work
1-5
100
Answer
scripts
1-5
-
Questionnair
e
3-5, Relevance
of the course
End of the course
Course Outcomes :
At the end of the course the students should be able to:
1. Distinguish between data and information retrieval systems and explain different classical
IR models
2. Evaluate the performance of information retrieval systems and use different querying
languages and protocols.
3. Perform query operations and recognize the use of metadata and markup languages.
4. Explain various text operations, indexing and search techniques, and the basics of
parallel and distributed IR.
5. Discuss the concepts of user interfaces for IR applications and web search techniques.
Program Outcomes
Course Outcomes
Distinguish between data
and
information
retrieval
systems
and
explain
different classical IR models
Evaluate the performance of
information retrieval systems
and use different querying
languages and protocols
Perform query operations
and recognize the use of
metadata
and
markup
languages
Explain
various
text
operations,
indexing
and
search techniques, and the
basics
of
parallel
and
distributed IR
Discuss the concepts of user
interfaces for IR applications
and web search techniques.
PO
(a)
PO
(b)
PO
(c)
PO
(d)
PO
(e)
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
92 PO
(f)
PO
(g)
PO
(h)
PO(i)
X
X
X
X
X
PO
(j)
PO
(k)
PO
(l)
X
X
X
X
X
X
X
X
PO
(m)
Elective
Year: 2014-2016
Course Title: Topics in Software Testing
Credits (L:T:P:SS) : 3:0:0
Type of course: Lecture/ Laboratory/
/Project/ assignment
Course Code: MCNE E45
Core/ Elective:
Total Contact Hours: 3 Hrs per week
Course Objectives:
This course will help students to achieve the following objectives: Select, with justification, an
appropriate set of tools to support the development of a range of software products, Analyze
and evaluate a set of tools in a given area of software development, Demonstrate the capability
to use a range of software tools in support of the development of a software product of medium
size, Identify the principal issues associated with software evolution and explain their impact on
the software life cycle, Discuss the challenges of maintaining legacy systems and the need for
reverse engineering, Outline the process of regression testing and its role in release
management, Estimate the impact of a change request to an existing product of medium size,
Develop a plan for re-engineering a medium-sized product in response to a change request,
Discuss the advantages and disadvantages of software reuse. Exploit opportunities for software
reuse in a given context and Identify weaknesses in a given simple design, and highlight how
they can be removed through refactoring.
Topics:
Basics of software testing and examples, decision table-based testing, data flow testing, levels of
testing, integration, system, interaction, OO, class, OO integration, GUI, OO system,
exploratory, model-based testing and test-driven development.
Course Outcomes:
This course uses assigned readings, lectures, and homework to enable the students to: design
and identify the suitable capabilities to use a range of software tools in support of the
development of a software product of medium size, identify the principal issues associated with
software evolution and analyze it, explain the impact on the software life cycle by different
testing tools, challenges of maintaining legacy systems and the need for reverse engineering
and outline the process of regression testing and its role in release management.
Detailed Syllabus:
Unit 1
Basics of Software Testing and Examples: Basic definitions, Test cases, Insights from a
Venn diagram, Identifying test cases, Error and fault taxonomies, Levels of testing. Examples:
Generalized pseudocode, The triangle problem, The NextDate function, The commission
problem, The SATM (Simple Automatic Teller Machine) problem.
Decision Table-Based Testing: Decision tables, Test cases for the triangle problem, Test
cases for the NextDate function, Test cases for the commission problem, Guidelines and
observations.
Data Flow Testing: Definition-Use testing, Slice-based testing, Guidelines and observations.
Unit 2
Levels of Testing: Traditional view of testing levels, Alternative life-cycle models, The SATM
system, Separating integration and system testing.
Integration Testing: A closer look at the SATM system, Decomposition-based, call graphbased, Path-based integrations, Case study.
93 System Testing: Threads, Basic concepts for requirements specification, Finding threads,
Structural strategies and functional strategies for thread testing, SATM test threads, System
testing guidelines, ASF (Atomic System Functions) testing example.
Unit 3
Interaction Testing: Context of interaction, A taxonomy of interactions, Interaction,
composition, and determinism, Client/Server Testing.
Issues in Object-Oriented Testing: Units for object-oriented testing, Implications of
composition and encapsulation, inheritance, and polymorphism, Levels of object-oriented
testing, GUI testing, Dataflow testing for object-oriented software, Examples.
Class Testing: Methods as units, Classes as units.
Unit 4
Object-Oriented Integration Testing: UML support for integration testing, MM-paths for
object-oriented software, A framework for object-oriented dataflow integration testing.
GUI Testing: The currency conversion program, Unit testing, Integration Testing and System
testing for the currency conversion program.
Object-Oriented System Testing: Currency converter UML description, UML-based system
testing, Statechart-based system testing.
Exploratory Testing: The context-driven school, Exploring exploratory testing, Exploring a
familiar example, Exploratory and context-driven testing observations.
Unit 5
Model-Based Testing: Testing based on models, Appropriate models, Use case-based testing,
Commercial tool support for model-based testing.
Test-Driven Development: Test-then-code cycles, Automated test execution, Java and JUnit
example, Remaining questions, Pros, cons, and open questions of TDD, Retrospective on MDD
versus TDD.
A Closer Look at All Pairs Testing: The all-pairs technique, A closer look at NIST study,
Appropriate applications for all pairs testing, Recommendations for all pairs testing.
Software Testing Excellence: Craftsmanship, Best practice of software testing, Top 10 best
practices for software testing excellence, Mapping best practices to diverse projects.
Text Book:
1. Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 3rd Edition, Auerbach
Publications, 2012.
Reference Books:
1. Aditya P Mathur: Foundations of Software Testing, Pearson, 2008.
2. Mauro Pezze, Michal Young: Software Testing and Analysis – Process, Principles and
Techniques, 1st edition, John Wiley & Sons, 2011.
3. Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices, 1st
Edition, Pearson, 2012.
4. Brian Marrick: The Craft of Software Testing, 1st edition, Pearson, 2012.
94 M. S. Ramaiah Institute of Technology
(Autonomous Institute, Affiliated to VTU)
BANGALORE-560054
Department of Computer Network Engineering Guidelines for M.Tech (CNE) Project Phase I and II
Course Details Subject Code /Name: MCSE 312 / Project Phase 1
Subject Code/ Name: MCSE 401/Project Phase-2
Max CIE marks: 100
Credits: 10
Credits: 23
Max SEE marks: 100
•
Project can be done in-house or externally in any reputed industry/institution.
•
Students are instructed to meet their Guides at the department every week.
Component
Timeline
Assessment
Project phase-I
End of 3rd semester-12th week
Interim Progress Assessment
Project phase-II
Mid of 4th semester -8th week
Design Development and
Solution
End of 4th semester -13th week
Written Report
Evaluation-1
Project phase-II
Evaluation-2
Final Presentation
Guidelines for External Projects •
•
The company/organization has to give an acceptance letter permitting the students to
carry out their project work in their premises prior to the conduction (Third Semester
Project Phase I).
•
Students are free to carry out their project work in the Company premises, but should
meet their guides at the department every week for updates.
•
Demonstration of the project is a must at the College during the final Viva Voce.
•
A project guide (external guide) shall be allotted by the company/organization for
supervising the progress of the project work (Third Semester Project Phase I).
•
An internal guide will be allotted by the department for every external project also. In
consultation with the external guide, the internal guide must be permitted to oversee
the progress of the project work at the company premises at suitable times.
•
Students carrying out external projects will have to submit synopsis / functional
specification of the project work duly signed by the external guide before the deadline
(Third Semester Project Phase I).
The project guides are responsible for approving the progress of their respective wards.
95 •
In case the project guide requires a committee to approve their status of the work, he/she can
convey the same to the project coordinator at regular intervals. An expert committee will be
then formed to review the work on request from the project guide. The committee will finalize
the work by end of the semester.
•
Students are required to maintain a project diary wherein all the student-guide meetings must
be recorded.
•
The Project Phase II will be reviewed in two stages.
a. Project Phase II- Evaluation-I: to be done after six (6) weeks from the start of the
semester. The evaluation should be done by the guide and two co-examiners. The
students are required to submit the work on the System Design, Detailed Design and
further course of action of their work.
b. Project Phase II- Evaluation-II: The same team that conducted the Evaluation-I must
do the Evaluation- II after twelve (12) weeks from the start of the semester. Students
must complete the implementation and testing by this time. They are required to
demonstrate the implementation, testing & results. All projects must be demonstrated
at the CSE Department.
c. The faculty should also visit the Industry (in case it is an external project) to interact
with the mentors so as to monitor the progress of their respective students.
d. A draft version of the complete project report must also be submitted at the end of
twelve (12) weeks.
•
The CIE marks will be cumulative sum of the two evaluations done (40:60) for a total of 100
marks.
•
The student should prepare a consolidated report in IEEE Format and should submit it for
possible publication in National/International Conferences/Journals before the submission of
the Thesis
•
A final project viva-voce will be conducted at the end of the semester (SEE) with an internal
examiner and an external examiner.
Typical Project Activities and Timelines The project activities include
1.
2.
3.
4.
Deciding the project subject area (Third Semester Project Phase I).
Establishing relevance and importance of the project (Third Semester Project Phase I).
Identifying requirements (Third Semester Project Phase I).
Feasibility study and freezing of project scope/objectives (Third Semester Project Phase
I).
5. General design inputs: requirements analysis (Third Semester Project Phase I).
6. System and subsystem level design: design(Fourth Semester Project Phase II)
96 7. Convert all designs into programs: implementation(Fourth Semester Project Phase II)
8. Performance evaluation at system/subsystem level testing (Fourth Semester Project Phase II)
9. Documentation of all above activities into a project report (Third Semester Project Phase I &
Fourth Semester Project Phase II)
Suggested Timelines for Activities and Deliverables
1. Start time + 6 weeks
Write very clearly scope/objective set for the project. The objectives must reflect as to what exactly is
proposed to implement. Freeze the title and scope/ objectives and this will not change under normal
situation.
System design: Understand the overall system functioning, identify and draw a system level block
schematic identifying all identified subsystems and their input/output need.
Prepare a list of
hardware systems, computing and network environment. Similarly identify the software – operating
systems, application software, case tools, simulators, databases, etc. Highlight what is already
available and what will be newly created or required for the project.
Deliverable: System design document
2. Start time + 8 weeks
Detailed Design: Design from the conceptual level block schematic, a detailed architectural layout,
indicate every subsystem and within that identify input/output and design for every small entity.
Draw functional block schematics, data flow diagrams for every small entity and subsystem.
Formulate a test plan: list the test data at the inputs, type of tests to be performed during development
and making of subsystems, and tests required during runtime or execution.
Deliverable: Detailed design document
3. Start time + 10 weeks
Write the algorithms, the pseudo code for every function call, the subroutines and the recursions.
Implement the design; execute the programs step by step for each module. Debug, evaluate the
performance and validate the design. Integrate all modules/subsystems to realize the over system.
Perform all system level tests, evaluate the results and compare the project scope/objects and the
requirements.
Deliverable: Implementation and testing document, demo of working code
4. Start time + 13 weeks
Complete all documentation and make the project report ready for submission.
Deliverable: Complete project report
97 The Students need to also consolidate their work into a IEEE and publish the same in a reputed
Journal or an International Conference.
At the end of the 13th week, The PG students can attend the Best Project Selection Process
which will be conducted at the department of Computer Science and Engineering, MSRIT.
The Criteria for the same would be Quality of the Project, Its relevance to the present
technology, Research contribution, relevant papers published, etc.
One Project will be selected from the Batch as the Best Project.
GUIDELINES FOR THE PREPARATION OF PG IV Semester (Project Phase‐ II) PROJECT REPORTS 1. Project reports should be typed neatly only on one side of the paper with 1.5 or double line
spacing on a A4 size paper. The margins should be: Left - 1.25", Right - 1", Top and Bottom 0.75".
2. The total number of reports to be prepared is 5 (4+1)
• Four (4) Copies to be submitted to the department
• One (1) copy to the student.
3. Before taking the final printout, the approval of the concerned guide(s) is mandatory and suggested
corrections, if any, must be incorporated.
4. The organization of the report should be as follows
•
Inner title page
•
Certificate from the guide and department
•
Declaration by candidate
•
Abstract or Synopsis
•
Acknowledgments
•
Table of Contents
• List of table & figures (optional)
All the above pages Starting from abstract must be numbered in roman ( i, ii,
iii, iv, v, …)
• Chapters ( page numbers in Arabic i.e. 1, 2, 3)
o Main body of the report divided appropriately into chapters, sections and subsections.
•
References or bibliography
98 5.
Numbering : The chapters, sections and subsections may be numbered in the decimal form for
e.g. Chapter 2, sections as 2.1, 2.2 etc., and subsections as 2.2.3, 2.5.1 etc.
6.
Fonts sizes:
a. The chapter number must be left or right justified (font size 16).
b. Followed by the title of chapter centered (font size 18),
c. Section/subsection numbers along with their headings must be left justified with
i. Section number and its heading in font size 16
ii. Subsection and its heading in font size 14.
iii. The body or the text of the report should have font size 12.
7. The figures and tables must be numbered chapter wise for e.g.: Fig. 2.1 Block diagram of a serial
binary adder, Table 3.1 Primitive flow table, etc. Figure captions must be placed below the figure
and centred. Table captions must be above the table and centred.
8. Reference or Bibliography: The references should be numbered serially in the order of their
occurrence in the text and their numbers should be indicated within square brackets for e.g. [3].
The section on references should list them in serial order in the following format.
For textbooks –
[1] A.V. Oppenheim and R.W. Schafer, Digital Signal Processing, Englewood, N.J., Prentice
Hall, 3 Edition, 1975.
For papers –
[2] Devid, Insulation design to combat pollution problem, Proc of IEEE, PAS, Vol 71, Aug
1981, pp 1901-1907.
Bibliography need not be numbered and need not be cited.
9.
Only SI units are to be used in the report. Important equations must be numbered in decimal
form for e.g.
V = IR
..........
(3.2)
All equation numbers should be right justified
10. The project report should be clearly written and include descriptions of work carried out by
others only to the minimum extent necessary. Verbatim reproduction of material available
elsewhere should be strictly avoided. If short excerpts from published work are desired to be
included, they should be within quotation marks and appropriately referenced.
99 11. Proper attention is to be paid not only to the technical contents but also to the organization and
clarity of the report. Due care should be taken to avoid spelling and typing errors. The student
should note that report-write-up forms the important component in the overall evaluation of the
project.
12. The colour of the cover page for Computer Science PG projects is CREAM.
100