Title of the Project
By
<Name of the Student>
<Roll No. of the Student>
[Name of the Department]
Birla Institute of Technology, Mesra
Ranchi, Jharkhand-835215
i
APPROVAL OF THE MENTOR
Recommended that the B. Tech. Minor Project entitled <Title of the report>
submitted by <Roll No. and Name of the student> is approved by me for
submission. This should be accepted as fulfilling the partial requirements for the
award of Degree of Bachelor of Technology in <Branch Name>. To the best of
my knowledge, the content of this report does not form a basis for the award of
any previous degree to anyone else.
Date:
<Name of the mentor>
<Designation of the mentor>
Department of Electronics & Communication Engineering
Birla Institute of Technology, Mesra
ii
DECLARATION CERTIFICATE
I certify that,
a) The work contained in the report is original and has been done by
myself under the general supervision of my supervisor.
b) The work has not been submitted to any other Institute for any
other degree or diploma.
c) I have followed the guidelines provided by the Institute in writing
the report.
d) I have conformed to the norms and guidelines given in the Ethical
Code of Conduct of the Institute.
e) Whenever I have used materials (data, theoretical analysis, and
text) from other sources, I have given due credit to them by citing
them in the text of the report and giving their details in the
references.
f) Whenever I have quoted written materials from other sources, I
have put them under quotation marks and given due credit to the
sources by citing them and giving required details in the
references.
Date:
<Name of the Student>
<Roll No. of the Student>
Department of Electronics & Communication Engineering
Birla Institute of Technology, Mesra
iii
CERTIFICATE OF APPROVAL
This is to certify that the work embodied in this Summer Internship Report
entitled:
“
”,
is carried out by <Name of the Student (Roll Number)> has been approved for
the degree of Bachelor of Technology in <Branch> of Birla Institute of
Technology, Mesra, Ranchi.
Date:
Place:
(Chairman)
Head of the Department
Dept. of ECE
(Panel Coordinator)
Examiner
Dept. of ECE
iv
ABSTRACT
We have implemented a water collection device using valves and stepper motor which goes to
v
ACKNOWLEDGEMENT
This section of a project report is used to express your gratitude to the persons or institutions
who significantly contributed during the project or research and helped you to accomplish
your project. In this section you may mention your Guide, Head of the Department, Teachers
of the department, Parents, Classmates, Lab staff, the department, institute etc. The maximum
word limit of this section is 300.
Date:
(Signature)
Name of the Student
Roll No. of the Student
vi
CONTENTS
ABSTRACT ..................................................................................................................... i
ACKNOWLEDGEMENT .............................................................................................. ii
LIST OF FIGURES ........................................................................................................ iii
LIST OF TABLES ..........................................................................................................iv
CHAPTER 1 <NAME OF THE CHAPTER> .............................................................. 1
<SECTION NO. (e.g., 1.1)> ....................................................................................................................... 1
<SUBSECTION NO. (e.g., 1.1.4)> .................................................................................................... 3
CHAPTER 2 <NAME OF THE CHAPTER> ............................................................. 10
The maximum number of Chapters should be Five.
1. Introduction: Overview of the project
2. Literature Review: Background of the project
3. Methodology: Description of the methods or algorithms applied.
4. Experimental Results and Discussion: Discussion and analysis of results.
5.
Conclusion: Summary of the work accomplished and future scopes.
APPENDIX A ACCEPTED / COMMUNICATED PAPERS .................................... 34
REFERENCES ............................................................................................................... 35
vii
LIST OF FIGURES
Figure 1.1
Caption or Label of the Figure
5
Figure 2.1
Caption or Label of the Figure
8
viii
LIST OF TABLES
Table 2.1
Label of the Table No. 2.1
10
Table 3.2
Label of the Table No. 3.2
15
ix
CHAPTER 2
LITERATURE REVIEW
This section offers a summary of recent literature that explores methods, technical elements and
significant advancements made by researchers striving to enhance accuracy in sampling process.
Francesco et al (2012) tackles the challenge of collecting deep water samples with small, portable
autonomous surface vehicles (ASVs). They designed a lightweight, low-power system with a
winch that lowers a probe 50 meters deep. This probe can take measurements and collect water
samples at various depths. It avoids contamination by using special materials and is designed to
be easily installed on these compact ASVs. The paper discusses initial tests of this system on a
HydroNet class robotic catamaran. Zhang et al(2019) proposes a new approach: using
autonomous underwater vehicles (AUVs) equipped with clever algorithms. These brainy AUVs
can actually find and track interesting events like algal blooms as they move with the currents.
This targeted approach allows the AUVs to take measurements and collect water samples right in
the heart of the action, making them much more efficient than traditional methods. The paper
highlights successful examples of these AUVs being used in real-world studies and discusses
exciting possibilities for the future. Koparan et al(2018) explores using a drone (UAV) with a
custom water sampler for autonomous water quality monitoring. This tackles limitations of
manual sampling, like safety risks and limited access, especially after disasters. The drone carried
the sampler and had special features to survive water landings. Field tests compared dronecollected samples to traditional methods. Results showed minimal differences for most
measurements. Overall, drone-based water sampling appears promising for faster, safer
monitoring of large or hard-to-reach water bodies. Zhang et al(2021) suggests that water quality
monitoring is difficult due to the complexity, frequency, and dangers involved in collecting
samples across various water environments. This paper introduces a new solution: an autonomous
water sampling system built on an unmanned surface vehicle (USV). This USV carries water
sampling equipment and can travel to designated spots, collecting samples safely and efficiently.
The paper will explore the design of this system, its operations, and a real-world example of its
use. This new approach promises to be cost-effective, safer for personnel, and allows data
collection in hard-to-reach or risky areas, ultimately leading to better water quality monitoring.
Zhang et al (2023) presents that freshwater monitoring with traditional USVs falls short. They
can't handle the frequent, multi-depth, and multi-point sampling needed. This paper proposes a
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solution: a multi-dimensional water sampling USV (WS-USV). The WS-USV is built for these
demanding tasks. It has both navigation and water sampling systems, allowing it to collect
samples automatically from various depths and pre-set locations. Tests show the WS-USV can
navigate to different sampling points and collect water samples entirely by itself, making it a
promising tool for freshwater monitoring. Madeo et al(2020) This article presents a novel
unmanned surface vehicle (USV) named WeMo designed for cost-effective water quality
monitoring in diverse aquatic environments. The emphasis lies on affordability to facilitate
widespread deployment by various stakeholders, including local communities, administrations,
and even private entities. WeMo utilizes commercially available components and boasts a
modular sensor array capable of measuring a range of chemical and physical water quality
parameters. Additionally, bathymetric measurements are achievable. The envisioned application
involves establishing a network of these USVs, essentially creating a "social sensor network" for
comprehensive water quality monitoring. Furthermore, the system incorporates data analytics
tools for both automated navigation and data processing. The article demonstrates successful
application of this model to assess oxygen concentration. Moreover, the system has the capability
to reconstruct water quality data for unmonitored locations along the USV's trajectory. In essence,
this research contributes to the development of a comprehensive monitoring ecosystem
encompassing data collection, storage, and analysis. This low-cost USV system holds promise for
facilitating widespread and affordable water quality monitoring efforts.
Summarizing the Literature Review
. These advancements promote safer data collection from hard-to-reach or hazardous areas,
ultimately leading to more comprehensive and effective water quality monitoring programs.
This report summarizes recent research efforts aimed at improving the accuracy and efficiency
of water sample collection. Traditional methods often pose safety risks and limitations in
accessing remote or hazardous locations. The following advancements in autonomous water
sampling techniques are noteworthy:
1)
Deployment of Autonomous Surface Vehicles (ASVs): Francesco et al (2012)
developed a lightweight, low-power system utilizing compact ASVs for deep water
sampling. This method offers a cost-effective and contamination-free approach.
2)
Intelligent Autonomous Underwater Vehicles (AUVs): Koparan et al(2018)proposed
the use of AUVs equipped with sophisticated algorithms to locate and target specific
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events, such as algal blooms, for sample collection. This targeted approach significantly
enhances sampling efficiency.
3)
Drone-based Water Sampling: Zhang et al (2023) explored the use of drones equipped
with custom water samplers for autonomous water quality monitoring. This method
proves particularly advantageous in situations where manual sampling presents safety
risks or limited access, especially in the aftermath of disasters.
4)
Unmanned Surface Vehicles (USVs) for Diverse Environments: Zhang et al(2021)
introduced USVs specifically designed to carry water sampling equipment, enabling safe
and efficient sample collection across various aquatic environments. Madeo et al. (2020)
presented a cost-effective USV named WeMo, facilitating widespread deployment by
diverse stakeholders. These USVs demonstrate promise for broader water quality
monitoring applications.
5)
Multi-dimensional Water Sampling USVs (WS-USVs): Zhang et al (2023) addressed
limitations of conventional USVs by developing a WS-USV specifically tailored for
freshwater monitoring. This innovative USV possesses the capability to collect samples
from various depths and pre-designated locations, offering a significant advancement in
freshwater sample collection techniques.
In conclusion, the field of water quality monitoring is witnessing a shift towards automation,
improved efficiency, and cost-effectiveness in sample collection.
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Figure 1.1 Confusion Matrix
Text Mining is one of the recent research domains in the field of Machine Learning [1]. Itis
the process of transforming unstructured textual content into structured data for simple
analysis.
Table 2: Performance Comparison of ML Algorithms
Classifiers
Precision
Recall
Accuracy
KNN
71.42
66.34
90.23
LR
79.42
62.34
91.23
SVM
80.30
72.40
92.93
XGB
91.42
77.23
93.12
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Text Mining is one of the recent research domains in the field of Machine Learning. It is the
process of transforming unstructured textual content [3] into structured data for simple analysis.
Algorithm 2 Basic Genetic Algorithm
1. Generate random population Pr of n chromosomes.
2. Evaluate fitness function 𝑓𝑓(𝑥) for each chromosome 𝑥 in population 𝑃𝑟.
3. Select two parent chromosomes from the population Pr according to their fitness value.
4. Perform Crossover operation.
5. Mutate new offspring.
6. Place new offspring in the new population 𝑃𝑛𝑒𝑛𝑛
7. Determine the fitness value of 𝑃𝑛𝑒𝑛𝑛.
8. Repeat the steps 3-7 until the termination condition is satisfied.
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REFERENCES
1) Caissie, D. (2006). The thermal regime of rivers: a review. Freshwater Biology, 51(12),
1189-1206.
2) Kaur et al., 2018
3) LeBlanc, M., Iqbal, F., Beanlands, M., & LeBlanc, P. (1997). Variation in brook trout
(Salvelinus fontinalis) growth rate with latitude and temperature in Newfoundland streams.
Canadian Journal of Fisheries and Aquatic Sciences, 54(4), 983-991.
4) Mohseni, O., Stefan, H. G., & Erickson, T. R. (2003). Sensitivity of stream temperatures in
the United States to air temperature changes projected by global climate models. Journal of
Geophysical Research: Atmospheres, 108(D14), 4499.
5) Ojanguren, A. F., & Brañta, J. M. (2000). Swimming performance of brown trout at different
temperatures: rearing temperature influences the effects of acclimation temperature.
Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology, 126(2),
227-239.
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Additional Information
Style & Format
1. Use A4 page size (8.27 x 11.69 in./21 x 29.7 cm) with Normal Margins (Top: 2.54 cm,
Bottom: 2.54 cm, Left: 2.54 cm, Right: 2.54 cm) as in MS Word. All text including
footnotes and illustrations must appear within this area.
2. No paragraph indent is required. Use 1.5 line spacing for all text.
3. Use Times New Roman Font for all text. Bold Face or italic text can be used for
emphasis only.
Report Title
18 pt bold title case
Chapter Title
18 pt bold italic title case
Section Heading
16 pt bold title case
Subsection Heading
14 pt bold normal
Body text
12 pt normal, justified to both margins
Figure / Table Captions
12 pt normal
Footnote text
9 pt normal
4. Each chapter must start on a new page. See the format of the chapter on page no. 1.
5. Avoid inline equations in the sentences. Provide proper number to each equation.
6. As far as possible figures (particularly block diagrams, simple line drawings, simple
graphs) and all tables must be computer generated. Try to use your own images. Consult
your guide about correct use of e-resources.
7. The maximum limit is 30 pages for the body of the report (i.e., excluding cover page,
certificate page, abstract, contents and all other lists, acknowledgment, and annexure
etc.). Reverse printing (both sides) is encouraged to save paper.
8. Only two copies of the report need to be submitted. Tape (Blue) binding with
transparent plastic sheet is required.
9. Plagiarism checking is mandatory. Consult your guide for instructions on
plagiarism check according to the institute rule. Use iThenticate software to
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compute similarity index. The plagiarism report must be attached to the summer
internship report.
10. The marking scheme shall provide weightage to the presence of the following
components of the report.
a. Proper literature reviews.
b. An articulate algorithm of flow of process (code snippets, algorithms, DFDs
etc.).
c. Proper reporting of the results obtained.
d. Well annotated data set descriptions.
e. Clear and precisely labelled figures.
f. Topic specific references.
These guidelines are to be followed even if a student interns in a corporate organization.
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