Session Input
Unit-V
Session Name
:
Applications
Course Title
:
CS2403 -Digital Signal Processing
Semester
:
VII Semester
Programme Name
:
B.E
Author Name
:
Mrs.V.GANDHIMATHI
Department
:
ECE
Institution Name
:
VRS College of Engg. & Tech
Mobile Number
:
9245582632
E-mail
:
adhihema02@gmail.com
Session -1
1. Introduction: Multirate signal processing
Suggested Activity: Introduces
https://www.google.co.in/?gws_rd=cr&ei=DExFUubNN4WHrgeDkICwBg#q=multirate+signal+processing
2. Decimation and interpolation
Suggested Activity: chalk and talk
http://users.abo.fi/htoivone/courses/sbappl/asp_chapter2.pdf
3.Conclusion: Recall by keywords
a. D
b. I
c. Up sampling
d. Down sampling
Multirate Digital Signal
Processing
Basic Sampling Rate Alteration Devices
• Up-sampler - Used to increase the sampling
rate by an integer factor
• Down-sampler - Used to decrease the
sampling rate by an integer factor
1
Copyright © 2001, S. K. Mitra
Down-Sampler
• Figure below shows explicitly the timedimensions for the down-sampler
x[ n]  xa ( nT )
Input sampling frequency
FT 
10
1
T
M
y[ n]  xa ( nMT )
Output sampling frequency
F
1
FT'  T 
M T'
Copyright © 2001, S. K. Mitra
Down-Sampler
• Figure below shows the down-sampling by
a factor of 3 of a sinusoidal sequence of
frequency 0.042 Hz obtained using Program
10_2
Input Sequence
1
0.5
Amplitude
Amplitude
0.5
0
-0.5
-1
Output sequence down-sampled by 3
1
0
-0.5
0
10
20
30
Time index n
40
50
-1
0
10
8
20
30
Time index n
40
50
Copyright © 2001, S. K. Mitra
Up-Sampler
• Figure below shows explicitly the timedimensions for the up-sampler
x[ n]  xa ( nT )
L
y[n]
 x ( nT / L ), n 0,  L , 2 L ,
 a
0
otherwise

Input sampling frequency
FT 
11
1
T
Output sampling frequency
1
FT'  LFT 
T'
Copyright © 2001, S. K. Mitra
Up-Sampler
• Figure below shows the up-sampling by a
factor of 3 of a sinusoidal sequence with a
frequency of 0.12 Hz obtained using
Program 10_1
Input Sequence
1
0.5
Amplitude
Amplitude
0.5
0
-0.5
-1
4
Output sequence up-sampled by 3
1
0
-0.5
0
10
20
30
Time index n
40
50
-1
0
10
20
30
Time index n
40
50
Copyright © 2001, S. K. Mitra
Session -2
1. Introduction: phase shifters
Suggested Activity: Discussion
http://www.qsl.net/va3iul/Phase_Shifters/Phase_Shifters.pdf
2. Design of phase shifters
Suggested Activity: PPT
http://en.wikipedia.org/w/index.php?search=phaseshifters&button=&title=Special%3ASearch
3. Conclusion: Rapid fire
a. What are phase shifters?
b. Give applications of phase shifters?
c. Purpose of poly phase structure.
Session -3
1. Introduction: digital filters
Suggested Activity: Introduces
2. Implementation of digital filter banks
Suggested Activity: writing board
3.Sub band coding of speech signals and quadrature mirror filters
http://www.compandent.com/
Suggested Activity: PPT
Sub-Band Coding
Digital signal
Analog signal
ADC
Transmitter
channel
DAC
Analog signal
Receiver
4.Conclusion: Questions and answers
a. Define sub band coding
b. What is digital filter bank?
c. Explain quadrature mirror filter.
Session -4
1. Introduction: ADC
Suggested Activity: Recall by words
Words:
a. Sampling.
b. Sampling theorem
c. Quantization
d. Continuous time signal
http://www.mds.com/products/s042?gclid=CIGf-I-htLoCFWFS4godrDQAJQ
2. Transmultiplexers and AD/DA conversions
Suggested Activity: PPT
Transmultiplexer
FDM to TDM Conversion:
SSB
Demodulator
FDM
TDM Signals
Decimator
O/p1
ADC
Signal
TDM Signals
SSB
Demodulator
Decimator
SSB
Demodulator
Decimator
O/p2
TDM Signals O/p3
TDM-to- FDM Conversion
TDM
signal
Inter
polator
SSB
Modulator
Inter
polator
SSB
Modulator
DAC
FDM
signal
Inter
polator
SSB
Modulator
3.Speech compression
Suggested Activity: PPT
4.Conclusion: Questions
a. Define ADC.
b. State sampling theorem.
c. Give the purpose of transmultiplexers.
d. What is speech compression?
Session -5
1. Introduction: Adaptive filters
Suggested Activity: PPT
http://www.eas.asu.edu/~dsp/grad/anand/java/AdaptiveFilter/Zero.html
Applications
•
Parameters
– u=input of adaptive filter=output to plant
– y=output of adaptive filter
– d=desired response=delayed system input
– e=d-y=estimation error
2. Wiener filters , LMS and RLS algorithm
Suggested Activity: PPT and board
•The LMS Algorithm consists of two basic processes
– Filtering process
• Calculate the output of FIR filter by convolving input and taps
• Calculate estimation error by comparing the output to desired signal
– Adaptation process
• Adjust tap weights based on the estimation error
http://www.eas.asu.edu/~dsp/grad/anand/java/ANC/ANC.html
3.Conclusion: Recall and list by key words
Words:
a. Adaptive filter.
b. LMS
c. RLS
d. FIR
e. IIR
Session -6
1. Introduction: Musical sound processing
www.eetindia.co.in/
Suggested Activity: Analogy
2. Audio processing
Suggested Activity: Presentation
3.Conclusion: Recall by writing
Sound production
Echoes
Audio sound
Session -7
1. Introduction: Image enhancement
http://www.r-s-c-c.org/node/225
Suggested Activity: Discussion & questionnaires
a. What is image?
b. What are the features of image?
2. Histogram equalization
The histogram of a digital image with gray levels in the range [0, L-1] is a discrete
function h(rk)=nk , where rk is the kth gray level and nk is the number of pixels in the
image having gray level rk. It is common practice to normalize a histogram by dividing each of its
values by the total number of pixels in the image, denoted by the product MN.
Thus, a normalized histogram is given by h(rk)=nk/MN
Suggested Activity: Presentation
 Let rk, k[0..L-1] be intensity levels and let p(rk) be its normalized histogram function.
 The intensity transformation function for histogram equalization is
k
s k  T (rk )  ( L  1) p r ( r j )
j 0

L 1 k
 n j , k  0,1,2,..., L  1
MN j 0
3. Conclusion: Brain storming
a. What is image enhancement?
b. Define histogram processing.
c. What do you mean by equalization?
Session -8
1. Introduction: Median
Suggested Activity: Introduces and questions
2. Geometric, Harmonic and Contra median:
http://www.maxvalue.com/tip104.htm
Suggested Activity: Chalk and talk
Consecutive terms of a geometric sequence are proportional
Consider the geometric sequence with a common factor 10.
4
40
=
40
400
A
B
D
3. Conclusion: Rapid fire
a. Define median.
b. What is geometric median?
c. Define Harmonic median.
d. What do you by contra median?
C
Session -9
1. Recap: filtering
Suggested Activity: Brain storming
http://cilab.knu.ac.kr/seminar/Seminar/2010/20101009%20Colour%20Image%20Enhancement%20by%
20Virtual%20Histogram%20Approach.pdf
2. Color Image enhancement
4
of
19
Neighbourhood Operations
For each pixel in the origin image, the
outcome is written on the same location at
the target image.
Origin
Origin
x
(x, y)
Neighbourhood
y
Target
Image f (x, y)
Suggested Activity: PPT
3.DSP processor
Suggested –Activity: Demonstration
4. Conclusion: Tit for tat
8
of
19
Smoothing Spatial Filters
One of the simplest spatial filtering
operations we can perform is a smoothing
operation
– Simply average all of the pixels in a
neighbourhood around a central value
– Especially useful
1/
1/
1/
9
9
9
in removing noise
Simple
from images
1/
1/
1/
averaging
9
9
9
– Also useful for
filter
highlighting gross
1/
1/
1/
9
9
9
detail