A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts
A signal is a pattern of variation that carry information.
Signals are represented mathematically as a function of one or more independent variable
basic concept of signals
types of signals
system concepts
It contains the introduction, modulation, demodulation, phasor diagram, constellation diagram,time-domain diagram, signal space diagram, power spectral diagram, spectral diagram, bandwidth, spectral efficiency, uses, advantages, and disadvantages.
The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
Digital Signal Processing[ECEG-3171]-Ch1_L02Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced
#Africa#Ethiopia
In this video, I will explain what is QAM modulation and what is 16QAM.
QAM Stands for Quadrature Amplitude Modulation. QAM is both an analog and a digital modulation method. But here, we are only talking about QAM as a digital modulation.
Quadrature means that two carrier waves are being used, one sine wave and one cosine wave. These two waves are out of phase with each other by 90°, this is called quadrature.
At the receiving end, the sine and cosine wave can be decoded independently, this means that by using both a sine wave and a cosine wave, the communication channel's capacity is doubled comparing to using only one sine or one cosine wave. That is why quadrature is such a popular technique for digital modulation.
QAM modulation is a combination of Amplitude Shift Keying and Phase Shift Keying, both carrier wave is modulated by changing both its amplitude and phase. As shown in this 8QAM waveform, the top is the sine wave carrier, for bit 000, the sin wave has a phase shift of 0°, and an amplitude of 2. While for bit 110, the phase shift is 180°, and the amplitude now is 1. So both phase and amplitude are changed.
In 16QAM, the input binary data is combined into groups of 4 bits called QUADBITS.
As shown in this picture, the I and I' bits are sent to the sine wave modulation path, and the Q and Q' bits are sent to the cosine wave path. Since the bits are split and sent in parallel, so the symbol rate has been reduced to a quarter of the input binary bit rate. If the input binary data rate is 100 Gbps, then the symbol rate is reduced to only 25 Gbaud/second. This is the reason why 16QAM is under hot research for 100Gbps fiber optic communication.
The I and Q bits control the carrier wave's phase shift, if the bit is 0, then the phase shift is 180°, if the bit is 1, then the phase shift is 0°.
The I' and Q' bits control the carrier wave's amplitude, if bit is 0, then the amplitude is 0.22 volt, if the bit is 1, then the amplitude is 0.821 volt.
So each pair of bits has 4 different outputs. Then they are added up at the linear summer. 4X4 is 16, so there is a total of 16 different combinations at the output, that is why this is called 16QAM.
This illustration shows an example of how the QUADBIT 0000 is modulated onto the carrier waves.
Here I and I' is 00, so the output is -0.22 Volt at the 2-to-4-level converter, when timed with the sine wave carrier, we get -0.22sin(2πfct), here fc is the carrier wave's frequency. QQ' is also 00, so the other carrier wave output is -0.22cos(2πfct).
Here is the proof that quadbit 0000 is modulated as a sine wave with an amplitude of 0.311volt and a phase shift of -135°. You can now pause for a moment to study the proof.
This list shows the 16QAM modulation output with different amplitude and phase change for all 16 quadbits. On the right side is the constellation diagram which shows the positions of these quadbits on a I-Q diagram.
You can visit FO4SALE.com f
Signal and System, CT Signal DT Signal, Signal Processing(amplitude and time ...Waqas Afzal
Signal and System(definitions)
Continuous-Time Signal
Discrete-Time Signal
Signal Processing
Basic Elements of Signal Processing
Classification of Signals
Basic Signal Operations(amplitude and time scaling)
It contains the introduction, modulation, demodulation, phasor diagram, constellation diagram,time-domain diagram, signal space diagram, power spectral diagram, spectral diagram, bandwidth, spectral efficiency, uses, advantages, and disadvantages.
The presentation covers sampling theorem, ideal sampling, flat top sampling, natural sampling, reconstruction of signals from samples, aliasing effect, zero order hold, upsampling, downsampling, and discrete time processing of continuous time signals.
Digital Signal Processing[ECEG-3171]-Ch1_L02Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced
#Africa#Ethiopia
In this video, I will explain what is QAM modulation and what is 16QAM.
QAM Stands for Quadrature Amplitude Modulation. QAM is both an analog and a digital modulation method. But here, we are only talking about QAM as a digital modulation.
Quadrature means that two carrier waves are being used, one sine wave and one cosine wave. These two waves are out of phase with each other by 90°, this is called quadrature.
At the receiving end, the sine and cosine wave can be decoded independently, this means that by using both a sine wave and a cosine wave, the communication channel's capacity is doubled comparing to using only one sine or one cosine wave. That is why quadrature is such a popular technique for digital modulation.
QAM modulation is a combination of Amplitude Shift Keying and Phase Shift Keying, both carrier wave is modulated by changing both its amplitude and phase. As shown in this 8QAM waveform, the top is the sine wave carrier, for bit 000, the sin wave has a phase shift of 0°, and an amplitude of 2. While for bit 110, the phase shift is 180°, and the amplitude now is 1. So both phase and amplitude are changed.
In 16QAM, the input binary data is combined into groups of 4 bits called QUADBITS.
As shown in this picture, the I and I' bits are sent to the sine wave modulation path, and the Q and Q' bits are sent to the cosine wave path. Since the bits are split and sent in parallel, so the symbol rate has been reduced to a quarter of the input binary bit rate. If the input binary data rate is 100 Gbps, then the symbol rate is reduced to only 25 Gbaud/second. This is the reason why 16QAM is under hot research for 100Gbps fiber optic communication.
The I and Q bits control the carrier wave's phase shift, if the bit is 0, then the phase shift is 180°, if the bit is 1, then the phase shift is 0°.
The I' and Q' bits control the carrier wave's amplitude, if bit is 0, then the amplitude is 0.22 volt, if the bit is 1, then the amplitude is 0.821 volt.
So each pair of bits has 4 different outputs. Then they are added up at the linear summer. 4X4 is 16, so there is a total of 16 different combinations at the output, that is why this is called 16QAM.
This illustration shows an example of how the QUADBIT 0000 is modulated onto the carrier waves.
Here I and I' is 00, so the output is -0.22 Volt at the 2-to-4-level converter, when timed with the sine wave carrier, we get -0.22sin(2πfct), here fc is the carrier wave's frequency. QQ' is also 00, so the other carrier wave output is -0.22cos(2πfct).
Here is the proof that quadbit 0000 is modulated as a sine wave with an amplitude of 0.311volt and a phase shift of -135°. You can now pause for a moment to study the proof.
This list shows the 16QAM modulation output with different amplitude and phase change for all 16 quadbits. On the right side is the constellation diagram which shows the positions of these quadbits on a I-Q diagram.
You can visit FO4SALE.com f
Signal and System, CT Signal DT Signal, Signal Processing(amplitude and time ...Waqas Afzal
Signal and System(definitions)
Continuous-Time Signal
Discrete-Time Signal
Signal Processing
Basic Elements of Signal Processing
Classification of Signals
Basic Signal Operations(amplitude and time scaling)
Digital Signal Processing[ECEG-3171]-Ch1_L05Rediet Moges
This Digital Signal Processing Lecture material is the property of the author (Rediet M.) . It is not for publication,nor is it to be sold or reproduced.
#Africa#Ethiopia
Entropy
- Measure of the average uncertainty in the random variable
It is equal to the number of bits on average required to describe the random variable.
Note: Average length of a random variable lies between H(X) and H(X) + 1, where H(X) is the entropy of the random variable X.
Relative Entropy
- Measure of the distance between two distributions
It is the measure of the inefficiency of assuming the distribution is q when true distribution is p.
Mutual Information
- Measure of the amount of information that one random variable contains about another random variable
It is the reduction in the uncertainty of one random variable because of the knowledge of the other
TASK: Obtain the proofs of the equations in the attached figure if you are new to information theory
Reference: Thomas M. Cover and Joy A. Thomas, "Elements of Information Theory", 2nd Edition. (I recommend this book to beginners).
This presentation referenced from different sources such as Huawei, Ericsson, Nokia, IEEE, among others was delivered during the IEEE Kenya Symposium 2018 at Riara University. The presentation was to give an update of 5G technology and demystify issues in 5G.
This document contains analogue filters design questions and solutions obtained from MATLAB.
Example shows bandpass filter, bandstop filter and highpass filter design.
Solving these problems equips one with basic skill needed to understand MATLAB. After a full understanding of the fundamentals, one can proceed to advanced application of MATLAB and use it to solve scientific and engineering problems.
This presentation was delivered to appreciate the importance of our cultural diversity in Kenya for strengthening our unity. It was delivered to university students, lectures and other players in government and non-governmental institutions at Multimedia University of Kenya.
Our diversity is our strength. Tribalism and negative ethnicity are discouraged.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
DIGITAL SIGNAL PROCESSING: Sampling and Reconstruction on MATLAB
1. MULTIMEDIA UNIVERSITY OF KENYA
FACULTY OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF ELECTRICAL AND COMMUNICATION ENGINEERING
(ECE)
BSC. TELECOMMUNICATION AND INFORMATION ENGINEERING
NAME: MARTIN WACHIYE WAFULA
REG. NO: ENG-211-075/2012
UNIT CODE: ETI 2505
UNIT NAME: DIGITAL SIGNAL PROCESSING (DSP)
LECTURER: MR. JOSIAH MAKICHE
Date of Submission: 5th
October,2016
TITLE: MATLAB ASSIGNMENT 1 - SAMPLING AND
RECONSTRUCTION OF ANALOG SIGNALS
2. Objectives
1. To study the sampling principle and the effect of sampling on the frequency-domain
quantities
2. To study several approaches of reconstruction
Introduction
DSP provides alternative method for processing analog signals. In DSP applications, real world
analog signals are converted into discrete signals using sampling and quantization operations
called analog-to-digital conversion or ADC. These discrete signals are processed by the digital
signal processors, and the processed output signals are converted into analog signal using a
reconstruction operation called digital-to-analog conversion or DAC.
Fig1: Block diagram of a digital signal processing system
The relationship between an analog signal and its discrete time sampled version is necessary to
understand the operation of DSP system. This relationship in time domain is given by:
x(n) = xa (nTs)
where Ts is the sampling interval. This relation can also be shown in the frequency domain using
the Fourier analysis.
The continuous time Fourier transform is given by
dtetxFX Ftj
aa
2
)()( .
The inverse continuous time Fourier transform is given by
dFeFXtx Ftj
aa
2
)()( .
The discrete time Fourier transform is given by
n
fnj
enxfX 2
)()( .
The inverse discrete time Fourier transform is given by
dFeFXtx Ftj
aa
2
)()( .
The relation between Xa(F) and X(f) is given by
k
sas FkfXFfX ))(()( .
3. where Fs is a sampling frequency = 1/Ts. In other words, X(f) consists of infinite numbers of
copies of scaled Xa(F) separated by frequency interval f = 1. From the relation between discrete
time and analog frequencies
sF
F
f
we get
k
sas
s
kFFXF
F
F
X )()(
in which )(
sF
F
X consists of infinite numbers of copies of scaled Xa(F) separated by interval F =
Fs.
Sampling Principle
To avoid aliasing, a band-limited signal xa(t) with bandwidth B can be reconstructed from
its sample values x(n) = xa(nTs) if the sampling frequency Fs = 1/Ts is greater than twice the
bandwidth B of xa(t).
BFs 2
Otherwise aliasing would result in x(n). The sampling rate of 2B for an analog band-limited
signal is called the Nyquist rate.
Reconstruction
From the sampling theorem and the above examples it is clear that if we sample band-
limited xa(t) above its Nyquist rate, then we can reconstruct xa(t) from its samples
x(n). Using an interpolation formula:
))((sinc)()( ss
n
a nTtFnxtx
where
x
x
x
)sin(
)(sinc is an interpolating function derived from an ideal low pass
reconstruction filter. However, since an ideal low pass reconstruction filter cannot be
implemented, we usually estimated the ideal low pass filter by the following methods:
Zero-order-hold (ZOH) interpolation: In this interpolation a given sample value is held
for the sample interval until the next sample is received.
( ) ( ), ( 1)a s sx t x n nT t n T
which can be obtained by filtering the impulse train through an interpolating filter of the
form
4. s
0
1 0 t<T
( )
0 otherwise
h t
which is a rectangular pulse. The resulting signal is a piecewise-constant (staircase)
waveform which requires an appropriately designed analog post-filter for accurate
waveform reconstruction.
x(n) xa(t) xa(t)
First-order-hold (FOH) interpolation: In this case the adjacent samples are joined by
straight lines. This can be obtained by filtering the impulse train through
s
1
1 0
( )
1 2
0 otherwise
s
s s
s
t
t T
T
h t t
T t T
T
Procedure
1. Sampling xa (t) at Fs = 50 samples/sec
ZOH Post-Filter
5. 1. %xa(t)= exp(-10|t|)
2. %CTFT is given by Xa(F) =
[(2*10)/(10^2 + (2*pi*F)^2)]
3. %sampling xa(t) at Fs = 50
samples/sec
4. clear all
5. tmin = -1;
6. tmax = 1;
7. %Analog signal
8. t = tmin: 0.001: tmax;
9. xa = exp(-10*abs(t));
10. %Sampling
rate(sample/second)
11. Fs = 50;
12. %Sample period
13. Ts = 1/Fs;
14. %Discrete time signal
15. n = tmin/Ts:tmax/Ts;
16. x = exp(-10*abs(n*Ts));
17. %Display signal in time
domain
18. figure(1)
19. subplot(211)
20. plot(t,xa)
21. title('Analog and
Discrete Time Signals')
22. xlabel('time(sec)')
23. ylabel('Analog Signal
x(t)')
24. subplot(212)
25. stem(n,x)
26. xlabel('n')
27. ylabel('Discrete time
signal x(n)')
28. %Computing FT
29. %Analog frequency (Hz)
30. F = -100:0.1:100;
31. W = (2*pi*F);
32. %DT Frequency
(circles/sample)
33. f = F/Fs;
34. w = 2*pi*f;
35. %Analog spectrum for
CTFT
36. XaF = 2.*(10./(10^2+
W.^2));
37. %DTFT
38. XF = x * exp(-1i*n'*w);
39. %Display spectra in
frequency domain
40. figure(2)
41. subplot(311)
42. plot(F,abs(XaF))
43. title('spectra of
signals')
44. xlabel('Freq(circle/sec
)')
45. ylabel('Original
Xa(F)')
46. subplot(312)
47. plot(F,abs(XF))
48. xlabel('Freq(circle/sec
)')
49. ylabel('X(F/Fs)')
50. subplot(313)
51. plot(f,abs(XF))
52. xlabel('Freq(circle/sec
)')
53. ylabel('X(f)')
54. %Display spectra in the
fundamental range
55. figure(3)
56. subplot(211)
57. plot(F,abs(XaF))
58. plot(F,abs(XF))
59. title('spectra in the
fundamental range')
60. xlabel('Freq(circle/sec
)')
61. ylabel('X(F/Fs)')
62. v = axis;
63. v(1:2) = [-Fs/2 Fs/2];
64. axis(v)
65. subplot(212)
66. plot(f,abs(XF))
67. xlabel('Freq(circle/sec
)')
68. ylabel('X(f)')
69. v = axis;
70. v(1:2) = [-1/2 1/2];
71. axis(v)
Experimental results
6. Explanation:
We have superimposed the discrete signal x(n) over x(t) to emphasize the sampling. The plot
shows that it is a scaled version (scaled by Fs = 50) of X(F). Clearly there is no aliasing.
Q2. Reconstruction of xa(t)
72. %Reconstruction of
xa(t)
73. t = tmin:0.001:tmax;
74. figure(4)
75. clf
76. subplot(211)
77. hold on
78. stem(n*Ts,x,'r')
79. for i= 1:size(x,2)
80. xsinc(i,:) =
x(i)*sinc(Fs*(t-(i+min(n)-
1)*Ts));
81. plot(t,xsinc(i,:))
82. end
83. title('Signal
reconstruction')
84. xlabel('time(second)')
85. ylabel('x(n)*Sinc(Fs*(t
-nTs))')
86. hold off
87. xar = sum(xsinc);
88. subplot(212)
89. plot(t,xar,'b-
',t,xa,'r:')
90.
91. legend('Reconstructed
signal','Origin signal')
92. ylabel('Reconstructed
signal xa(t)')
93.
94. xlabel('time (second)')
95. %reconstruction error
96. maxerror = max(abs(xa-
xar));
Experimental Results
7. Explanation:
The maximum error between the reconstructed and the actual analog signal is 0.0380. This is
because xa(t) is not strictly band-limited and we also have a finite number of samples. From the
reconstructed figure above, we note that visually the reconstruction is excellent.
Q3. For Fs = 10 samples/cycle, the results are as shown below:
8. Explanation:
Here Fs = 10 < 20. There is considerable amount of aliasing. From the figure above, this is
evident since shape of x(n) is different from that of Xa(F) and can be seen as adding overlapping
replicas of Xa(F).
Explanation:
The maximum error between the reconstructed and the actual analog signals is 0.1852, which is
significant and cannot be attributed to the nonband-limitedness of xa(t) alone. From Figure
above, the reconstructed signal differs from the actual one in many places over the interpolated
regions. This is the visual demonstration of aliasing in the time domain. (Proakis, 2012)
Reason why Fs = 50 samples/sec is better than Fs = 10 samples/sec:
- Since the bandwidth of xa(t) = 20Hz, the Nyquist rate Fo = 40 Hz according to the
Sampling theorem. To prevent aliasing, the analog signal should be sampled at a rate
higher than twice the highest frequency in the analog signal.
- Therefore, for Fs = 50 Hz > Fo there is no aliasing hence better compared Fs = 10
samples/sec < Fo . (Manolakis, 1996)
Q4. xa(t) = sin(20πt), 0≤ t ≤ 1. Samples at Ts = 0.01, 0.03, 0.05, 0.07 and 0.1
a) For each Ts, x(n) was plotted.
MATLAB Code
1. tmin = 0;
2. tmax = 1;
3. %Analog signal
4. t = tmin: 0.001: tmax;
5. xa = sin(20*pi*t);
6. %Sample period
7. Ts1 = .01;
8. %Discrete time signal
9. n1 = tmin/Ts1:tmax/Ts1;
10. x1 = sin(20*pi*n1*Ts1);
11. %Display signal in time
domain
12. figure(5)
13. subplot(211)
14. plot(t,xa)
9. 15. title('Analog and
Discrete Time Signals')
16. xlabel('time(sec)')
17. ylabel('Analog Signal
x(t)')
18. subplot(212)
19. stem(n1,x)
20. xlabel('n1')
21. ylabel('Discrete time
signal x1(n)')
22. Ts2 = .03;
23. %Discrete time signal
24. n2 = tmin/Ts2:tmax/Ts2;
25. x2 = sin(20*pi*n2*Ts2);
26. %Display signal in time
domain
27. figure(6)
28. subplot(211)
29. plot(t,xa)
30. title('Analog and
Discrete Time Signals')
31. xlabel('time(sec)')
32. ylabel('Analog Signal
x(t)')
33. subplot(212)
34. stem(n2,x2)
35. xlabel('n2')
36. ylabel('Discrete time
signal x2(n)')
37. Ts3 = .05;
38. %Discrete time signal
39. n3 = tmin/Ts3:tmax/Ts3;
40. x3 = sin(20*pi*n3*Ts3);
41. %Display signal in time
domain
42. figure(7)
43. subplot(211)
44. plot(t,xa)
45. title('Analog and
Discrete Time Signals')
46. xlabel('time(sec)')
47. ylabel('Analog Signal
x(t)')
48. subplot(212)
49. stem(n3,x3)
50. xlabel('n3')
51. ylabel('Discrete time
signal x3(n)')
52. Ts4 = .07;
53. %Discrete time signal
54. n4 = tmin/Ts4:tmax/Ts4;
55. x4 = sin(20*pi*n4*Ts4);
56. %Display signal in time
domain
57. figure(8)
58. subplot(211)
59. plot(t,xa)
60. title('Analog and
Discrete Time Signals')
61. xlabel('time(sec)')
62. ylabel('Analog Signal
x(t)')
63. subplot(212)
64. stem(n4,x4)
65. xlabel('n4')
66. ylabel('Discrete time
signal x4(n)')
67. Ts5 = .1;
68. %Discrete time signal
69. n5 = tmin/Ts5:tmax/Ts5;
70. x5 = sin(20*pi*n5*Ts5);
71. %Display signal in time
domain
72. figure(9)
73. subplot(211)
74. plot(t,xa)
75. title('Analog and
Discrete Time Signals')
76. xlabel('time(sec)')
77. ylabel('Analog Signal
x(t)')
78. subplot(212)
79. stem(n5,x5)
80. xlabel('n5')
81. ylabel('Discrete time
signal x5(n)')
10. For Ts = 0.01, figure 5, Ts = 0.03 figure 6, Ts = 0.05 figure 7, Ts = 0.07 figure 8 and Ts = 0.1
figure 9 were plotted.
(b) Reconstruction of ya(t) code (Proakis, 2012)
11. (c) Results
The maximum error between the reconstructed and the actual analog signal is 0.0013. This is
because xa(t) is not strictly band-limited and there is a finite number of samples. From the
reconstructed figure above, we note that visually the reconstruction is excellent.
QUESTIONS
1. What is the MATLAB function that would be used to plot a staircase (ZOH) interpolation
of the analog signal?
stairs(n * Ts * frequency, x);
hold on
stem(n* Ts * frequency, x);
hold off
(Proakis, 2012)
2. What is the MATLAB function that would be used to plot a staircase (ZOH) interpolation
of the analog signal?
plot(n * Ts * frequency, x);
hold on
stem(n* Ts * frequency, x);
hold off
(Proakis, 2012)
3. From the experiment, describe why minimum sampling rate must be at least twice the
bandwidth of an analog signal?
12. To avoid aliasing
Explanation of the function of each part of DSP system above:
ADC - The output of the A /D converter is a digital signal that is appropriate as an input to the
digital processor. ADC coverts continuous time signal into discrete time signals through
sampling and quantization
The digital signal processor- may be a large programmable digital computer or a small
microprocessor programmed to perform the desired operations on the input signal. It may also be
a hard wired digital processor configured to perform a specified set of operations on the input
signal.
DAC – converts the digital output of the processor into analog signal. This is called
reconstruction.(Manolakis, 1996)
References
Manolakis, J. G. (1996). Digital Signal Processing - Principles, Algorithms and Applications (Third ed.).
New Jersey, USA: Prentice Hall International-Inc.
Proakis, V. K. (2012). DIGITAL SIGNAL PROCESSING USING MATLAB (3rd ed.). Stamford, USA: Cengage
Learning.