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http://metalab.uniten.edu.my/~zainul/
This Home Page is for my students whoare taking the following Classes as below:-
1) Digital Signal Processing EEEB363
Section 3A/B. 2) Digital Signal Processing EEEB363
Section 4A/B.
Lecturer :-
Dato Prof. Dr. Ir Zainul Abidin Md Sharrif.
http://metalab.uniten.edu.my/~zainul/EEEB364_sect1.htmhttp://metalab.uniten.edu.my/~zainul/EEEB364_sect1.htmhttp://metalab.uniten.edu.my/~zainul/EEEB363_sect2.htmhttp://metalab.uniten.edu.my/~zainul/EEEB363_sect2.htmhttp://metalab.uniten.edu.my/~zainul/EEEB363_sect2.htmhttp://metalab.uniten.edu.my/~zainul/EEEB363_sect2.htmhttp://metalab.uniten.edu.my/~zainul/EEEB364_sect1.htmhttp://metalab.uniten.edu.my/~zainul/EEEB364_sect1.htm8/10/2019 dspl1a.pdf
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Course Code:- EEEB363
Course Title :- Digital Signal Processing
Prerequisites:- Signals and Systems (EEEB233)
Upon completion of the course, the student should have a solidfoundation in basic digital signal processing.
Aims/Objectives
To introduce the concepts, theory, techniques and applicationsassociated with the understanding of digital signal processing.
To develop methods for processing discrete-time signals.
To understand the processes of analog-to-digital and digital-to-analog conversion.
To understand the discrete Fourier transform , fast Fouriertransform, design and implementation of digital filters.
To be aware of some applications associated with digital signalprocessing.
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Course Description
Signal processing is a method of extracting informationfrom signal which in turn depends on the type of signal andthe nature of information it carries.
Therefore, signal processing is concerned with the
representing signals in mathematical terms and extractingthe information by carrying out algorithmic operations onthe signal.
A signal can be mathematically expressed in terms of basicfunctions in original domain of independent variable or it
can be expressed in terms of basic functions in transformeddomain.
In this course we will use tools available in both domains toanalyze signals and systems in discrete time domain.
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Upon completion of the course, students should be
able to do the following:
1 Compute the discrete- time convolution of two signals.
2. Use the concepts of linearity, time-invariance, causality, and stability to classify adiscrete-time system.
3. Evaluate the frequency response of a discrete-time, linear time-invariant (LTI)system from its impulse response and vice versa.
4. Understand and be able to apply the definition, properties, and applications of theDiscrete-time Fourier Transform (DTFT).
5. Explain and apply sampling theorem, analog to digital and digital to analog
conversion. Understand ideal sampling and reconstruction. 6. Design DSP systems for processing continuous-time signals.
7. Be able to apply definition and properties of Discrete Fourier Transform (DFT) andFast Fourier Transform (FFT).
8. Use DTFT, DFT, and FFT to analyze discrete time signals and systems.
9. Be able to use the definition and properties of Z-transform to describe, and analyze
the behavior of LTI systems, 10. Describe the input-output characteristics of a LTI system in both time domain and
frequency domain. Relate the poles and zeros of the system to its frequency response,phase response, and stability and causality properties.
11. Design and implement different frequency selective Finite Impulse Response(FIR), and Infinite Impulse Response (IIR) filters to meet frequency domainspecifications.
12. Describe engineering trade-offs in filter design. Understand linear and nonlinearphase response.
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course content and time
allocation 1.Signals and Signal Processing:- (6Hours)
1.1 Characterization and Classification of Signals 1.2 Typical Signal ProcessingOperations 1.3 Examples of Typical Signals 1.4 Typical Signal ProcessingApplications 1.5 Why Digital Signal Processing?
2.Discrete-Time Signals and Systems:- (4 Hours)
2.1 Discrete-Time Signals 2.2 Typical Sequences and Sequence Representation 2.4Discrete-Time Systems 2.5 Time-Domain Characterization of LTI Discrete-Time
Systems 2.9 Correlation of Signals. 3.Discrete-Time Fourier Transform:- (4 Hours)
3.1 The Continuous-Time Fourier Transform 3.2 The Discrete-Time FourierTransform 3.3 Discrete-Time Fourier Transform Theorems 3.5 Band-Limited Discrete-Time Signals 3.8 The Frequency Response of an LTI Discrete-Time System3.9 Phase andGroup Delays.
4.Digital Processing of Continuous-Time Signals:- (6 Hours)
4.1 Introduction4.2 Sampling of Continuous-Time Signals4.3 Sampling of BandpassSignals 4.4 Analog Lowpass Filter Design 4.5 Design of Analog Highpass, Bandpass, andBandstop Filters4.6 Anti-Aliasing Filter Design 4.10 Reconstruction Filter Design 6
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course content and time
allocation. continued. 5.Finite Length Discrete Transforms:- (6Hours)
5.2 The Discrete Fourier Transform 5.3 Relation Between the Fourier Transform and theDFT, and Their Inverses 5.6 DFT Symmetry Relations5.7 Discrete Fourier TransformTheorems 5.9 Computation of the DFT of Real Sequences11.3.2 Decimation in Time andDecimation in Frequency.
6.z-Transform:- (4Hours) -
6.1 Definition and Properties 6.2 Rational z-Transforms 6.3 Region of Convergence of a
Rational z-Transform 6.4 The Inverse z-Transform 6.5 z-Transform Properties 6.7 TheTransfer Function
7.LTI Discrete-Time Systems in the Transform Domain:- (4 Hours)
7.1 Transfer Function Classification Based on Magnitude Characteristics 7.2 TransferFunction Class ideation Based on Phase Characteristics 7.3 Types of linear-Phase TransferFunctions 7.6 Inverse Systems
8.Digital Filter Structures:- (2Hours)
8.1 Block Diagram Representation 8.3 Basic FIR Digital Filter Structures8.4 Basic IIR DigitalFilter Structures.
9.IIR Filter Design & FIR Filter Design:- (6 Hours)
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Course Outcomes
1. Compute the discrete- time convolution of two signals and classify the discrete time system andthe process of signals correlation
2. Evaluate the frequency response of a discrete-time, linear time-invariant (LTI) system from itsimpulse response and vice versa
.3. Apply the definition, properties of the Discrete-time Fourier Transform (DTFT) in signaltransformations.
4. Explain and apply sampling theorem, analog to digital, digital to analog conversions and signalreconstruction.
5. Determine the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) of discretesignal
6. Describe and analyze the behavior of an LTI system using the definition and properties of Z-transform.
7. Draw and describe the poles and zero plot according to input output characteristics of an LTIsystem and classify the stability and causality of an LTI system from plot
8. Design and implement different frequency selective Finite Impulse Response (FIR), and InfiniteImpulse Response (IIR) filters to meet frequency domain specifications.
9. Recognize the linear and nonlinear phase response of an LTI system. 10. Draw the basic structure of an LTI system from its input output characteristics and analyze the
input output of an LTI system from the basic structure
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Grading Policy:
Test 20%
Laboratory & Assignment 30%
Final: 50% Total: 100%
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Signal Processing
Digital SignalProcessing
Analog SignalProcessing
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Digital SignalProcessing
Digital audio signalprocessing
Digital controlengineering
Digital imageprocessing
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Digital SignalProcessing
Speech processing.RADAR Signal
processingCommunicationssignal processing
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What Is DSP?
a bit loudAnalog Computer
Digital Computer
ADC
DSP
DAC OUTPUT
1010 1001
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Introduction
Digital Signal Processing
Digital: converting and using of discrete signals to represent
information in the form of numbers
Signal: a variable parameter that convey information.
Processing: to perform operations on the numbers according to
programmed instructions
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A Typical DSP System
DSP Chip
Memory
Converters (Optional)
Analog to Digital
Digital to Analog
Communication Ports Serial
Parallel
DSP
MEMORY
ADC
PORTS
DAC
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Multiply and Add
Most Common Operation in DSP
A = B*C + D
Multiply, Add, and Accumulate
E = F*G + A
...
MAC Instruction
1+2 = 3
+
0001
0010
0011
Add Multiply 5*3 = 15
Typically 70 Clock Cycles With
Ordinary Processors
MAC Operation
0
1
0
1
x
x
x
x
8
4
2
1
0011
0011
0011
0011
x
x
x
x
0000
0011
0000
0011
=5 3
Shifted and
added multiple
times
Typically 1 Clock Cycle With
Digital Signal Processors
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DSP Development
DSP
ASSEMBLER
HIGH-LEVEL LANGUAGE
EMULATOR
ADD A, B
Tools of the Trade
TEST
S/W DESIGN
OK?
Y
N
PRODUCT
CODE
11100010010100001001
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Digital Computers
STORED
PROGRAM
AND
DATA
ARITHMETIC
LOGIC
UNIT
INPUT/
OUTPUT
von Neuman Machine
Harvard Architecture
STORED
PROGRAM
ARITHMETIC
LOGIC
UNIT
INPUT/
OUTPUT STORE
D
DATA
A
DD
D
A
A
A = ADDRESS
D = DATA
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TMS320 Family16-Bit Fixed Point Devices
C1x Hard-Disk Controllers
C2x Fax Machines
C2xx Embedded Control
C5x Voice Processing
C54x Digital Cellular
Phones
32-Bit Floating Point Devices
C3x Videophones
C4x Parallel Processing
Other Devices
C6x Advanced VLIW
Processor
Wireless Base
Stations/PooledModems
C8x Video Conferencing
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A Typical DSP System.
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Why Digital Processing?
Advantages to Digital Processing
Programmability
Stability
Repeatability
Special Applications
ADC DACPROCESS
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One Hardware = Many Tasks
Upgradability and Flexibility
Develop New Code Upgrade Analog Solder New Component
Programmability
LOW-PASS FILTER
MUSIC SYNTHESIZER
MOTOR CONTROL
SOFTWARE 1
SOFTWARE 2
SOFTWARE N
SAME
HARDWARE.. ..
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Analog Variability
Analog Circuits are affected byTemperature
Aging
Tolerance of ComponentsTwo Analog Systems using the same design andcomponents may differ in performance
1k + 10 years = 1.1k
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Digital Repeatability
Perfect Reproducibility Nearly identical performance from unit to unit
Performance not affected by tolerance
No drift in performance due to temperature or aging
Guaranteed accuracy
A CD player always plays the same musicquality
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Performance
Some special functions are best implementeddigitally
f
f1 f2
phase
frequency
gain
frequency
Lossless Compression
Linear Phase Filters Adaptive Filters
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Digital Signal Processing
(DSP) Advantages Repeatability
Low sensitivity to component tolerances
Low sensitivity to temperature changes
Low sensitivity to aging effects Nearly identical performance from unit to unit
Matched circuits cost less
High noise immunity
In many applications DSP offers higherperformance and lower cost
CD players versus phonographic turntable
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Practical DSP Systems
Hi-Fi Equipment
Toys
Videophones
Modems
Phone Systems
3D Graphics
Image Processing
And More ...
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Typical Signal Processing
Applications
Sound Recording Applications
Compressors and limiters
Expander and noise gate
Equalizers and filters
Noise reduction system
Delay and reverberation systems
Special effects
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Typical Signal Processing
Applications
Telephone Dialing Applications
FM Stereo Applications
Musical Sound Synthesis Echo Cancellation in Telephone Networks
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DSP Applications.
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Signal Generation
Sinusoidal signal- oscillators
Square wave signal
Triangular wave signal Random signals white noise
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Examples of Typical Signals
Electrocardiography (ECG) Signals
Electroencephalogram (EEG) Signals
Seismic Signals
Speech Signals Music Sound Signals
Time Series / Econometric Signals
Image Signals
Video Signals
Mechanical vibration signals