dspl1a
DESCRIPTION
dsp labTRANSCRIPT
http://metalab.uniten.edu.my/~zainul/
• This Home Page is for my students who
are 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.
• Course Code:- EEEB363
• Course Title :- Digital Signal Processing
• Prerequisites:- Signals and Systems (EEEB233)
• Upon completion of the course, the student should have a solid foundation in basic digital signal processing.
• Aims/Objectives
To introduce the concepts, theory, techniques and applications associated 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 Fourier transform, design and implementation of digital filters.
• To be aware of some applications associated with digital signal processing.
EEEB363/4 Digital Signal
Processing
• Adopted Text Book:-
Digital Signal Processing - A Computer Based Approach, by S. K. Mitra. Published by McGraw Hill International, 3rd Edition, Year:2006.
• References:
1. Discrete Time Signal Processing A. V. Oppenheim and R. W. Schafer Second Edition Publisher Prentice Hall International.
2. Digital Signal Processing - A Practical Approach By E. C. Ifeachor and B. W. Jervis. Published by Addision-Wesley publishing Company, Year:1996
3. Signals and Systems by A. V. Oppenheim, A. S. Willsky, and H. S. Nawab. Published by Prentice Hall, 2nd edition. Year 1997.
4. Signal Processing First by James H. McClellan, R. W. Schafer, and M. A. Yo-der. Published by Prentice Hall, Year:2003.
Course Description
• Signal processing is a method of extracting information from signal which in turn depends on the type of signal and the nature of information it carries.
• Therefore, signal processing is concerned with the representing signals in mathematical terms and extracting the information by carrying out algorithmic operations on the signal.
• A signal can be mathematically expressed in terms of basic functions in original domain of independent variable or it can be expressed in terms of basic functions in transformed domain.
• In this course we will use tools available in both domains to analyze signals and systems in discrete time domain.
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 a discrete-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 the Discrete-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) and Fast 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 domain specifications.
• 12. Describe engineering trade-offs in filter design. Understand linear and nonlinear phase response.
course content and time
allocation• 1.Signals and Signal Processing:- (6Hours)
1.1 Characterization and Classification of Signals 1.2 Typical Signal Processing Operations 1.3 Examples of Typical Signals 1.4 Typical Signal Processing Applications 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 2.4 Discrete-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 Fourier Transform 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 and Group Delays.
• 4.Digital Processing of Continuous-Time Signals:- (6 Hours)
• 4.1 Introduction4.2 Sampling of Continuous-Time Signals4.3 Sampling of Bandpass Signals 4.4 Analog Lowpass Filter Design 4.5 Design of Analog Highpass, Bandpass, and Bandstop Filters4.6 Anti-Aliasing Filter Design 4.10 Reconstruction Filter Design 6
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 the DFT, and Their Inverses 5.6 DFT Symmetry Relations5.7 Discrete Fourier Transform Theorems 5.9 Computation of the DFT of Real Sequences11.3.2 Decimation in Time and Decimation 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 The Transfer Function
• 7.LTI Discrete-Time Systems in the Transform Domain:- (4 Hours)
• 7.1 Transfer Function Classification Based on Magnitude Characteristics 7.2 Transfer Function Class ideation Based on Phase Characteristics 7.3 Types of linear-Phase Transfer Functions 7.6 Inverse Systems
• 8.Digital Filter Structures:- (2Hours)
• 8.1 Block Diagram Representation 8.3 Basic FIR Digital Filter Structures8.4 Basic IIR Digital Filter Structures.
• 9.IIR Filter Design & FIR Filter Design:- (6 Hours)
Course Outcomes
• 1. Compute the discrete- time convolution of two signals and classify the discrete time system and the process of signals correlation
• 2. Evaluate the frequency response of a discrete-time, linear time-invariant (LTI) system from its impulse response and vice versa
• .3. Apply the definition, properties of the Discrete-time Fourier Transform (DTFT) in signal transformations.
• 4. Explain and apply sampling theorem, analog to digital, digital to analog conversions and signal reconstruction.
• 5. Determine the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) of discrete signal
• 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 LTI system and classify the stability and causality of an LTI system from plot
• 8. Design and implement different frequency selective Finite Impulse Response (FIR), and Infinite Impulse 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 it’s input output characteristics and analyze the input output of an LTI system from the basic structure
Grading Policy:
• Test 20%
• Laboratory & Assignment 30%
• Final: 50%
• Total: 100%
Signal Processing
Digital Signal Processing
Analog Signal Processing
Digital Signal Processing
Digital audio signalprocessing
Digital controlengineering
Digital imageprocessing
Digital Signal Processing
Speech processing.RADAR Signal
processingCommunicationssignal processing
What Is DSP?
a bit loudAnalog Computer
Digital Computer
ADC
DSP
DAC OUTPUT
1010 1001
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
A Typical DSP System
DSP Chip
Memory
Converters (Optional) Analog to Digital
Digital to Analog
Communication Ports Serial
Parallel
DSP
MEMORY
ADC
PORTS
DAC
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
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
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
AA
A = ADDRESS
D = DATA
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/Pooled
Modems
’C8x Video Conferencing
’
A Typical DSP System.
Why Digital Processing?
Advantages to Digital Processing
Programmability
Stability
Repeatability
Special Applications
ADC DACPROCESS
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.. ..
Analog Variability
Analog Circuits are affected byTemperature
Aging
Tolerance of ComponentsTwo Analog Systems using the same design and
components may differ in performance
1k + 10 years = 1.1k
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 music
quality
Performance
Some special functions are best implemented
digitally
f
f1 f2
phase
frequency
gain
frequency
Lossless Compression
Linear Phase Filters Adaptive Filters
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 higher
performance and lower cost
– CD players versus phonographic turntable
Practical DSP Systems
Hi-Fi Equipment
Toys
Videophones
Modems
Phone Systems
3D Graphics
Image Processing
And More ...
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
Typical Signal Processing
Applications
• Telephone Dialing Applications
• FM Stereo Applications
• Musical Sound Synthesis
• Echo Cancellation in Telephone Networks
DSP Applications.
Signal Generation
• Sinusoidal signal- oscillators
• Square wave signal
• Triangular wave signal
• Random signals – white noise
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