Discrete Time Signal Processing A. V. Oppenheim and R. W. Schafer. Second ...
Signals and Systems by A. V. Oppenheim, A. S. Willsky, and H. S.. Nawab.
Lecturer :Prof. Emeritus Dato’ Dr. Ir Zainul Abidin Md Sharrif.
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 1A. (for CC Students only) • 2) Digital Signal Processing EEEB363 Section 1B/C. (for EE/EP Students only)
• 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-toanalog 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. latest 4th Edition • 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: • • • •
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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 •
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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 DiscreteTime 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. • •
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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)
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8 Course Outcomes Represent Infinite/Finite Length sequences in terms of Time-Domain and Frequency-Domain representation by applying the Discrete-time Fourier Transform (DTFT). Explain and apply sampling theorem, analog to digital, digital to analog conversions and signal reconstruction. Determine the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) of discrete signal. Analyze and describe the behavior of an LTI system according to its poles and zeros and properties of Z-transform. Design and implement different frequency selective Finite Impulse Response (FIR), and Infinite Impulse Response (IIR) filters to meet frequency domain specifications. Analyze the input output of the linear and non-linear phase response of an LTI system from the basic structure. Able to simulate the DSP concepts using MATLAB and Real-Time laboratory implementation using DSP board. Able to produce a lab report based on the results of experiment and simulation.
Grading Policy: • • • •
Test Laboratory & Assignment Final: Total:
20% 30% 50% 100%
Signal Processing
Digital Signal Processing
Analog Signal Processing
Digital Signal Processing
Digital audio signal processing
Digital control engineering
Digital image processing
Digital Signal Processing
Speech processing.
RADAR Signal processing
Communications signal processing
What Is DSP? Analog Computer
a bit loud
Digital Computer DSP DAC
ADC 1010
1001
OUTPUT
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 MEMORY
ADC
DSP Chip Memory Converters (Optional) Analog to Digital Digital to Analog
DSP DAC
Communication Ports Serial Parallel
PORTS
Multiply and Add 1+2 = 3
Add +
0001 0010 0011
Multiply 0 1 0 1 5
Most Common Operation in DSP A = B*C + D E = F*G + A
.. .
Multiply, Add, and Accumulate MAC Instruction
x x x x
8 4 2 1
x x x x
Shifted and added multiple times
5*3 = 15 0011 0011 0011 0011
0000 0011 0000 0011
3
MAC Operation Typically 70 Clock Cycles With Ordinary Processors Typically 1 Clock Cycle With Digital Signal Processors
=
Digital Computers von Neuman Machine A
STORED PROGRAM AND DATA
D
INPUT/ OUTPUT
ARITHMETIC LOGIC UNIT
A = ADDRESS D = DATA
Harvard Architecture A
STORED PROGRAM D
A
ARITHMETIC LOGIC UNIT
INPUT/ OUTPUT D
STORE D DATA
TMS320 Family 16-Bit Fixed Point Devices
32-Bit Floating Point Devices
’C1x
Hard-Disk Controllers
’C3x
Videophones
’C2x
Fax Machines
’C4x
Parallel Processing
’C2xx
Embedded Control ’
’C5x
’C54x
Other Devices
Voice Processing
Digital Cellular Phones
’C6x Advanced VLIW Processor Wireless Base Stations/Pooled Modems
’C8x
Video Conferencing
A Typical DSP System.
DSP Development ADD A, B 11100010010100001001
HIGH-LEVEL LANGUAGE
ASSEMBLER
CODE EMULATOR
TEST
S/W DESIGN N DSP
OK? Y
Tools of the Trade
PRODUCT
Why Digital Processing? ADC
PROCESS
DAC
Advantages to Digital Processing Programmability Stability Repeatability Special Applications
Programmability One Hardware = Many Tasks SOFTWARE 1 SOFTWARE 2
..
SAME HARDWARE
SOFTWARE N
LOW-PASS FILTER MUSIC SYNTHESIZER
.. MOTOR CONTROL
Upgradability and Flexibility Develop New Code Upgrade Analog Solder New Component
Analog Variability Analog Circuits are affected by Temperature Aging
Tolerance of Components Two 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
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
Performance Some special functions are best implemented digitally
Lossless Compression
Adaptive Filters
f
gain
Linear Phase Filters phase
frequency
frequency f1
f2
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
