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