dspl1a.pdf

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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.htm


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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 audio signalprocessing

Digital controlengineering

Digital imageprocessing


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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

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