This document provides an overview of an introductory signal processing course. It outlines the pre-requisites, course structure including distribution of grades, relevant textbooks, and provides definitions and examples of signals and systems. It also gives an overview of analog and digital signal processing and discusses applications in various domains such as communications, speech, image processing, biomedical engineering, military and mechanical. Finally, it provides a tentative week-by-week outline of topics to be covered including transforms, filter design techniques, and sampling.

1. Lecture 1
Introduction

2. Pre-Requisites
• Signals and Systems
• Signal Basics/Transformations
• System Basics
• Transforms (Fourier, Laplace)
• MATLAB ®
• Think and Test Approach
• No need to reinvent the wheel
• Albeit with its limitations

3. Course Distribution – 3 Credit Hour
3
•Quizzes (5-6) 10%
•Assignments (3-4) 5%
•Midterm 30%
•Complex Engineering Problem 15%
•Final 40%
© Hira Ali Jamal

4. Relevant Books
• Textbook:
(Prentice-Hall signal processing series) Oppenheim, Alan V._ Schafer,
Ronald W - Discrete-Time Signal Processing-Pearson (2009)
• Reference Book:

5. Signals & Systems ?
• Signal Definition?
• representation of information
• a physical quantity that is a function of any independent variable (time, space,
wavelength, …)
• measured quantity that varies with time (or position)
• Examples?
• electrical signal received from a transducer (microphone, thermometer, accelerometer, antenna, etc.)
• amount of precipitation over time; speech signal interpreted by your brain
• System Definition?
• a physical entity that processes the signal in some manner
• Examples ?
human ear; a board marker,…
• defined by models

6. What is Signal Processing (Kuhn 2005)
Signals may have to be transformed in order to
 amplify or filter out embedded information
 detect patterns
 prepare the signal to survive a transmission channel
 undo distortions contributed by a transmission channel
 compensate for sensor deficiencies
 find information encoded in a different domain.
To do so, we also need:
 methods to measure, characterize, model, and simulate signals.
 mathematical tools that split common channels and transformations into easily manipulated
building blocks.

7. Signal Processing
Signal
Processing
System
Input
signal
Output
signal
• Function : extract (output) desired
information (e.g. filtering,
parameter estimation)
analog
system
signal output
continuous-time signal continuous-time signal
discrete-
time
system
signal output
discrete-time signal discrete-time signal
digital
system
signal output
digital signal digital signal

8. Signal Processing
A/D DSP D/A
analog
signal
analog
signal
digital
signal
digital
signal
• Analog input – analog output : Example
– Digital recording of music
• Analog input – digital output: Example
– Touch tone phone dialing
• Digital input – analog output : Example
– Text to speech
• Digital input – digital output : Example
– Compression of a file on computer
Signal
Processing
System
Input
signal
Output
signal
• Function : extract (output) desired
information (e.g. filtering,
parameter estimation)
Processing Examples

22. Analog Electronics for Signal Processing
(Kuhn 2005)
Passive networks (resistors, capacities,
inductivities, crystals, nonlinear
elements: diodes …), (roughly) linear
operational amplifiers
Advantages:
 passive networks are highly linear
over a very large dynamic range and
bandwidths.
 analog signal-processing circuits
require little or no power.
 analog circuits cause little additional
interference

23. Digital Signal Processing (Kuhn 2005)
Analog/digital and digital/analog converters, CPU, DSP, ASIC, FPGA
Advantages:
 noise is easy to control after initial quantization
 highly linear (with limited dynamic range)
 complex algorithms fit into a single chip
 flexibility, parameters can be varied in software
 digital processing in insensitive to component tolerances, aging, environmental conditions,
electromagnetic inference
Relatively small size
Digital circuits can easily be mass produced, for example in VLSI form.
Desired accuracy can be achieved by increasing wordlength, within any cost limitation
Digital processors can be time-shared
Adaptive processing is permitted
Storage of digital signals is straightforward

24. Why Digital Signal Processing
• Disadvantage include:
• Need for A/D and D/A processing
• can require significantly more power (battery, cooling)
• digital clock and switching cause interference

25. DSP-Where?
• Communication – Signal Processing for Communication
• Channel Estimation, Echo cancellation, Detection, etc.
• Cellular phones, Satellite receivers, Modems, etc.
Courtesy : Lake of Soft

26. DSP-Where? Contd.
• Speech (sound) applications
• Speech compression, speaker identification, speech enhancement in noisy
environment, special effects
• Cell Phones, MP3 Players, Movies, Dictation, Text-to-speech,…
Speech enhancement
Speaker identification
Courtesy : DSP Development Corp.
Courtesy : Busim SPG
Courtesy : HumanScan GmBH

27. DSP-Where? Contd.
• Image Processing
• Image is after all a 2-D signal
Raw Image Filtered Image
Object tracking
Courtesy : ISIR, OSAKA Univ.

28. DSP-Where? Contd.
• Biomedical Signal Processing
• Magnetic Resonance, Tomography, Electrocardiogram,…
Courtesy : Srinivasan

29. DSP-Where? Contd.
• Military
• Radar, Sonar, Space photographs, remote sensing
Courtesy : IAI
Courtesy : CRISP, NUS

30. DSP-Where? Contd.
• Mechanical
• Motor control, process control,…
• Automotive
• ABS, GPS, Active Noise Cancellation, Cruise Control, Parking,…

31. Course Outline -Tentative
31 © Hira Ali Jamal

32. 32
Week Description
1 Introduction to discrete-time signals and systems.
Mathematical description of discrete-time signals and systems both in time domain
as well as in frequency domain, sequences, series, convergence.
2 The Discrete-time Fourier Transform
Discrete Fourier series and its properties, Discrete Fourier transform and its
properties
3 Computation of the Discrete-time Fourier Transform
Efficient computation of the DFT using decimation-in-time and decimation-in-
frequency algorithms.
4 The Z transform – 1
Forward and Inverse Z transform, Properties of the region of convergence (ROC)
5 The Z transform – 2
Properties of Z transform
6 Sampling of Continuous-Time Signals
Frequency domain representation of sampling, Reconstruction of a bandlimited
signal from its samples
7 Multirate Signal Processing - 1
Changing the sampling rate of discrete-time signals.
8 Multirate Signal Processing – 2
© Hira Ali Jamal

33. 33
Week Description
9 Transform Analysis of Discrete-Time Systems – 1
Minimum phase systems, Generalized linear phase systems.
10 Transform Analysis of Discrete-Time Systems – 2
Minimum phase systems, Generalized linear phase systems.
11 Filter design techniques - 1
Laplace Transform, Design of analog filters
12 Filter design techniques – 2
Design of discrete-time IIR filters from continuous-time filters.
13 Filter design techniques – 3
Design of discrete-time FIR filters.
14 Filter design techniques – 3
Design of discrete-time FIR filters.
15 Computation of the Discrete-time Fourier Transform
Efficient computation of the DFT using decimation-in-time and decimation-in-
frequency algorithms.
16 Revision © Hira Ali Jamal