Course Instructor : Dr. F. Asamoah
To present the fundamentals of discrete-time signals, systems, and modern digital processing algorithms and applications.
At the end of the
course students should be able to :
(1) define continuous
signals, disorder signals, digital signals.
(2) define Z-transform
(3) solve difference
equations
(4) design digital
filters
(5) apply DFT to
analyze signals
(6) understand F1R
and IIR systems
The rapid advances in computer technology in recent times has had a major inpact on a variety of disciplines. Thus there is the need for the student to be familiar with discrete-time systems and signals. This course is an introduction to a vast field of DSP, and deals with the fundamentals. DSP techniques are now used to analyze and process data in many areas of engineering and science, medicine, economics, social sciences, oil and gas exploration, and other geographical studies. There are many other applications.
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Scope of Discrete Signal Processing |
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Signal Classification |
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Difference Equations |
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The Z–transform; mapping properties of Z = eSt |
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Inverse Z-transform |
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Sampling Theorem |
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Fourier series expansion of periodic sequences |
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Discrete fourier Transform |
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Parseval Theorem |
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Fast Fourier Transform |
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MATLAB Computations |
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Discrete Time convolution |
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Realization Forms |
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IIR Discrete-Time Filters |
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The Impulse Lavanant Method |
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Bilinear Transform Design |
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Butterworth Discrete-time Filters |
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High order filters |
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Frequency Response |
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MATLAB computations |
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FIR Discrete Time Filters |
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Founer series method |
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Windowing |
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Implementation considerations |
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Frequency sampling design methods |
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MATLAB computations |
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Some implementation considerations |
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It is assumed that the student has had undergraduate courses in
(1) advanced calculus
(including differential equation)
(2) linear systems
for continuous time signals
(3) introduction
to the Laplace Transform
(4) Fourier Series
and Fourier Transform
2 Credits
Scope of Discrete Signal Processing, Signal Classification, Some practical applications, Difference Equations, The Z–transform; mapping properties of Z = eSt, Inverse Z-transform, Sampling Theorem, Fourier series expansion of periodic sequences, Discrete fourier Transform, Parseval Theorem, Fast Fourier Transform, MATLAB Computations, Discrete Time convolution, Realization Forms, IIR Discrete-Time Filters, The Impulse Lavanant Method, Bilinear Transform Design, Butterworth Discrete-time Filters High order filters, Frequency Response , MATLAB computations, FIR Discrete Time Filters, Founer series method, Windowing, Implementation considerations, Frequency sampling design methods, MATLAB computations, Some implementation considerations.
End of Semester Exam:
2-hour paper
- 90%
Laboratory:
1 laboratory exercise
- 10%
Required Text:
Introduction
to Discrete-Time Signal Processing (J.R. Johnson)
Supplementary Text:
(1) Digital
Signal Processing (Principles, Algorithms, Applications)
(J. G. Poakis and , D. G. Manoiakis)
(2) Introduction
to Signal Processing (J. O. Orfanidis)
Page Created by Maurice Burke, Sian
Katwaru, Shiva Mahadeo, Aneil Ramdeen and Ken Sooknanan.
The Department of Electrical and
Computer Engineering is part of the Faculty
of Engineering, The University of the West Indies