ECE 498: Signal and
Image Analysis
Course Syllabus
Recommended
Texts:
The
text will be supplemented with notes and slides from the instructor, and
material on line.
Meeting Schedule/Contact Hours: Three 50-minute
lectures (3 contact hours) per week and one 50-minute laboratory section (1
contact hour) per week. Course is worth 4 credit hours.
Topical Outline:
Lecture Topics Contact hours
Signal
processing overview; continuous and discrete-time signals; period and
frequency |
2 |
Review
of complex numbers, Fourier Series, Fourier transform and properties |
5 |
Linear
and shift-invariant systems; convolution and impulse response; frequency
response |
4 |
Discrete-time
Fourier transform; discrete Fourier transform |
4 |
DFT-based
spectral analysis; the short-time Fourier transform |
3 |
Sampling;
ideal analog-to-digital and digital-to-analog conversion, quantization |
4 |
Digital
processing of analog signals, DT system theory |
4 |
Difference
equations; z-transform; poles and zeros; stability and causality conditions |
3 |
FIR
and IIR filters; notch filters; IIR filter types; generalized linear phase |
3 |
FIR
filter design |
3 |
Basics
of image processing: 2-D signals; human visual system; representation of
color |
2 |
Histogram
equalization, edge enhancement filters, directional (fan) filters, wavelets |
1 |
Overview
of image formation: projection-slice theorem, tomography, CT, MRI |
1 |
Overview
of discrete-time random processes and classical statistical spectral analysis |
1 |
Vector
space concepts and matched filters |
1 |
In-class
quizzes |
2 |
Lecture TOTAL |
43 |
Laboratory Topics Contact hours
Introduction
to Matlab |
1 |
Fourier
series; additive music synthesis |
1 |
DFT-based
spectral analysis |
1 |
DFT-based
analysis of digital images |
1 |
Spectral
analysis of biological data exercise |
1 |
Short-time
Fourier transform analysis of biological data |
1 |
Filtering
via frequency-masking: exercise with time-series and image data |
1 |
Notch
filter design and implementation: 60Hz denoising,
image denoising |
1 |
FIR
and IIR bandpass filter design, implementation, and
application to real data |
2 |
Image
processing: directional filtering |
1 |
Statistical
spectral analysis: Wiener filtering of audio and images |
3 |
Laboratory
Contact Hours TOTAL |
14 |
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Grading Policy:
Homework 20%
Labs 25%
In-class Quizzes 30%
Final 25%
Prepared
By:
Mark Hasegawa-Johnson