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Image Processing and Neural Networks Lab
Image Processing and Neural Networks Lab
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Reference Text: "Discrete-Time
Signal Processing" by Oppenheim and Schafer Digital
signal processing is widely used in digital communications for equalization
(ISI, channel, and multipath) and simulation. In spread spectrum
applications, including CDMA, IIR filters are widely used in the
LPC encoding and decoding of speech, and in the generation of pseudonoise. Other
applications include the processing of biomedical signals such as CAT scans and
NMR images, machine vision, the enhancement of satellite images, the processing
of sonic and electromagnetic signals by the oil and oil service companies, the
processing of SONAR and RADAR signals, and automatic target recognition.
This course provides an introduction to digital signal processing for
both undergraduate students (in EE4318) and for graduate students (in EE5350).
In the first part of the course, we will study convolution, time invariance,
stability of discrete-time systems, and the discrete-time Fourier transform. In
the second part of the course, we will use the Z-transform to analyze the
stability of systems, and to find the system transfer function. The discrete
Fourier transform (DFT) and fast Fourier transform (FFT) will be studied.
In the third part, we will examine time and frequency domain techniques for
designing and applying infinite impulse response (IIR) and finite impulse
response (FIR) digital filters. There will be at least four program assignments in the course, and a fifth optional extra credit project. Prerequisite: EE3317. EE5350 Digital Signal Processing is a prerequisite for EE5351 and EE5352.
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