COURSES
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Probability and Statistics (EE 3341), Spring 2008
Probability axioms, conditioning and independence, combinatorics, random
variables and distributions, averages and moments, functions of a random
variable, joint distributions and densities, limits, moment generating
function, the central limit theorem, sample mean and variance,
regression techniques, empirical distributions, law of large numbers.
- Signal Theory (EE 6350), Fall 2007
Basic overview of matrix and vector analysis, vector representation of
signals, least squares (LS) approximation and the orthogonality
principle, Minimum-norm and MNLS solutions, psuedo-inverses, eigen-value
and singluar-value decompositions. Time permitting, additional advanced
topics will also be visited.
- Coding
Theory (EE 6344), Spring 2007
Theory and practice of error-control coding; Linear block codes,
cyclic codes, BCH codes, Reed-Solomon codes, convolutional codes,
trellis coded modulation.
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Information Theory (EE 6341), Fall 2006
Probability review, Entropy, mutual information, the asymptotic
equipartition property. Lossless compression: Huffman, Shannon, Elias,
and arithmetic coding. Channel capacity: Shannon's channel coding
theorem, discrete channels, Gaussian channels, waveform channels,
elements of rate-distortion theory.
- Random
Processes (EE 6349), Summer 2004
Probability review, sequences of random variables, convergence, random
processes, continuity, Markov processes, Wiener and Poisson processes,
random signals and linear systems, filtering, prediction and smoothing
- Digital
Communications (EE 4360), Spring 2004
Baseband signal analysis, PCM, Channel effects: noise and distortion,
Detection of binary signals in noise, Matched filter and correlation
receiver, Intersymbol interference and equalization, ASK, PSK, FSK,
Coherent and non-coherent detection, Error performance, M-ary signaling,
Elements of coding: block codes and their decoding, convolutional codes.
- Source
Coding and Compression (EE 7V84), Spring 2001
Lossless and lossy compression of signals. Review of Huffman,
arithmetic and Lempel-Ziv coding. Overview of linear estimation and
prediction, the Levinson-Durbin algorithm. Scalar quantization,
optimality conditions and the Lloyd-Max algorithm. Vector quantization,
optimality and the generalized Lloyd algorithm. Predictive quantization
and performance bounds. Bit allocation and transform
coding. Tree-structured VQ, hierarchical VQ. Advanced topics: trellis
coded quantization (TCQ).
- Digital Signal Processing
Digital signals and systems, digital filter design, the FFT and its
variations, multirate signal processing and wavelets
- Image and Video
Communications
Advanced graduate course on image and video compression, image and
video standards
- Signals and Systems (undergraduate)
Continuous- and discrete-time linear time-invariant systems; time
domain analysis, Fourier and Laplace frequency domain analysis,
stability, Nyquist sampling.
Aria Nosratinia
Last modified Sept. 2006