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ECE 710

Special Topics in Communications and Information Theory

Graduate

Fall 2021 · Topics Communication and Inform Theory: Digital Signal Processing

Topics

  • Discrete Signals and Systems, Z-transform, and Discrete Fourier Analysis Discrete signals and systems; Z -transform; discrete-time Fourier transform (DTFT); transform domain analysis of signals and systems; discrete Fourier series (DFS) and transform (DFT); fast Fourier transform (FFT).
  • Digital Filter Design Finite impulse response (FIR) filter design; infinite impulse response (IIR) filter design.
  • Digital Processing of Analog Signals Bandlimited signals and Shannon’s sampling theorem; reconstruction from sampling.
  • Multi-Rate Digital Signal Processing Discrete sampling rate conversion; narrowband and short time Fourier transform and analysis; spectrogram; analysis-synthesis filter banks and multi-resolution analysis.
  • Multi-Dimensional Digital Signal Processing
  • Multi-dimensional DFT and FFT; multi- dimensional filter design; multi- dimensional general sampling lattice.
  • Digital Signal Processing and Machine Learning Neural networks; convolutional neural networks; machine learning for digital signal processing.

Readings

  • Discrete-Time Signal Processing, Oppenheim, Schafer and Buck , 2nd edition, Prentice-Hall, 1998.
    2nd edition · unspecified
  • Digital Signal Processing, Proakis and Manolakis, 3rd edition, Prentice-Hall, 1996.
    3rd edition · unspecified
  • Multidimensional Digital Signal Processing, Dudgeon and Mersereau, Prentic-Hall, 1983.
    unspecified
  • Both paper and computer homework assignments/solutions will be posted at LEARN. Homework assignments are not graded but are important to after -exam happiness. Computer homework uses MATLAB as an analysis, design and visualization tool.
    unspecified
Winter 2018 · Estimation and Detection Theory

Readings

  • E. Wong; Introduction to random processes, Springer-Verlag, 1983. B. Picinbono, Random Signals and Systems, Prentice-Hall, 1993. T. Kailath, A. Sayed, and B. Hassibi; Linear Estimation, Prentice-Hall, 1999. V. Poor; Introduction to signal detection and estimation, Springer-Verlag. A. V. Balakrishnan, Kalman ltering theory, Optimization Software Publ, 1995.
    unspecified
  • There is no single text that covers all the material adequately. Class notes on selected topics will be given out.
    unspecified
Winter 2018 · Topic 2 - Winter 2018

Readings

  • Schwartz, M., Broadband Integrated Networks, Prentice Hall, 1996.
    unspecified
  • Bertsekas, D. and R. Gallager, Data Networks, Prentice Hall, 1992.
    unspecified
  • Recently published research papers in wireless resource management
    unspecified
Winter 2017 · Topic 12 - Winter 2017

Topics

  • Review of the basics of single-user information theory: source coding theorem; channel coding theorem; typical sequences; Fano’s inequality.
  • Multiple access channel: the capacity region. Encoding of correlated sources: Slepian- Wolf Coding.
  • Broadcast channel: physically degraded, stochastically degraded, superposition coding, Marton’s region.
  • Relay channel: the capacity of the degraded case, block Markov coding, decode-and- forward, successive decoding, sliding-window decoding, backward decoding.
  • Review with Gaussian channels: geometric interpretation, Gel’fand-Pinsker Channel and Costa’s dirty paper coding.
  • Rate-distortion theory and Wyner-Ziv Coding: strongly typical sequences, the CEO problem.
  • Relay networks: compress-and-forward, extension to multiple relays, noisy network coding, repetitive encoding vs. cumulative encoding, joint decoding vs. successive decoding, backward decoding vs. forward decoding.
  • General wireless networks: Max-flow min-cut lemma, the cut-set bound, multiple cuts, transport capacity, upper bounds of the transport capacity, relation between transport capacity and network energy, scaling laws, hierarchical cooperation schemes.
Spring 2017 · Topic 21 - Spring 2017

Readings

  • Communication System Security
    unspecified
  • Introduction to Modern Cryptography
    unspecified
  • Some notes and supplement materials will be provided.
    unspecified
Spring 2017 · Topic 5 - Spring 2017

Readings

  • Error Control Coding, 2nd edition
    2nd edition · unspecified
  • Introduction to Coding Theory
    unspecified
  • Sphere Packing, Lattices and Groups, 3rd edition
    3rd edition · unspecified
  • Information Theory and Reliable Communication
    unspecified
  • No required textbook.
    unspecified
  • Papers in the Transactions of the IEEE (to be communicated during the lectures).
    unspecified
Spring 2016 · Topic 16 - Spring 2016

Readings

  • Discrete-Time Signal Processing, Oppenheim, Schafer and Buck, 2nd edition, Prentice-Hall, 1998.
    2nd edition · unspecified
  • Digital Signal Processing, Proakis and Manolakis, 3rd edition, Prentice-Hall, 1996.
    3rd edition · unspecified
  • Multidimensional Digital Signal Processing, Dudgeon and Mersereau, Prentic-Hall, 1983.
    unspecified
  • No required textbook. Lecture notes and study materials will be provided.
    unspecified
Spring 2015 · Topic 14 - Spring 2015

Topics

  • Topic 1: Sampling and Reconstruction (7 hours)
  • Riesz bases and frames in Hilbert spaces; Wittaker-Shannon-Nyquist sampling theorem; aliasing; general sampling theorem and approximation spaces; minimum error sampling; sampling in shift-invariant spaces; compressive sampling; non-uniform sampling.
  • Topic 2: Modeling and Representation of Signals and Images (6 hours)
  • Time-frequency representations and Heisenberg principle; global and local Fourier analysis; Gabor frames; wavelet transforms; Karhunen-Loeve basis of principal components; indepen- dent component analysis and sparse representations.
  • Topic 3: Basics of Numerical Optimization (5 hours)
  • Convex sets and functions; convex optimization problems; unconstrained minimization; gradient descent methods; conjugate gradient and Newton’s methods; constrained minimization; the Lagrangian functional; linear and quadratic optimization problems; duality.
  • Topic 4: Operator Equations and Regularization (7 hours)
  • Linear operators in Hilbert spaces and their spectral properties; invertibility and ill-posedness; Tikhonov regularization; Bayesian estimation framework; maximum-a-posteriori estimation; prior probabilities, their properties and construction.
  • Topic 5: Enhancement of Signals and Images (6 hours)
  • Linear filtering; locally adaptive filters; bi-literal filtering; total variation de-noising; wavelet de-noising; image de-blurring; super-resolution and information fusion.
  • Topic 6: Modern Methods of Image Processing (5 hours)
  • Image segmentation; active contours; image registration; image inpainting; “u + v” decomposition; pose and motion estimation; basics of visual-based control.

Readings

  • Basic Operator Theory
    unspecified
  • A Wavelet Course in Signal Processing
    unspecified
  • Bayes and Empirical Bayes Methods for Data Analysis
    unspecified
  • Convex Optimization
    unspecified
  • The students will be provided with copies of lecture notes. The recommended reading includes:
    unspecified
  • Relevant journal publications
    unspecified
Fall 2015 · Topic 6 - Fall 2015

Readings

  • Course lecture notes. No textbook is required.
    unspecified
Sources