Course description:

• ELEC 6161 Stochastic Processes for Communications and Signal Processing (4 credits)
  Bayesian, maximum likelihood and mean-square estimation, mean square sense ergodicity, differentiation and integration, Wiener and Kalman filters, nonlinear systems with stochastic input, direct and Rice methods for calculation of an autocorrelation function, linearization methods, discrete-time Markov chains, state occupancy time, global balance, limiting probabilities, Markov process, Gauss-Markov (Ornstein-Ulenbeck) process. Lectures: three hours per week. Project: two hours per week. Note: Student who have received credit for ELEC 7141 or ENCS 6161 may not take this course for credit.
• ELEC 6601 Digital Signal Processing (4 credits)
  A review of discrete-time signals and systems; difference equations, the ztransform, the discrete Fourier series and transform, circular convolution; recursive and non-recursive network structures; discrete random signals, spectral estimation of discrete signals; filter structures for adaptive filtering, static and transient properties of the LMS adaptive filter; adaptive filtering applications. Lectures: three hours per week. Project: two hours per week.
• ELEC 6611 Digital Filters (4 credits)
  Approximation and design of recursive and non-recursive digital filters. Transformations. Stability. Digital filter structures including wave and lattice structures. Effect of quantization, noise and limit cycles. Hardware implementation. Digital filter applications. Lectures: three hours per week. Project: two hours per week. Prerequisite: ELEC 6601
• ELEC 6631 Digital Video Processing (4 credits)
  Video processing fundamentals; video signals and systems. Fourier analysis of video signals, video scanning and transmission, spatio-temporal sampling, selected material on the Human Visual System, modelling of video components, motion estimation and representation. Video filtering and enhancement: noise reduction, noise estimation, de-interlacing, frame-rate conversion, signal processing for improved TV-systems. An introduction to video compression, Lowlevel video analysis: local operators, linear and non-linear operators, rankorder filters, morphological filters, edge detection, segmentation. Lectures: three hours per week. Project: two hours per week. Prerequisites: ELEC 6601; ENCS 6161 (or ELEC 6161)
• ELEC 6621 Digital Waveform Compression (4 credits)
  Numerical representation of waveform information; common waveform communication systems; statistical models used for waveforms; visual psychophysics. Differential PCM, motion estimation/compensation for video compressions. Transform coding: run length coding, Huffman and arithmetic coding, control of Q factor and Q table, segmentation/contour/edge based coding; pre-processing and post-processing strategies. Vector quantization. Subband coding and Wavelet Transform. Zero trees. Channel concerns: robustness, error recovery, masking video/image bit rate source models. Coding of twolevel graphics. Review of standards: JPEG, MPEG, H.261. Lectures: three hours per week. Project: two hours per week. Prerequisite: ELEC 6601, ENCS 6161
• ELEC 7601 Adaptive Signal Processing (4 credits)
  Optimal filtering; filter structures for adaptive filtering; the LMS stochastic gradient algorithm; block least-squares methods; lattice structures. Convergence properties of transversal and lattice stochastic gradient algorithms. Stability and sensitivity analysis of adaptive filters. Lectures: three hours per week. Project: two hours per week. Prerequisites: ENCS 6161, ELEC 6601
• ELEC 7611 Advanced Signal Processing (4 credits)
  Parameter characterization, waveforms and structural models. Least-squares solutions. Noise cancellation. Predictive spectral estimation. Structural signal representation. Generalized phase plane. Function elimination filtering. Homomorphic signal analysis. Application in communication, speech recognition and measurements. Lectures: three hours per week. Project: two hours per week. Prerequisites: ENCS 6161, ELEC 6601
• ELEC 7631 Multi-dimensional Signal and Image Processing (4 credits)
  Multidimensional signals and systems. Two-dimensional discrete Fourier analysis: discrete Fourier transform, computation of DFT and computational considerations. Two-dimensional FIR filters: convolutional and DFT implementations, design using windows, least-squares design. Recursive systems. Two-dimensional IIR filters: implementations, space-domain design methods, frequency domain design, design for specialized structures. One of more specialized topics: finite-word-length effects, symmetry in two-dimensional filters, signal reconstruction and real-time image processing. Lectures: three hours per week. Project: two hours per week. Prerequisite: ELEC 6601
• ENCS 6161 Probability and Stochastic Processes (4 credits)
  Review of probability and random processes; stationary processes; ergodic processes; power density spectra of stationary processes; complex analytic representation of processes; Gaussian process; the Kharhunen-Loeve expansion; mean square estimation, spectral estimation. Prerequisite: ENCS 6011 or equivalent.
• ENCS 6181 Optimization Techniques I (*)(4 credits)
  Prerequisite: ENCS 6101 or equivalent. The optimization problem; classical optimization; one dimensional search techniques; unconstrained gradient techniques; quadratically convergent minimization algorithms; constrained optimization; constrained gradient techniques; penalty-function methods; applications. Project: two hours per week.
• COEN 6331 Neural Networks (4 credits)
  Prerequisites: COEN 5301, ENGR 6131. Fundamentals of artificial neural networks; rigorous analysis of and introduction to various network paradigms: perceptrons, backpropagation, counterpropagation, Hopfield nets, bi-directional associative memories, adaptive resonance theory, cognitron and neocognitron; neural network topologies, memories, learning, stability and convergence; applications to adaptive knowledge, knowledge processing, classification, pattern recognition, signal processing, communications, robotics and control; and assessment of current neural network technology. Lectures: three hours per week. Project: two hours per week.
• ELEC 6151 Information Theory and Source Coding (4 credits)
  Prerequisite: ENCS 6161 or ELEC 6161. Entropy of a source, rate distortion functions, source coding, analog to digital conversion, effects of sampling and quantization, vector quantization. discrete memoryless channels and their capacity, cost functions, channel coding theorem, channel capacity, fundamental concepts of information theory with applications to digital communications, theory of data compression, broadcast channels, application to encryption, DES, public key encryption, computational complexity. Lectures: three hours per week. Project: two hours per week. 08
• ELEC 6111 Detection and Estimation Theory (4 credits)
  Prerequisite: ENCS 6161 or ELEC 6161. Basic hypothesis testing, cost functions, Bayes and Neyman Pearson tests, the power of a test, sequential tests; estimation, Bayes estimates, maximum a posteriori estimates; the Cramer-Rao inequality, maximum likelihood estimates; composite hypothesis testing, application of estimation theory to phase locked loops, vector representation of signals in noise, application of the Kharhunen- Loeve expansion, complex analytic representation of signals; detection and estimation of signals in white and non-white noise, the matched filter, composite hypothesis testing, random amplitude and phase, multi-path channels, waveform estimation, Wiener filters, Kalman filters. Lectures: three hours per week. Project: two hours per week.
• COEN 6321 Applied Genetic and Evolutionary Systems (4 credits)
  Prerequisite: COEN 5301. Motivation for the use of genetic algorithms (GAs). Theory: the Schema Theorem, the K-armed Bandit, the Building Block Hypothesis, the Idealized GA, comparison of GA s. Methodology: representation, fitness and selection, crossover and mutation, parameterization and constraints, implementation. Applications: function optimization, evolving computer programs, optimizing a pattern recognizer, system modeling. Identification of classes of problems suitable for the use of GAs. Lectures: three hours per week. Project: two hours per week.
• ELEC 6831 Digital Communications I (4 credits)
  Random processes and linear systems; baseband modulation/demodulation, optimal receivers in AWGN, correlation and matched-filter receivers, pulse shaping for band-limited channels; bandpass modulation techniques such as PAM, PSK, DPSK, FSK, QAM; Introduction to error control coding, Linear block codes, Cyclic codes, Convolutional codes. Lectures: three hours per week. Project: two hours per week.
• ELEC 6181 Real-time and Multimedia Communication over Internet (4 credits)
  Prerequisite: ELEC 6851. Review of Internet architecture and protocols. Network impairments: jitter and delay. RTP: transport protocols for real-time data. Packet scheduling, QoS in the Internet: differentiated services, integrated services, Resource reservation protocol (RSVP), Multi protocol label switching (MPLS). Voice/Fax/Video over IP. Internet-to-PSTN. Protocols and standards - H.323, Session Initiation Protocol (SIP) and Media Gateway Control Protocol (MGCP). Internet telephony signaling. Interoperability issues. Lectures: three hours per week. Project: two hours per week. ELEC 6221
• ELEC 7141 Advanced. Stochastic Processes for Communications and Signal Processing (4 credits)
  Wiener processes, Markov processes, the Ornstein Ullenbeck (Gauss-Markov) process, diffusion equations, Fokker Planck equation, shot noise process, Markov chains, classification of chains, steady-state solutions, first passage problems, imbedded Markov chains, point processes, nonlinear systems with stochastic inputs. Lectures: three hours per week. Project: two hours per week. Prerequisite: ENCS 6161
• COMP 6771 Image Processing (*) (4 credits)
  Prerequisite: COMP 5511. Digital image fundamentals; image transforms: Fourier, Walsh, Haar, Hotelling, wavelet; image enhancement: histogram processing, spatial filtering, high- and low-pass filtering; image restoration; image compression; elements of information theory, image compression models, error-free compression, lossy compression, image compression standards; image segmentation: line detection, Hough transform, edge detection and linking, thresholding, region splitting and merging; representation and description: chain codes, signatures, skeletons, shape descriptors, moments, texture. A project.
• COMP 7781 Advanced Image Processing and Computer Vision (4 credits)
  Prerequisite: COMP 6771. Wavelet transforms, image filtering and compression; image segmentation, active contour models, geodesics, snakes, Markov random fields, Mumford-Shah model; Expectation-Maximization (EM) algorithm; motion and tracking; texture; watermarking and image inpainting. A project.
• ENGR 8901 Master of Applied Science Research and Thesis (29 credits)
• ENGR 8911 Doctoral Research and Thesis (70 credits)