The Iby and Aladar Fleischman
Faculty of Engineering

School of Electrical Engineering

               Home               

               Teaching               

             Publications             

             Invited Talks             

     Activities and Awards     

Former Graduate Students

     Downloads and Links     

Courses I teach / taught:
Note: courses web-sites are at Virtual.t@u, accessible only to registered students.

Introduction to Statistical Signal Processing
(a 4th year undergraduate and graduate-level course):

Discrete-time Random Processes; Wide-sense stationary (WSS) processes and their properties; Linear parametric processes: Auto-Regressive (AR), Moving Average (MA), ARMA; Spectral estimation: nonparametric methods (periodogram, correlogram,  Blackman-Tukey, Welch), parametric methods, Yule-Walker (YW) and Modified YW equations; Detection of deterministic signals in noise: matched filter; Optimal linear filtering: causal and non-causal Wiener filters, Kalman filter; Introduction to adaptive filtering.

Communication Systems
(a 3rd-4th year undergraduate course): 

Basic concepts of communication systems, frequency-domain analysis of deterministic signals vs. stochastic processes; Digital communication: Pulse-Amplitude Modulation (PAM) and Pulse-Coding Modulation (PCM), quantization and quantization-noise; Matched Filter; Inter-Symbol Interference (ISI) and eye-patterns; Line Codes; Delta Modulation; Analog communication: Baseband signals, Noise Figure, Narrowband Noise Characterization; Amplitude-modulation methods: Double-SideBand (DSB), Amplitude Modulation (AM), Single SideBand (SSB), Hilbert Transform and its use; Phase-modulation methods: Phase Modulation (PM), Frequency Modulation (FM), NarrowBand FM (NBFM); Phase-Locked Loops (PLL).

Digital Signal Processing
(a graduate course):

Design of Finite Impulse Response (FIR) discrete-time filters: Generalized Linear Phase (GLP) filters and their four types; Coefficients design methods: Impulse-response Truncation (IRT), windowing, “optimal” windows, Least-squares (LS) weighting approaches, the Alternation Theorem and related algorithms (Parks-McClellan, Remez Exchange). Implementation approaches, computational efficiency and finite word-length considerations. Multi-rate signal processing, sampling rate conversion, Polyphase filters, Multi-stage filtering, Filter banks, Quadrature Mirror Filters (QMF), conditions for perfect reconstruction. Concepts in Linear Time-Varying (LTV) systems: Tellegen’s theorem; generalization of filter-banks to time-frequency analysis, introduction to Wavelets.

Digital Processing of Single- And Multi-Dimensional Signals
(a graduate course):

Single-Dimensional signals: Digital filtering principles, phase and amplitude relations, minimum phase, linear phase, group delay vs. phase delay, Generalized Linear Phase (GLP) filters; Multi-Dimensional (MD) signals and systems: MD Fourier transform, MD Z-transform, MD extensions of the Region of Convergence (ROC) concept, stability issues; FIR coefficients design: Single-dimensional: Impulse Response Truncation (IRT), windowing, Least-Squares approaches, Parks-McClellan and Remez exchange algorithms; Multi-Dimensional: zero-phase filters, MD windowing, frequency sampling, transformation approaches, extensions of single-dimensional tools; Muti-rate processing of single-dimensional signals, Polyphase filters, filter banks, perfect reconstruction, generalization of filter-banks to time-frequency analysis, the relation to wavelets; Wavelets in MD signal processing, pyramid representations; Interpolation with splines.

Advanced Digital Signal Processing Lab
(a 4th year undergraduate course):

The lab is based on TI’s TMS320UC6416, and features five DSP experiments aimed at demonstrating and exploring both theoretical and practical considerations in real-time implementation of DSP algorithms. The experiments include basic introductory functions, as well as more elaborate tasks such as real-time spectral analysis, FIR and IIR filtering and adaptive notch filtering.

Introduction to Signal Processing
(an undergraduate course to students of Biomedical Engineering):

Discrete and continuous signals and systems, classification, transformations, representation of signals in terms of an orthonormal basis; Linear, time-invariant (LTI) systems, impulse response, convolution, exponential signals as eigen-signals of LTI systems; Analysis of continuous-time periodic signals in terms of Fourier series, "continuous-discrete" signals and discrete signals, analysis of discrete-time periodic signals using a discrete-time Fourier series; Analysis of non-periodic continuous-time and discrete-time signals; The sampling theorem, reconstruction of continuous signals; Unilateral and bilateral Z-transform, its relation of the Discrete-Time Fourier Transform, rational LTI systems and their representation using difference equations.