###EE 779 ADVANCED TOPICS IN SIGNAL PROCESSING

Course offered in: 2013

Instructors: V.Rajbabu

Course Content: The course is an extension of the DSP course.The topics given on the course webpage include: Introduction – Relevant concepts in DSP, linear algebra, matrix analysis, and statistical signal analysis (as needed) Spectral estimation (temporal) – Non-parametric spectrum estimation – Parametric methods for rational spectra (ARMA, MA, AR processes) and line spectra Array signal processing (spatial) – Arrays and spatial filters, space-time processes – Waveform estimation – broadband and narrowband beamformers – Subspace algorithms – MUSIC, ESPRIT, Root-MUSIC – Applications – direction-of-arrival (DOA) estimation, signal separation Selected topics in sampling and reconstruction – Dithered sampling, use of a random dither – Sampling, quantization, and interpolation – Sampling of non-bandlimited signals – Finite rate of innovation signals Change point problems – Canonical change point problems – Non-parametric and Bayesian approaches to the change-point problems – Feedback in change point problems Approximations in bases and compression – Linear and non-linear approximations – Karhunen Loeve approximations – Transform coding – Distortion rate of quantization Sparse signal processing – Sparse representation and recovery, pursuit algorithms, compressive sensing Monte Carlo methods in signal processing – Particle filter Inference in Hidden Markov Models, Expectation- Maximization (EM) algorithm

Prerequisites: “A sound understanding of the Digital Signal Processing course and linear algebra is necessary. The linear algebra prerequisites include an understanding of eigenvectors, Singular Value Decomposition (SVD).“

Further Applications of this Course: The course is quite useful in most of the fields of advanced signal processing & wireless communication such as: spectrum sensing, compressed sensing, blind source detection, coprime array processing etc. Textbooks/references The course is quite useful in most of the fields of advanced signal processing & wireless communication such as: spectrum sensing, compressed sensing, blind source detection, coprime array processing etc. Software There are weekly simulation assignments which are to be done on matlab. The coding level expected is moderate.

Difficulty: “The course is of average difficulty level with an average workload.

Grading Statistics: Last year the grading statistics were as follows: AA 5 AB 5 BB 6 BC 4 CC 2 CD 1 DD 1 Total 25″

Review by - Kedar Tatwawadi