###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