The Course Presents Fundamental Methods For Processing and Analyzing
Signals and Data Sources, Using Both Deterministic and Stochastic
Tools. The Topics Covered Are# Signals and Systems in Discrete
Domain, Fourier Analysis For Linear Time Invariant Systems, Signal
Processing and Representation in Algebraic Spaces, Statistical
Estimation - Maximum Likelihood, Bayesian Methods, Fundamentals In
Random Processes - Wiener, Poisson, and Markov Processes, Linear
Filtering, Random Noise, Parametric Models For Random Processes And
Their Estimation, The Cramer-rao Bound, Linear Prediction and Adaptive
Filters. Proving Optimality and Uniqueness of The Truncated Fourier
Basis in Representing Functions With Bounded Dirichlet Energy,
2-optimal Approximation For The Clustering Problem and Introduction To
Learning Outcomes# Graduates of This Course Will Be Able To#
1. Characterize Linear Time Invariant Systems, and Tie This Analysis
To The Frequency Domain.
2. Transform Signals to Alternative
Representations, Adopting a Matrix Form For The System and a Vector
For The Signals
3. Apply Statistical Estimation Methods, Parametric
And Direct, to a Variety of Basic Problems in Data Processing.
4. Apply Linear Filtering Methods Such As The Wiener Filter,
To Stochastic Sources of Noisy Data.
5. Set Performance Bounds For
Various Estimation Problems.
Faculty: Computer Science
(44130 - Signals and Systems and 44131 - Signals and Systems and 104174 - Algebra Bm and 234125 - Numerical Algorithms)
Course with no extra credit
236201 - Introduction to Data Processing And
236327 - Digital Image and Signal Processing