Peyman Milanfar


EE 262: Statistical Signal Processing
Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise; includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Reviews the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.
EE 264: Image Processing and Reconstruction
Fundamental concepts in digital image processing and reconstruction. Continuous and discrete images, image acquisition, sampling. Linear transformations of images, convolution and superposition. Image enhancement and restoration, spatial and spectral filtering. Temporal image processing: change detection, image registration, motion estimation. Image reconstruction from incomplete data. Applications.
EE 153: Digital Signal Processing
Introduction to the principles of signal processing, including discrete-time signals and systems, the z-transform, sampling of continuous-time signals, transform analysis of linear time-invariant systems, structures for discrete-time systems, the discrete Fourier transform, computation of the discrete Fourier transform, filter design techniques.
EE 103: Signals and Systems
Characterization and analysis of continuous-time signals and linear systems. Time domain analysis using convolution. Frequency domain analysis using the Fourier series and the Fourier transform. The Laplace transform, transfer functions and block diagrams. Continuous-time filters.