This course covers: fundamental concepts of digital signal processing (DSP) systems such as analog to digital converter (A/D), aliasing, scalar and vector quantization, and coding; point spread function (PSF) of imaging systems; modulation transfer function (MTF); circularly symmetric imaging systems; techniques used for analyzing DSP based systems such as convolution, frequency response, MTF measurement, etc.; Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) algorithms, fast convolution and correlation; design of 1-D and 2-D digital FIR filters; sampling rate conversion; spectral estimation; adaptive signal processing with applications to deconvolution, system identification, and noise removal; adaptive arrays and beamforming; signal detection and estimation techniques; software for simulating some of the component blocks will be provided by the instructor.

The course deals with theory and application of continuous and discrete wavelet transforms. Topics covered include: time-frequency resolution, orthogonal and biorthogonal discrete wavelet transforms, multiresolution analysis, wavelet packets, wavelet design, implementation methods, image compression techniques and standards (JPEG2000), pattern recognition, watermarking for secure multimedia information dissemination and wavelet multitone modulation for high-speed internet access using ADSL technology.

This course is designed to give the audience an understanding of linear and nonlinear Principal Components Analysis (PCA). Principal Components Analysis is a useful statistical technique to find patterns in data of high dimension and reduce the data dimensionality. It is a nonparametric approach to extract relevant information from high dimension raw data sets. The course provides basic methods and algorithms to compute linear and nonlinear PCA from large sets of data. Participants will learn specific topics in detail, including data normalization, relevant statistics, data covariance matrix, overview of statistical signal processing, eigenvalue/eigenvector decomposition, linear PCA and its limitations, nonlinear PCA, feature vector extraction, and implementation of linear and nonlinear PCA. Application examples to be discussed include data compression in one dimensional (1-d), 2-d and 3-d space, remote sensing, face recognition, data processing for direction finding, image indexing and retrieval, source localization and signal detection in communication and radar systems.