Proposed Courses

Important Notes State-of-the-art challenges in TV Broadcasting o New technologies in TV o Multi-view broadcasting o HDR imaging

Digital Image Processing (HDR) Within this course the following topics will be discussed: Image acquisition, sampling and quantization. Image enhancement: Spatial and frequency domain techniques. Image restoration: Inverse, Wiener and mean filtering. Color image processing: color models, color transformations and color segmentation. Image compression: Compression models, elements of information theory, error-free and lossy compression. Morphological image processing: Dilation, erosion, opening and closing, basic morphological algorithms. Image segmentation: Thresholding and region-based segmentation. Object recognition.

Wireless Communication (Multi-View) Within this course the following topics will be discussed: Decision theory: Binary hypothesis testing, M-ary testing, Bayes, Neyman-Pearson, Min-Max. Performance. Probability of error, ROC. Estimation theory: linear and nonlinear estimation, parameter estimation. Bayes, MAP, maximum likelihood, Cramér-Rao bounds. Bias, efficiency, consistency. Asymptotic properties of estimators. Orthogonal decomposition of random processes and harmonic representation. Waveform detection and estimation. Wiener filtering and Kalman-Bucy filtering. Recursive algorithms. Spectral estimation.

*Probability Theory and Stochastic Processes Within this course the following topics will be discussed: Probability theory. Random variables, distribution and density functions, expectation, moments, characteristic functions, functions of random variables, sequences, convergence concepts. Weak and strong law of large numbers, the central limit theorem. Stochastic processes, mean, autocorrelation, autocovariance, cross-correlation, cross- covariance. Orthogonal and independent processes. Stochastic differential equations. Ergodicity. Power spectral density. Gaussian, Poisson, Markov processes.

UT - Curriculum

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