Digital Image Fundamentals: Elements of visual perception–Image sampling and quantization Basic relationship between pixels– Basic geometric transformations. Image fundamentals and image restoration: Spatial domain methods‐Spatial filtering‐ Frequency domain filters –Model of Image Degradation/restoration process – Noise models – Inverse filtering ‐Least mean square filtering –Constrained least mean square filtering – Blind image restoration – Pseudo inverse – Singular value decomposition.

Introduction: speech production and perception, information sources in speech, linguistic aspect of speech, acoustic and articulatory phonetics, nature of speech ,models for speech analysis and perception; Short ‐ term processing: need, approach, time, frequency and time ‐ frequency analysis; Short ‐ term Fourier transform (STFT): overview of Fourier representation, non ‐ stationary signals, development of STFT, transform and filter ‐ bank views of STFT; cesptrum analysis: Basis and development, delta, delta ‐ delta and mel ‐ cepstrum, homomorphic signal processing, real and complex cepstru

Motivating examples of digital communications. Spectrum availability and channels. Channel modeling base band and pass band channels. Digital modulation schemes for base band and pass band channels. Line coding, Pulse amplitude modulation, Phase modulation, CPFSK, Frequency shift keying, QAM.

Maximum Likelihood Estimation (MLE): Exact and approximate methods (EM algorithm, alternating maximization, etc.)Cramer-Rao Lower Bound (CRLB) Minimum Variance Unbiased Estimation (MVUE) Sufficient Statistics Best Linear Unbiased Estimation (BLUE) Large and Small Sample Properties of Estimators: Understanding the behavior of estimators in both large and small sample sizes Bayesian Inference and Estimation: Minimum Mean Square Error (MMSE) estimation MAP Estimation (Maximum A Posteriori Estimation)Wiener and Kalman Filtering (Sequential Bayes) Detection Theory: Likelihood Ratio Testing Bayes