Courses | VD Academic Affairs

Introduction and fundamentals of nonlinear systems, phase plane analysis, Lyapunov stability, feedback linearization, sliding mode control, output feedback control, back stepping control, case studies.

Introduction to optimization and Mathematical review, formulation of optimization problems, linear programming: the simplex method, duality, applications of linear programming, nonlinear programming: unconstrained single variable and multivariable optimization, constrained single variable and multivariable optimization, case studies.

Mathematics of fuzzy sets and logic, fuzzy rule based and fuzzy inference engines, Fuzzifiers and defuzzifiers, fuzzy systems and their properties, design of fuzzy controllers using clustering and table look-up scheme, introduction to other AI techniques.

Introduction to adaptive control, parameter estimation/identification, self-tuning regulators, model-reference adaptive control, properties of Adaptive systems: stability, averaging, robustness, adaptive control using different control methodologies, case studies.

Source models?Basic concepts of information theory?Variable- length coding, Huffman codes, Colomb codes, Tunstall codes?Universal source coding, Arithmetic coding, Dictionary coding?Context-based coding, ppm coding, Burrows-Wheeler transform ? Lossless image coding, Predictive coding, Progressive transmission, Run-length coding, Facsimile coding ? Quantization?Transform coding, Walsh-Hadamard transform, Haar transform, Discrete-Cosine transform?JPEG image coding standard?Overview of audio and video coding.

Digital signaling formats and Power Spectra, Random Process, transmission of Random Processes and White Gaussian Noise (WGN) through Linear Systems, Signal-to-Noise Ratio (SNR) at the Input and Output of Low Pass Filter (LPF) and Matched Filter (MF), Information Theory, Entropy, Channel Capacity, Shannon Theorem and Applications, performance of Digital Communications Systems, Baseband Binary and Coherent and Non-coherent Communication Systems.

Laboratory experiments related to 0610482 course contents.

Digital filters design: IIR and FIR. Windowing, frequency sampling, S-to-Z methods, frequency-transformation methods. Advanced digital signal processing topics: flow graphs, realizations, quantization effects, linear prediction, statistical and deterministic least squares filter design, finite length register effects and their optimization in digital filters, introduction to adaptive filtering. 2-dimensional filter design.

Laboratory experiments related to 0610385 course contents.

Various Radar Systems and Range Measurements, Doppler (Velocity) Measurements, Range-cross Range (Angular) Resolution, Doppler Resolution, Monostatic Radar Equation, Bistatic Radar Equation, Beacon and Jammer Radar Equation, Continuous Wave (CW), Frequency Modulated (FM) Radar System, Moving Target Indicator (MTI) Radar System, Target Detection, Waveform Design and the Ambiguity Function, Phased Array Antennas, Ultra-Wideband Impulse Radar Technology and its Applications

An image model, sampling and quantization and basic relationships between pixels, Imaging geometry, two dimensional Fourier transforms, image enhancement: spatial, domain and frequency-domain methods, image restoration, image segmentation.

Artificial neural system: preliminaries, fundamental concepts and models of artificial neural system, single layer preceptor classifiers, multi-layer feed forward networks, single layer feedback networks, associative memories, matching and self organizing networks, applications of neural algorithms and systems, neural network implementation.

Formal classroom instruction of a new topic.

The student undertakes an independent project (theoretical, and/or, practical, and/or research topics) under the supervision of a faculty advisor. The objective is to provide the student with an opportunity to integrate and apply the knowledge gained throughout his course in an actual problem. The student must document his study in a technical report and give an oral presentation.

Basic methods of modern system theory. Time domain techniques for both linear and nonlinear systems. Characterisation of both continuous and discrete time linear systems in the time and frequency domain. stability, controllability and observability for linear and nonlinear systems.