Image filtering and deconvolution, eigenimages, noise reduction and restoration, color image processing, multi-resolution processing, image compression, morphological image processing,scale-space techniques, feature extraction and recognition, image thresholding/segmentation, image registration and image matching.

Pattern recognition algorithms and their applications, statistical decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, feature extraction, feature selection, linear classifiers, neural networks, nonmetric methods, unsupervised learning and clustering.

Introduction to radars, radar data acquisition, radar waveforms, the range equation, radar detection in interference, propagation effects and mechanisms, characteristics of clutter, target reflectivity, target reflectivity fluctuations, Doppler processing, radar antennas, transmitters and receivers, radar signal processing, and radar remote sensing.

The course will consist of lectures, homework assignments and a project on a topic related to the student's area of interest. We will aim for the right balance of theory and applications. Analysis of Filter Banks and Wavelets, Design Methods, Applications, Hands-on Experience with Software.

Biomolecular and Cellular Principles, Physiological Principles, Biomechanics, Bioinstrumentation, Bioimaging and Signal Processing, Biotechnology, Engineering of Immunity, Biomaterials

Optimal mean-square estimation, Wiener filters. Introduction to adaptive structures and the least squares method. State space models. Kalman filters. Search techniques: Gradient and Newton methods. LMS (least mean squares), RLS (recursive least squares).

Techniques used in the time-frequency analysis of nonstationary signals: Empirical Time Decomposition, Ensemble Empirical Time Decomposition, Multivariate Emprirical Time Decomposition, Variational Mode Decomposition, Intrinsic Time-Scale Decomposition, Short-time Fourier Transform, Fourier Based Synchrosqueezing Transform

Artificial neural networks architectures and learning algorithms. Multi layer perceptron, radial basis function networks and support vector machines. Regression / function approximation, classification and clustering. Artificial neural networks for signal processing, filtering and pattern recognition. Artificial neural networks for system identification and control.

Static optimization with and also without constraints. Optimality conditions. Lagrange multipliers. Karush-Kuhn-Tucker conditions. Steepest-descent and Newton methods. Calculus of variations. Optimal control of discrete time and continuous time systems. Linear quadratic regulator, steady state closed loop control and tracking control. Dynamic programming of both discrete time and continuous time systems.

This course covers basics of wind energy generation systems, detailed steady state and dynamic operation principles of back-to-back power converters and doubly fed induction generator, analog current-voltage measurements and discretization, signal filtration and separation for power converters, vector control strategies and estimator structures of power converter controllers.

In this course, various analytic and iterative optimization methods will be introduced to determine the "best" or "most desired" solution to the problems encountered in Engineering. The most commonly used optimization methods will be presented with computer solutions to problems in electrical engineering. Programs will be developed for iterative solution of optimization methods using MATLAB platform.

Topics include convex sets, convex functions, optimization problems, least-squares, linear and quadratic programs, semidefinite programming, optimality conditions, and duality theory. Applications to signal processing, control, digital and analog circuit design, computational geometry, statistics, and mechanical engineering are presented. Students complete hands-on exercises using high-level numerical software.

This course covers introduction to autonomous robotics, motion models of a robot, measurement models of different sensor types, filtering techniques, simultaneous localization and mapping method

This course is an introduction to deep learning which is behind many recent advances in AI, such as face recognition, self-driving cars, human-like speech generators, etc. A range of topics including basic neural networks, convolutional and recurrent network structures and NLP will be covered, focusing on both theory and practice. A strong mathematical background in calculus, linear algebra, probability & statistics, and coding experience with Python are expected. Machine learning background is good to have but not required.

In this course at which current scientific applications on circuits and systems are considered, as reference, journal and conference papers leading the area in the global scale are used. Applications of circuits and systems theory in new scientific theories and emergent technologies are studied.

Characterization of one- and two-port networks using y-, z-, h-, ABCD-, and s-parameters signal flow graph analysis, passive circuit design, power dividers and directional couplers, filters, antennas, active circuit design, microwave transistor amplifiers, microwave transistor oscillators, wireless Systems.

Electro-magnetic resonance and magnetic curve based methods, Near field load modulated passive RFID methods, Full active transponders, Spectrum use and performance limitations, Data formats, encoding methods and standards, Data integrity and security for RFID, Multi-tag arbitration and addressing algorithms.

Overview of antenna systems and radiation mechanism; antenna types; fundamental parameters of antennas; engineering principles; radiation integrals; linear wire antennas; microstrip and printed antennas; aperture antennas; numerical computations; measurements.

The Maxwell equations, time-domain methods: finite differences and finite elements, frequency-domain methods: The method of moments, finite elements, high frequency methods: Geometrical optics, diffraction and multipole methods. Areas of application.

In this course, localization, object recognition, tracking, sensor fusion, mapping, avoiding obstructions in autonomous vehicles will be explained and Python based perception, motion planning and navigation techniques using Robot operating System (ROS) environment will be taught.

Advanced topics in electromagnetics, advanced antenna systems, advanced topics in propagation of electromagnetic waves and measurements

Models for Integrated Circuit Active Devices, Bipolar, MOS and BiCMOS Integrated Circuit Technology, Transistor Amplifiers, Current Mirrors, Active Loads and References, Output Stages, Operational Amplifiers, Frequency Response of Integrated Circuits, Feedback, Stability, Nonlinear Analog Circuits

MOS Transistor Theory, CMOS Technology, Differential and Operational Amplifiers, Output Stages, Pseudo Analog Techniques, Switched Capacitor Circuits, Continuous Time Filters, Data Converters, Voltage References

MOS Transistor Theory, CMOS Processing Technology, Circuit Characterization, CMOS Logic Gate Design, CMOS Logic Structures, Dynamic Logic and Clocking Strategies, I/O Structures, Memory, Low Power VLSI Design, Design Strategies, Chip Design Options, Design and Verification Tools, CMOS Testing

Introduction to data converters, sampling in analog/digital and digital/analog converters, issues in data conversions, algorithmic converters, switched capacitor circuits, non-linearity in converters, switched-capacitor amplifiers/integrators, sample-and-hold circuits, Nyquist-rate converters, and oversampled converters. Limitations of converters, linear and non-linear noise.

Transistor models and distortion generation, large-signal performance at basic gain stages of analog ICs; power series and distortion in amplifiers, distortion generation using source resistance and nonlinear beta, distortion in feedback amplifiers, basic output stages of ICs, simple bandpass amplifiers, transformators, basic electronic oscillators, analog multipliers, mixers, modulators, demodulators and detectors, phase-locked loop.

Detection theory, binary M-level hypothesis test, estimation theory, representation of stochastic processes, Karhunen-Loeve expansion, detection and estimation of signal parameters in presence of white and colored noise, estimation of continuous waveforms, optimum linear realizable processors, and Wiener-Hopf equation.

Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

Overview of wireless communications, path-loss shadowing, Wireless channels models, Basic digital modulation techniques over wireless channels.

Introduction and Background, Orbital Aspects and Launching, Spacecraft Subsystems, Link Budgets, Modulation, Multiple Access & On - board Processing, Coding, Frequency & Propagation Aspects, Earth Station Technology & VSATs, Non-Geosynchronous Orbits (NGSO), Applications (GPS, Mobile, Internet, etc.).

Internationally leading journal and conference papers will be the resource of this course in which latest scientific developments will be covered. Examples of applications in circuits and systems in theframework of recent theory and emerging technologies will be studied. theory Current scientific theories and emerging technologies, of the theory will be examined. Students will learn about the main concepts of the field in addition to multidisciplinary, interdisciplinary and transdisciplinary research.

The course will cover hardware and software design methodologies, use of CAD and simulation tools, assemblers, compilers, debuggers, and programmers. Different microprocessor architectures such as Motorola, Intel, and ARM will be discussed and evaluated, as well as Operating Systems such as uC-Linux and PalmOS. Computer interfaces such as USB, PCI, Ethernet, and Bluetooth will also be discussed in detail.

Hardware and software aspects of embedded DSP systems, interaction between hardware and software, real-time principles and trade-offs in algorithm design and implementation.

Definitions of stability in energy systems, simulation methods, swing equation, equal area criterion, mathematical model of synchronous machines, excitation and mechanical regulator models, multi-machine system modeling, numerical methods, and stability analysis of a single and multi-machine systems.

This course addresses concepts that underlie power quality issues such as harmonic generation and harmonic flow, and the modeling of voltage sags and swells. The effects of such disturbances on equipment (transformers, rotating machines, lamps, relays and converters) performance are studied by means of actual field cases. Other topics covered are Power Quality measurements in the era of smart grid, Power Quality problems caused by Renewable Generators, and Engineering Economics issues related to Power Quality.

Solar energy, bioenergy, small scale hydro-turbines, wind energy, wave energy, geothermal energy, tidal energy, production, storage, and use of hydrogen energy.

The content of this course is a set of selected research papers representing a major breakthrough in the area or a recent survey about a given topic.

The course will cover fast wavelet transform algorithms – relation to filter banks, wavelet packets construction of wavelets, biorthogonality and biorthogonal basis, biorthogonal system of wavelets - construction, and the lifting scheme.

Introduction to video systems, Fourier analysis of video signals, spatio-temporal sampling, motion analysis and motion estimation, video filtering and restoration, video coding and video compression techniques, superresolution, digital TV and video communication standards.

Introduction to nonlinear phenomena: multiple equilibria, limit cycles, bifurcations, complex dynamical behavior. Planar dynamical systems, analysis using phase plane techniques. Describing functions. Input-output analysis and stability. Lyapunov stability theory. The Lure problem, Circle and Popov criterion. Feedback linearization and sliding mode control.

A unification of chaotic dynamics and fractal sets in a dynamical system and set theory background. Sensitive dependency and topological transitivity in invariant sets. From stable fixed points to period doubling, and entrance to chaos. Symbolic dynamics and examples for strange attractors. From cantor set to classical fractals. Self-similarity and fractal dimension. Image encoding by iterated function systems. Randomness in fractal construction. Chaotification. Engineering applications of chaos and fractals.

Boundary value problems in electrostatics and magnetostatics, Laplace´s equation in various coordinates, Green´s functions. Multipole expansion. Dispersion and dissipation in media, Kramers-Kronig relations. Wave guides and cavities. Radiation from a relativistic charged particle, fields, frequency and angular distribution of the radiation, synchrotron radiation.

This is a mandatory course in Electrical and Electronics Engineering Ph.D. Program. The course is in the area of Random Signals, Probability Theory, Stochastic Processes and Random Signal Processing. The course aims to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals

Common LISP and Prolog; Intelligent Agents; Problemsolving and Search: uninformed and heuristic search, A*, local search and optimization; Constraint satisfaction problems; Game playing and adversarial search; Logical reasoning. Propositional Logic. Firstorder logic. Inference in firstorder logic; Planning; Reasoning under uncertainty. Bayes rule. Belief networks. Using beliefs to make decisions. Learning beliefs; Special topics: Robotics, Natural Language Processing, Game Theory, other AI applications.

This course introduces the concept of heuristics to the students who have already know about mathematical optimization. The topics include basic heuristic constructs (greedy, improvement, construction); meta heuristics such as simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. The basic material on the heuristic will be covered in regular lectures The students will be required to present a variety of application papers on different subjects related to the course. In addition, as a project assignment the students will design a heuristic, write a code of an appropriate algorithm for the problem and evaluate its performance.

Supervised Learning: Decision trees, nearest neighbors, linear classifiers and kernels, neural networks, linear regression; learning theory. Unsupervised Learning: Clustering, graphical models, EM, PCA, factor analysis. Reinforcement Learning: Value iteration, policy iteration, TD learning, Q learning. Bayesian learning, online learning.

Topics of this course include theory, algorithms, and computational aspects of linear programming; formulation of problems as linear programs; duality and sensitivity analysis; primaldual simplex methods; the transportation, transshipment and assignment algorithms; extensions of linear programming; integer programming formulations and solution methods.

The following topics will be included: Digital images as twodimensional signals; twodimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Wavelets and Multiresolution processing; Image coding and compression; Video processing including video coding and compression.