Master of Science in Electrical and Electronics Engineering Department of Electrical and Computer Engineering

Size: px

Start display at page:

Download "Master of Science in Electrical and Electronics Engineering Department of Electrical and Computer Engineering"

Melina Griffin
6 years ago
Views:

1 Master of Science in Electrical and Electronics Engineering Department of Electrical and Computer Engineering Program Components The program requirements for the MSEEE program comprise of 9 credits of basic courses (3 courses) required of all students and 15 credits of elective courses (5 courses) in four different specializations areas of :. Control and Power Electronics Communication Systems Signal and Image Processing So, to be awarded the MSEEE degree, a student has to complete 33 credit hours distributed as given in Table 1. After careful reading the external review team (ERT) report and in corresponding to the requirement no. 8 and requirement no.13 and after taking the approval of the Electrical and Computer Engineering Department council and the approval of the college of engineering council, the Electrical and computer engineering department modified and shortlisted the list of courses as shown below. Table 1 - Program Components Requirements Credits 3 Basic Courses 9 5 Elective Courses 15 Thesis 9 Total 33 I. Basic Courses Table 2 - MEE Portfolio of Basic Courses Department Requirements Basic Courses Course No. Course Title Credits Prerequisite / Optimization Methods in Engineering 3 Grad Standing / Applied Mathematics for Engineering 3 Grad Standing / Engineering Research Methodologies Engineering 3 Grad Standing 206

II. Elective Courses Table 3 MEE Portfolio of Elective Courses Index Course No.

and Automation 5 0402630 Robust Feedback Control 0402530 6 0402632 Predictive Control 0402530 7 0402536 Modeling and Control of Power Systems 8 0402537 Analysis and Control of Electrical Machines 9

0402544 Error Control Codes 15 0402549 Special Topics in Communications 16 0402643 0403640 Mobile Computing 17 0403540 Computer Networks 18 0402550 Advanced Electronics 19 0402551 0403554 Analog IC

2 II. Elective Courses Table 3 MEE Portfolio of Elective Courses Index Course No. Course Title Pre-requisite Linear Multivariable Control System Nonlinear Systems Analysis and Design System Identification Special Topics in Control and Automation Robust Feedback Control Predictive Control Modeling and Control of Power Systems Analysis and Control of Electrical Machines Special Topics in Power Systems Advanced Power Electronics Communication Systems Engineering Detection and Estimation Information Theory Error Control Codes Special Topics in Communications Mobile Computing Computer Networks Advanced Electronics Analog IC Design Integrated Circuit Fundamentals Non-linear Circuits Analysis and Design Advanced Optoelectronics Special Topics in Electronics Neural Networks and Applications Digital Signal Processing Pattern Recognition Speech Processing Image Processing and Applications Special Topics in Signal and Image Processing Adaptive Filtering Wavelet and Time Frequency Signal Processing Computer Vision Independent Studies in Electrical and Electronics Engineering College of Engineering 207

3 / Optimization Methods in Engineering (3-0:3) Formulation, solution and implementation of optimization models such as linear programming, dynamic programming, integer programming, quadratic programming, convex programming, geometric programming and unconstrained optimization for analyzing complex systems problems in industry / Applied Mathematics for Engineering (3-0:3) Solution of linear equations. Eigenvalue eigenvector decomposition. Special functions. Complex analysis. Fourier analysis. Laplace transform. Introduction to partial differential equations. Various examples from engineering disciplines / Engineering Research Methodologies (3-0:3) Overview of methodological approaches to research; Basics of research design (e.g., hypothesis formulation); The research process: documenting research, sources of information, research funding, creativity and intellectual discovery; Guidelines and a framework for efficient development of research; legal and ethical issues; protecting and exploiting research; Intellectual Property rights; Managing a research project: supervision, planning and organization; problems and pitfalls Nonlinear Systems Analysis and Design (3-0:3) Introduction to nonlinear systems dynamics. Linearization, iteration and perturbation analysis. Phase plane method. Describing functions analysis. Limit cycles. Lyapunov stability. Input/output stability. Input/output linearization. Stabilization and control of nonlinear systems. Chaos System Identification (3-0:3) Review of transient and frequency response analysis. Regression analysis. Parameterization of models. Maximum likelihood and prediction error methods. Mathematical and experimental modeling. Model validation. Model approximation. Real-time identification. Closed loop identification. Introduction to nonlinear system identification Modeling and Control of Power Systems (3-0:3) Dynamic model of synchronous machines. Excitation and governor systems. Nonlinear and linear modeling of single machines infinite bus systems. Stability analysis and control design. Direct method of stability determination. Multimachine systems modeling. Power system dynamic equivalents Analysis and Control of Electrical Machines (3-0:3) This course covers the steady state and dynamic analysis of electrical AC and DC machines. Field orientation theory and control, (Direct and quadrature axis transformation) for AC machines. In addition to some recent control strategies for DC and AC machines by using. linear state space representation and simulation of electromechanical systems Special Topics in Control and Automation (3-0:3) Advanced and emerging topics are selected from the area of Control and Automation. Contents of the course will be provided one semester before it is offered Robust Feedback Control (3-0:3) Elements of robust feedback control theory. Norms of signals and systems. Performance specifications. Stability and performance of feedback systems. Performance limitations. Model uncertainty and robustness. Parametrization of stabilizing controllers. Loop transfer recovery robust design. H-infinity control and filtering. 208

4 Predictive Control (3-0:3) Predictive control concept. Process models and prediction. Optimization criterion. Predictive control law. Performance and robustness. Minimum cost horizon. disturbance model. Overview of well-known predictive controllers. Tuning of predictive controller design parameters. Predictive control with output constraints. Implementation issues. Industrial case studies Advanced Power Electronics (3-0:3) Review of power semiconductor devices: thyristors, GTO, power transistor, and MOSFET. Power control converters. AC voltage Controllers, PWM techniques, Multilevel inverters, Drive specifications. Rectifier control of DC motors. Fully controlled single-phase and three-phase drives. Multiquadrant operation of DC motors. Closed-loop control of DC motors. Induction motors by voltage controllers. Frequency controlled induction motor drives. Slip power control. Self-controlled synchronous motors. Current/voltage source inverter drives. Introduction to microcomputer control of AC and DC drives Special Topics in Power Systems (3-0:3) This course gives a glimpse for several attractive topics for research in power systems along with their subsystems. The course covers Flexible AC Transmission Systems, (FACTS), controllers and how these controllers are operated to improve transient stability, and control the active and reactive power in the transmission systems. The course also presents the power quality concepts, problems and phenomena along with their detection and mitigation means and tools. Finally, the course provides information for recent distributed generation systems and their integration with the power grids Linear Multivariable Control System (3-0:3) State space representation of systems. Linear algebra background. Modeling of multivariable systems. Realization theory. Controllability and observability. Minimality. Stability. State feedback and Estimation. Separation theorem. Output Feedback. Compensation Communication Systems Engineering (3-0:3) Representation of signals. Spectral density and autocorrelation. PAM and PCM systems. Detection of binary and M-ary signals in Gaussian noise. Matched filter and correlator receivers. Pulse shaping. Band pass modulation and demodulation techniques. Error performance for binary and M-ary systems. Spectral analysis of digital signals. Communication link analysis. College of Engineering Detection and Estimation (3-0:3) Binary and M-hypotheses Detection techniques: Maximum likelihood, Newman Pearson, Minimum probability error, Maximum a posteriori probability, Bayes decision and mini-max detection. Parameter estimation: weighted least squares, BLUE, Maximum likelihood, Mean square estimation. Signal estimation and filtering: Wiener filtering, Kalman filtering and estimation. Simultaneous detection and estimation. Application to system identification and communication systems Information Theory (3-0:3) Measures of information, Entropy, Source Coding theory, Lossless data compression, Huffman Codes, Ziv-Lempel and Elias Codes, Arithmetic Codes, Run-length Encoding, Sources with memory, Lossy data compression, Rate distortion theory, Mutual Information, Memoryless, channels, Channel capacity, Channel coding theory, Differential Entropy, Capacity of AWGN channels. 209

5 Error Control Codes (3-0:3) Finite field arithmetic, Linear codes, Block codes, Cyclic codes, BCH and Reed-Solomon codes, Encoding and decoding methods, Performance analysis of block and cyclic codes, Convolutional codes, Trellis representation, The Viterbi algorithm, Performance analysis of convolutional codes, Coded modulation, Turbo codes Special Topics in Communications (3-0:3) Advanced and emerging topics are selected from the area of communications. Contents of the course will be provided one semester before it is offered Computer Networks (3-0:3) Network technologies, Packet switching, Cell switching, Switching and routing, Packet switch architectures, Internet routers, Network interface, Protocol processing, Network control, Traffic management, Congestion control Mobile Computing (3-0:3) The course discusses the convergence of wide-area wireless networking and mobile telephony to support ubiquitous access to information, anywhere, anyplace, and anytime. Topics include Mobile-IP, Ad-hoc networks, Local connectivity, 3G- wireless networks, Approaches to building mobile applications (e.g., mobile client/server, thin client, proxy architectures, and disconnected operation) and mobile e-commerce Advanced Electronics (3-0:3) The Advanced electronics will cover high frequency circuits with special attentions to integrated circuits at both transistor and system levels. The course include high frequency analog building blocks like the current conveyors and the current feedback operational amplifiers, high-speed amplifiers and tuned amplifiers, high frequency oscillators and the phase locked loop (PLL). All the course topics will be analyzed and designed based on intuitive design methods, physical understanding, quantitative performance evaluation using both hand calculation and simulation, and technology limitations Integrated Circuit Fundamentals (3-0:3) Basic integrated circuit design & process technology, Design of simple analog & digital IC components in Bipolar & MOS technology. Modeling & simulation of integrated circuits, SPICE simulation, Fundamentals of Photo-lithography, Basic integrated circuit layout techniques, Applications & types of IC chips Analog IC Design (3-0:3) The course serves as an advanced course for electronics students in analog integrated circuits (IC) design. The course will focus on conventional and modern analog building blocks for analog signal processing in BJT and MOS technology both in continuous time and discrete time applications. The course include analog multipliers, the opamp applications in active filters, op-amp non-idealities, Nonlinearity cancellation of the MOS transistors, MOS-C Continuous time filters, Switched-C Circuits, and High frequency analog blocks ( ex: Current Conveyors and current feedback amplifiers) Non-linear Circuits Analysis and Design (3-0:3) This course is an advanced course in electronics aiming at studying nonlinear modeling and analysis techniques for nonlinear circuits as well as their applications. Topics that are covered include, equilibrium points, stability analysis, nonlinear driving point characteristics, hysteresis, nonlinear differential equation models, nonlinear transistor models, sinusoidal and relaxation oscillators analysis and design, analog multipliers, introduction to nonlinear dynamics and chaos. 210

6 Advanced Optoelectronics (3-0:3) The course serves as an advanced course for optoelectronics course of undergraduate students. The course topics include variety of different subjects related to the physics and operating characteristics of optoelectronic semiconductor devices. This include a detail discussion of the design and operation of optical LEDs, the basic physics and operation of lasers and photodetectors, details of the basic physics and operation of solar cells, the design and operation of optoelectronic modulation and switching devices, and design of optoelectronic integrated circuits Digital Signal Processing (3-0:3) Classification of discrete-time signals and systems. Basic and lattice structures, Finite-word length effects. Discrete Fourier Transform and its efficient implementations. Introduction to spectral analysis. FIR and IIR filter design techniques: Windowing techniques, Analog-to-Digital transformation techniques, Computer-aided design techniques Pattern Recognition (3-0:3) Decision functions. Distance classification. Clustering algorithms. Pattern classification by likelihood, Deterministic pattern classifier, Supervised and unsupervised classification, Statistical pattern classifier. Feature selection. Neural network approach to pattern recognition. Applications to engineering and machine vision Speech Processing (3-0:3) Speech analysis, Digital processing of wave forms, Waveform coding, Parametric coding of speech: linear predictive coding, Text-to-Speech (TTS) synthesis, Stochastic modeling of speech signals, Pattern recognition and its application to speech, Speech recognition and its applications, Speaker recognition and its applications, emotion/talking condition recognition and its applications, and the latest developments in the different areas of speech Image Processing and Applications (3-0:3) Two-dimensional systems and mathematical preliminaries. Perception and human vision systems. Sampling and quantization. Image transforms. Image representation by stochastic models. Image data compression, enhancement, filtering, restoration. Reconstruction from projection. Analysis and computer vision. College of Engineering Special Topics in Signal and Image Processing (3-0:3) Advanced and emerging topics are selected from the area of Signal and Image Processing. Contents of the course will be provided one semester before it is offered Adaptive Filtering (3-0:3) Introduction to adaptive signal processing. Fundamentals of adaptive filter theory. The LMS algorithm, LMS-based algorithms. Conventional RLS adaptive filtering. Adaptive lattice-based RLS algorithms. Fast algorithms. Implementation issues. Adaptive IIR filters. HOS-based adaptive filtering. Introduction to nonlinear filtering. Applications to echo cancellation, equalization, noise canceling and prediction Wavelet and Time Frequency Signal Processing (3-0:3) Cosine transform and short-time Fourier transform, Analysis of filter banks and wavelets, Sub-band and wavelet coding, Multirate signal processing, Wavelet transform, Daubechies wavelets, Orthogonal and biorthogonal wavelets, Time-frequency and time-scale analysis, Design methods, Applications of wavelets to audio and image compression, Medical imaging, Geophysics, Scientific Visualization (Cross listed with ) Neural Networks and Applications (3-0:3) Introduction, background and biological inspiration. Survey of fundamental methods of artificial neural networks: single and multi-layer networks; Perceptions and back propagation. Associative memory and statistical networks. Supervised and unsupervised learning. Merits and limitations of neural networks. Applications. 211

0402663 (Cross listed with 0403632) Computer Vision (3-0:3) Image formation, image representation and display, image processing (smoothing, enhancement, edge detection, filtering), convolution,

7 (Cross listed with ) Computer Vision (3-0:3) Image formation, image representation and display, image processing (smoothing, enhancement, edge detection, filtering), convolution, Gaussian masks, scale, space and edge detection, Feature extraction, Hough transforms, stereoscopic vision and perspective projection, motion, active contour models Independent Studies in Electrical and Electronics Engineering (3-0:3) The student is expected to carry out an independent study on a current issue in a selected area of Electrical-Electronics Engineering. This study is to be supervised by a faculty member and requires the approval of the department. The student is required to produce a formal report, which will be evaluated by his instructor Graduate Seminar (3-0:3) Students are required to attend seminars given by faculty members, visitors, and fellow graduate students. Each student is also required to present a seminar outlying the research topic of the master thesis Master Thesis (3-0:3) The student has to undertake and complete research topic under the supervision of a faculty member. The thesis work should provide the student with an in-depth understanding of a research problem in Electrical-Electronics Engineering. It is expected that the student, under the guidance of the supervisor, will be able to conduct research somewhat independently, and may also be able to provide solution to that problem. 212

Masters of Engineering in Electrical Engineering Course Syllabi ( ) City University of New York--College of Staten Island

City University of New York--College of Staten Island Masters of Engineering in Electrical Engineering Course Syllabi (2017-2018) Required Core Courses ELE 600/ MTH 6XX Probability Theory and Stochastic