Master of Science [M.Sc] Electrical and Computer Engineering From Khalifa University
course_name

Master of Science [M.Sc] Electrical and Computer Engineering

Khalifa University

Back to Courses

Program Overview

The degree of Master of Science in Electrical and Computer Engineering (MSc in ECE) is awarded for successfully completing the requirements of a program of study, which includes taught courses as well as thesis. The thesis is an independent investigation of specialized areas within the general field of electrical and computer engineering and associated disciplines.
 

The MSc in ECE gives candidates the opportunity to deepen their knowledge in the broad field of ECE and contribute to the process of discovery and knowledge creation through the conduct of original research. Candidates for this degree are taught and supervised by experienced faculty and are expected to demonstrate initiative in their approach and innovation in their work. In addition to successfully completing the taught course component of the program, candidates prepare and present a thesis on their chosen area. Research may be undertaken in several topics corresponding to the areas of focus identified by the University.

0
LocationPin
  Location

Abu DhabiUnited Arab Emirates

DurationIcon
  Course Duration

24 Months

LocationPin
  Tuition Fee

AED 89,181

 Score

IELTS: 6.5 GRE: 300 TOEFL: 91

Confused?

We can help you

phone

The MSc in Electrical and Computer Engineering (ECE) program consists of a minimum of 36 credit hours. The required program credits are distributed as follows: 12 credits of Program Core courses (including 3 credits of engineering mathematics courses), 12 credits of Program Elective courses, and 12 credits of ECE Master’s Thesis work.  A student may organize the selection of the elective courses and the master’s thesis topic to follow a specialization track within the broad field of ECE.  In such cases, the track will be noted on the student’s transcript.  The table below presents a summary of the MSc in ECE degree program structure and requirements.  All the MSc in ECE program courses, with the exception of the Research Seminar and the Master’s Thesis, have a credit rating of three credits each.
 

Summary of MSc in ECE Degree Program Structure and Requirements
 

Category

Credits Required

Program Core

12

Program Electives

12

ECE Master’s Thesis

12

Total

36

 

PROGRAM REQUIREMENTS
 

Students seeking the degree of MSc in ECE must successfully complete a minimum of 36 credited hours as specified in the categories detailed in this section, with a minimum Cumulative Grade Point Average (CGPA) of 3.0.
 

PROGRAM CORE
 

The MSc in ECE degree program core requires a minimum of 12 credits, consisting of 3 credits of engineering mathematics, 9 credits of ECE core courses, and the research seminar course which has zero credit rating.  The courses for each one of the core categories are specified below.
 

1. Engineering Mathematics Courses (3 credits)
 

Students must select at least one course from the list below:

  • ENGR 602 Engineering Numerical Methods

  • ENGR 605 Systems Optimization

  • ENGR 606 Advanced Engineering Mathematics
     

2. ECE Core Courses (9 credits):
 

 

Students must select at least three courses from the list below:

  • ECCE 610 Digital Signal Processing

  • ECCE 620 Real-Time Embedded Systems

  • ECCE 630 Advanced Computer Networks

  • ECCE 635 Deep Learning System Design

  • ECCE 650 Linear Systems

  • ECCE 660 Power Systems Analysis

  • ECCE 661 Power Electronics

  • ECCE 671 Fabrication of Nano Devices (if student taken ECCE 495 Fabrication course or equivalent) else ECCE 670 Micro/Nano Processing Technologies

  • ECCE 672 Principles and Models of Semiconductor Devices
     

3. ENGR 695 Seminar in Research Methods (0 credits)
 

PROGRAM ELECTIVES

Students must complete a minimum of 12 credits of electives.  The list of electives that students can select from includes any core courses not used to meet the ECE Core requirement above as well as those listed below. Students can also select up to two elective courses (6 credits) from other MSc programs in the College of Engineering at KU subject to the approval of their research advisor. 

  • ECCE 611 Advanced Digital Signal Processing

  • ECCE 612 Multimedia Processing

  • ECCE 621 Digital ASIC Design

  • ECCE 622 RF and Mixed-Signal Circuit Design

  • ECCE 623 High-Speed Communication Circuits

  • ECCE 624 Analog Integrated Circuits

  • ECCE 625 Digital Integrated Circuit Design

  • ECCE 627 Computer-Aided Design of Microelectronic Systems

  • ECCE 628 Computer Architecture

  • ECCE 631 Blockchain Fundamentals and Applications

  • ECCE 632 Advanced Operating Systems

  • ECCE 633 Machine Vision and Image Understanding

  • ECCE 636 Human Computer Interaction

  • ECCE 637 Parallel Programming

  • ECCE 640 Communication Systems Design

  • ECCE 641 Wireless Communications Systems

  • ECCE 642 Broadband Communication Networks

  • ECCE 643 Radar Systems

  • ECCE 652 Modeling and System Identification

  • ECCE 653 Advanced Digital Control Systems

  • ECCE 654 Adaptive Control

  • ECCE 655 Artificial Intelligence for Control Engineering

  • ECCE 656 Nonlinear Control

  • ECCE 657 Process Instrumentation

  • ECCE 658 Autonomous Robotic Systems

  • ECCE 659 Modeling and Control of Robotic Systems

  • ECCE 662 Electric Drives

  • ECCE 663 Distribution Systems Design and Operation

  • ECCE 664 Distributed Generation

  • ECCE 665 Electric Power Quality

  • ECCE 666 Power System Protection

  • ECCE 667 High Voltage Engineering

  • ECCE 668 Electric Machines

  • ECCE 669 Power System Operation

  • ECCE 671 Fabrication of Nano Devices

  • ECCE 673 Fundamentals of Photonics

  • ECCE 674 Semiconductor Optoelectronic Devices

  • ECCE 675 Nanoscale Integrated Circuits Technology

  • ECCE 694 Selected Topics in ECCE

  • COSC 606 Machine Learning

  • COSC 604 Artificial Intelligence

  • CSEC 630 Artificial Intelligence Techniques for Cyber-Security

  • ENGR 610 Risk, Reliability and Uncertainty in Engineering Systems
     

MASTER’S THESIS
 

ECCE 699 MASTER’S THESIS (12 CREDITS)
 

A student must complete a master’s thesis that involves creative research-oriented work within the broad field of ECE, under the direct supervision of the main advisor, who must be a full-time faculty in the Electrical Engineering and Computer Science Department, and at least one other full-time faculty who acts as co-advisor. The research findings must be documented in a formal thesis and defended successfully in a viva voce examination.  Furthermore, the research should lead to publishable quality scholarly articles.
 

PROGRAM TRACKS
 

A student may select a group of elective courses to form a specialization track within the MSc in ECE program.  The track will be noted on the student’s academic record (transcripts), provided that the student completes:
 

  • A minimum of 9 credits from the group of courses designated by the track.

  • A master research thesis within the domain of the track.
     

The tracks supported by the MSc in ECE program and the required courses for each of the tracks are set out below.
 

Communication and Signal Processing Systems

ECCE 611

Advanced Digital Signal Processing

ECCE 612

Multimedia Processing

ECCE 633

Machine Vision and Image Understanding

ECCE 640

Communication Systems Design

ECCE 641

Wireless Communication Systems

ECCE 642

Broadband Communication Networks

ECCE 645

Stochastic Processes, Detection, and Estimation

Embedded Systems

ECCE 621

Digital ASIC Design

ECCE 622

RF and Mixed-Signal Circuit Design

ECCE 623

High-Speed Communication Circuits

ECCE 624

Analog Integrated Circuits

ECCE 625

Digital Integrated Circuit Design

ECCE 627

Computer-Aided Design of Microelectronic Systems

ECCE 628

Computer Architecture

ECCE 629

Hardware Accelerators for Artificial Intelligence

ECCE 675

Nanoscale Integrated Circuits Technology

Micro and Nano Systems

ECCE 622

RF and Mixed-Signal Circuit Design

ECCE 623

High-Speed Communication Circuits

ECCE 624

Analog Integrated Circuits

ECCE 625

Digital Integrated Circuit Design

ECCE 670

Micro/Nano Processing Technologies

ECCE 671

Fabrication of Nano Devices

ECCE 672

Advanced Microelectronics Devices

ECCE 673 

Fundamentals of Photonics

ECCE 674

Semiconductor Optoelectronic Devices

ECCE 675

Nanoscale Integrated Circuits Technology

Computing Systems

ECCE 628

Computer Architecture

ECCE 632

Advanced Operating Systems

ECCE 631

Blockchain Fundamentals and Applications

ECCE 633

Machine Vision and Image Understanding

ECCE 636

Human Computer Interaction

ECCE 637

Parallel Programming

Artificial Intelligence

COSC 606

Machine Learning

ECCE 658

Autonomous Robotics Systems

COSC 604

Artificial Intelligence

ECCE 633

Machine Vision and Image Understanding

ECCE 655

Artificial Intelligence for Control Engineering

CSEC 630

Artificial Intelligence Techniques for Cyber-Security

Robotics, Controls, and Autonomous Systems

ECCE 633

Machine Vision and Image Understanding

ECCE 653

Advanced Digital Control Systems

ECCE 654

Adaptive Control

ECCE 655

Artificial Intelligence for Control Engineering

ECCE 656

Nonlinear Control

ECCE 657

Process Instrumentation

ECCE 658

Autonomous Robotic Systems

ECCE 659

Modeling and Control of Robotic Systems

Electrical Power Engineering

ECCE 662

Electric Drives

ECCE 663

Distribution Systems Design and Operation

ECCE 664

Distributed Generation

ECCE 665

Electric Power Quality

ECCE 666

Power System Protection

ECCE 667

High Voltage Engineering

ECCE 668

Advanced Electric Machines

ECCE 669

Power System Operation

 

ECCE 610 Digital Signal Processing (3-0-3)

Prerequisite: Undergraduate knowledge of digital signal processing and linear algebra.

This course is meant to be a second course in discrete-time signal processing. It provides a comprehensive treatment of signal processing methods to model discrete-time signals, design optimum digital filters, and to estimate the power spectrum of random processes. It includes topics such as signal models, parametric and nonparametric power spectrum estimation, optimal filters, the Levinson recursion, lattice filters, and Kalman filter.

 

ECCE 611 Advanced Digital Signal Processing (3-0-3)

Prerequisite: ECCE 610 Digital Signal Processing (or equivalent).

Statistical Signal Processing; Adaptive Filtering; Time-Frequency and Multiscale Signal Processing; ADSP Applications.

 

ECCE 612 Embedded Digital Signal and Image Processing (3-0-3)

Prerequisite: Undergraduate course in Digital Signal Processing (or equivalent).

This course introduces the students to the design, prototyping, and verification of real-time, embedded digital signal and image processing systems. Such systems have wide applications in communications, multimedia, surveillance, control, robotics, machine vision, remote sensing, biomedical signal processing and medical imaging. The course will focus on the development of working knowledge and advanced skills in the use of DSP hardware platforms for the implementation of a variety of real-time, embedded signal and image processing systems selected from the applications areas mentioned above. An advanced commercial DSP development kit will be used for hands-on experience and course lab and projects.

 

ECCE 620 Real-Time Embedded Systems (3-0-3)

Prerequisite: Undergraduate knowledge of microprocessor/microcontroller systems.

The design of embedded systems is often challenged by soft or hard timing requirements of the application and by the limited computational power of available platforms. This course addresses design aspects of real-time embedded systems and their applications.

 

ECCE 621 Digital ASIC Design (3-0-3)

Prerequisite: Undergraduate knowledge of digital logic design.

ASIC design flow: role of HDL in ASIC design. HDL coding style for synthesis. ASIC testing and testbench creation. Clocking in ASIC design. ASIC libraries. Constraints for synthesis. Static timing analysis (STA), statistical timing analysis and chip variation. Floor-planning. Place and Route of ASICs. Parasitics, noise, and cross talk. Chip filling and metal filing. Timing closure and tapeout. Fault models, test pattern generation and design for testability techniques. The course will use state of the art EDA (Electronic Design Automation) tools such as Cadence and Synopsys.

 

ECCE 622 RF and Mixed-Signal Circuits Design (3-0-3)

Prerequisite: Undergraduate knowledge of electronic circuits and devices.

The course covers most relevant topics in the design of the RF receiver architectures in CMOS technology. It also discusses issues related to the design of mixed-signal circuits. This is addressed in the context of the common wireless standards and modulation schemes.

ECCE 623 Digital Systems Design with FPGA (3-0-3)

Prerequisite: Undergraduate course in Digital Systems or Equivalent

This course introduces the students to the design, prototyping, and verification of sophisticated digital systems using hardware description languages. The focus of the course will be on the development of modular digital designs and their architectural explorations to meet timing, area, and power design specifications and to use IP cores to design advanced digital systems. Examples of digital systems covered in the course include codecs, memory controllers, bus interfaces, and various accelerators such as crypto and linear algebra modules. The FPGA prototyping of such advanced digital systems will also be covered as part of the course lab assignments.

 

ECCE 673 Secure Embedded System Design (3-0-3)

Prerequisite: Undergraduate course in Embedded Systems, Microprocessor Systems, Information Security, or Digital VLSI.

This course covers information security as it relates to both the digital design of hardware for cryptographic primitives and the design of temper-resistant hardware systems. The course will appeal to graduate students who are interested in acquiring a multi-disciplinary perspective on the design of secure information systems, including fundamental algorithms, their software models, and their hardware implementation.

 

ECCE 625 Digital Integrated Circuits Design (3-0-3)

Prerequisite: Undergraduate knowledge of digital logic design and electronic circuits and devices.

Analysis and design of digital integrated circuits. Fabrication processes, device characteristics, parasitic effects static and dynamic digital circuits for logic and memory functions. Process technology scaling and challenges, emerging technology and its impact on digital integrated circuits. Impact of process variation on circuit behavior. Design building block of digital system including memory, combinational, sequential, and IO. System integration options (TSV, SOC, SOP). Noise and noise sources in digital systems. Interconnect and its impact on digital design performance, power, and area. Synchronous and A synchronous design, clock generation and distribution. The course will use state of the art EDA (Electronic Design Automation) tools such as Cadence and Synopsys.

 

ECCE 628 Computer Architecture (3-0-3)

Prerequisite: ECCE 621 Digital ASIC Design(or equivalent).

This course provides students with solid working knowledge of modern computer architecture and design. It covers the organization and architecture of computer systems hardware; the hardware/software interface; instruction set architectures; addressing modes; register transfer logic; processor design; pipelining; memory hierarchy; caches; virtual memory; input/output; and bus architectures.

 

ECCE 629 Hardware Accelerators for Artificial Intelligence (3-0-3)

Prerequisite: Undergraduate knowledge of digital logic design, microprocessor/microcontroller systems or embedded systems.

This course provides a hands-on introduction to the computational structures that are common to cognitive systems and to their hardware implementations on energy-and-area-constrained nodes. The course will explore the impact of including cognitive functions in existing devices such as low-cost microcontrollers and microprocessors as well as the design of novel constrained nodes with built-in cognitive functions.

ECCE 630 Advanced Computer Networks (3-0-3)

Prerequisite: Undergraduate knowledge of computer networks or communications networks.

Modern and popular computer network technologies, protocols and services. Next Generation Networks, Triple-play services, Network management, Firewall and Intrusion detection, Wireless ad-hoc networks. Performance analysis, modeling and simulation of computer networks.

 

ECCE 631 Blockchain Fundamentals and Applications (3-0-3)

Prerequisite: Undergraduate knowledge of computer networks or communications networks.

Introduction to cryptocurrencies, wallets, and Blockchain; Blockchain key features, benefits, and popular use cases; Blockchain fundamentals, protocols, algorithms, and underlying infrastructure Building Ethereum and Hyberledger blockchains; Decentralized applications (DApps); Smart contracts; Trusted Oracles; Decentralized storage; Designing and architecting blockchain-enabled systems and solutions for applications in IoT, AI, Supply Chain Management and Logistics, Healthcare, Smart Grids, 5G networks, Telecommunication, etc. Cost and Security Analysis; Limitations and open research challenges in Blockchain.

 

ECCE 632 Advanced Operating Systems (3-0-3)

Prerequisite: Undergraduate knowledge of operating systems.

The course presents the main concepts of advanced operating systems (parallel processing systems, distributed systems, real time systems, network operating systems, and open source operating systems), including the hardware and software features that support these systems.

 

ECCE 633 Machine Vision and Image Understanding (3-0-3)

Prerequisite: Undergraduate knowledge of complex variables and transforms, programming, and signals and systems (or equivalents).

The course covers the fundamental principles of machine vision and image processing techniques. This includes multiple view geometry and probabilistic techniques as related to applications in the scope of robotic and machine vision and image processing by introducing concepts such as segmentation and grouping, matching, classification and recognition, and motion estimation.

 

ECCE 635 Deep Learning Systems Design (3-0-3)

Prerequisite: Undergraduate knowledge of Artificial Intelligence (or equivalent).

High level introduction to deep learning concepts and essential contexts, deep learning computational framework, system implementation practicalities, machine learning workflow, practical classification problems for different data modalities, state of the art deep learning models.

 

ECCE 636 Human Computer Interaction (3-0-3)

Prerequisite: Undergraduate knowledge of software engineering.

This course covers the principles of human-computer interaction, the design and evaluation of user interfaces. Topics include an overview of users’ needs and how cognitive aspects affect the design of user interfaces; the principles and guidelines for designing usable user interfaces, with emphasis on the different and novel interactions and trends in HCI; the interaction evaluation methodologies and techniques that can be used to measure the usability of software. Other topics may include World Wide Web design principles and tools, crowdsensing/sourcing, speech and natural language interfaces, and virtual reality interfaces.

 

ECCE 637 Parallel Programming (3-0-3)

Prerequisite: ECCE 316 or ECCE 341 or ECCE 342. Undergraduate knowledge of programming in C, C++, Java or similar, data structures and algorithms, and basic computer architecture.

This course is a hands-on introduction to parallel computing for MSc students with emphasis on the most common and accessible parallel architecture, namely, the Graphics Processing Unit (GPU). The course will introduce students to modern GPU architectures and the fundamental concepts of parallel computing, including data parallelism, scalable execution, memory and data locality, multithreading, and synchronization.

The course will also cover some of the most common parallel patterns such as convolution, prefix sum, graph search, and sparse matrix multiplications, along with their GPU implementations. The case study of deep convolutional neural networks will be covered in detail. NVIDIA’s CUDA programming environment will be used throughout the course for homework assignments and the course project.

 

 

ECCE 640 Communication Systems Design (3-0-3)

Prerequisite: Undergraduate knowledge of digital communications (or equivalent).

This course covers the main concepts in digital data transmission. The topics covered will provide the student with thorough understanding of the algorithms and techniques used to design digital transmitters and receivers to a high degree of fidelity.

 

ECCE 641 Wireless Communications Systems (3-0-3)

Prerequisite: Undergraduate knowledge of wireless communications (or equivalent).

This course covers advanced topics in wireless communication systems and communication theory. The goal of this course is the design and analysis of fundamental and emerging topics in wireless communication systems, e.g., multiple-input-multiple-output (MIMO) and multi-carrier systems. Further topics include, but not limited to, capacity analysis of fading channels, adaptive modulation and coding, MIMO-orthogonal frequency division multiplexing (OFDM), and cooperative communications.

 

ECCE 642 Broadband Communication Networks (3-0-3)

Prerequisite: Undergraduate knowledge of computer networks and/or wireless communications (or equivalent).

The course is to present the key facets of broadband communication networks. The main topics include: introduction to networks, probabilistic description of networks, queuing analysis, and layering; mobile broadband-enabling technologies; LTE-Advanced; 5G and beyond; and hybrid terrestrial/satellite networks.

 

ECCE 643 Radar Systems (3-0-3)

Prerequisite: Undergraduate courses in electromagnetics, and probability and statistical inference (or equivalents).

The course introduces the fundamentals of modern radar systems design and operation. The covered topics include the Radar equation, propagation environment, radar cross-section, clutter characteristics, detection, tracking and parameter estimation, radar antenna, transmitter and receiver. The course covers Pulsed, Doppler, and FMCW radars as the well as Synthetic Aperture Radars (SAR).

 

ECCE 644 Radio Frequency Measurements (2-1-3)

Prerequisite: Undergraduate course in Electromagnetics (or equivalent).

The course covers experimental characterization of RF and high-speed digital electronics using modern frequency- and time-domain measurement techniques. Advanced RF network, spectrum, field, and noise analysis will be covered. It offers in-depth treatment of RF measurement concepts, experimental methods, and test equipment. The course is augmented with laboratory sessions and it follows hands-on learning approach.

 

ECCE 645 Stochastic Processes, Detection, and Estimation (3-0-3)

Prerequisite: Undergraduate knowledge of probability and statistics, and discrete mathematics, (or equivalent).

This is a graduate-level course to introduce some fundamentals of stochastic processes, detection, and estimation involving signal models in which there is some inherent randomness. The concepts that we’ll develop are extraordinarily rich, interesting, powerful, and form the basis for an enormous range of algorithms used in diverse applications. The material in this course constitutes a common foundation for work in the statistical signal processing, communication, and control areas.

 

ECCE 650 Linear Systems (3-0-3)

Prerequisite: Undergraduate knowledge of feedback control systems (or equivalent).

State space methods, Theory of multivariable systems, Jordan canonical forms, Transformation matrices, Realization theory, Controllability, Observability, Stability, Robust stability, State feedback controllers, Full and reduced order observers, Output feedback controllers, Compensation, Decoupling and model matching, Introduction to optimal control.

 

ECCE 653 Advanced Digital Control Systems (3-0-3)

Prerequisite: ECCE 650 Linear Systems (or equivalent).

Classical and modern digital control system analysis and design techniques. Various discrete time controllers are designed including series compensation methods, PID-controllers, pole placement, linear quadratic optimal control, optimal state estimation and Kalman filters, Use of computer-aided analysis and design tools.

 

ECCE 654 Adaptive Control (3-0-3)

Prerequisite: ECCE 650 Linear Systems (or equivalent).

Introduction to various approaches to adaptive control, direct and indirect adaptive control schemes such as model reference adaptive control, auto-tuning, gain scheduling, and self-tuning regulators, benchmark comparison of adaptive control designs, convergence, stability and robustness, typical industrial applications.

 

ECCE 655 Artificial Intelligence for Control Engineering (3-0-3)

Prerequisite: Undergraduate knowledge of control systems and programming.

Intelligent control strategies: Fuzzy logic control, Neural networks, Optimization control techniques including Genetic algorithms, Swarm intelligence, and applications to engineering optimization problems.

 

ECCE 656 Nonlinear Control (3-0-3)

Prerequisite: ECCE 650 Linear Systems (or equivalent).

Introduction to nonlinear control systems by means of analysis, simulation, and synthesis. The course will include phase plane analysis and classification of equilibrium points, linearization, Lyapunov method, Passivity & input-output stability, Stability of feedback systems, feedback linearization, tracking, regulation, disturbance rejection and Observers, and Describing functions.

 

ECCE 657 Sensor systems (3-0-3)

Prerequisite: Undergraduate courses in Electronics and Microprocessor/Microcontroller or equivalent.

This course presents different types of sensors used in robotics, autonomous navigation, process industry, power systems, and biomedical engineering. This includes but not limited to temperature, force, torque, position, velocity, vibration, electric power, medical, and gas/liquid leak sensors. It also presents various signal conditioning techniques used for front-end sensor interfacing. Hardware and algorithmic solutions used to minimize the power consumption for seamless integration in wireless sensor networks and in IoT device will also be covered. Digital interfacing with microcontrollers and microprocessors to fill the gap for understanding various embedded sensor systems will also be addressed.

 

ECCE 658 Autonomous Robotic Systems (3-0-3)

Prerequisite: Undergraduate knowledge of complex variables and transforms and feedback control systems (or equivalents).

The course addresses some of the main aspects of autonomous robotic systems. This includes artificial intelligence, algorithms, and robotics for the design and practice of intelligent robotic systems. Planning algorithms in the presence of kinematic and dynamic constraints, and integration of sensory data will also be discussed.

 

ECCE 659 Modeling and Control of Robotic Systems / Cross-Listed with MEEN 659 (3-0-3)

Prerequisite: Undergraduate knowledge of complex variables and transforms and feedback control systems (or equivalents).

The course covers the theory and practice of the modeling and control of robotic devices. This includes kinematics, statics and dynamics of robots. Impedance control and robot programming will also be covered. Different case-studies will be presented to support hands-on experiments.

 

ECCE 660 Power System Analysis (3-0-3)

Prerequisite: Undergraduate knowledge of power systems analysis (or equivalent).

Power system modelling; Advanced load flow techniques; Symmetric faults on generators; Single machine and Multi-Machine transient stability; Transmission line transient analysis and Power systems transients.

 

ECCE 661 Power Electronics (3-0-3)

Prerequisite: Undergraduate knowledge of power electronics (or equivalent).

The objectives of this course are to teach the principles of power electronics devices; introduce students to different electronics devices and converters and design of converters. The course includes: the application of electronics to energy conversion and control. Modeling, analysis and control techniques. Design of power circuits including inverters, rectifiers, and dc-dc converters.

 

ECCE 662 Electric Drives (3-0-3)

Prerequisite: ECCE 650 Linear Systems, ECCE 661 Power Electronics, and undergraduate knowledge of electric machines.

Selection of drives based on motor and load characteristics, modeling, simulation and control of electric drives, regenerative braking, and power quality issues related to electric drives. High power drives and current topics in electric drives.

 

ECCE 663 Distribution Systems Design and Operation (3-0-3)

Prerequisite: ECCE 660 Power System Analysis (or equivalent).

Distribution feeders configurations; voltage levels; Voltage drop and power loss calculations in distribution networks; Distribution feeder modeling and analysis; Distribution Networks planning and reliability; impact of integrating distributed energy resources.

 

ECCE 664 Distributed Generation (3-0-3)

Prerequisite: ECCE 660 Power System Analysis (or equivalent).

The course provides up-to-date knowledge about the technical issues related to distributed generation. The course will provide an introduction to DG and their impacts on power system studies including load flow, short circuit and transient stability. The students will also learn how to perform studies, relevant to DG  technology, which include protective device coordination and electricity market operation. By the end of the course, the students should have developed an understanding of some of the current challenges associated with the integration of DG in distribution systems and should be capable, through the tools presented in the course, of exploring new strategies to mitigate the impacts of DG in order to facilitate widespread integration of DG in distribution systems.

 

ECCE 665 Electric Power Quality (3-0-3)

Prerequisite: ECCE 660 Power Systems Analysis, ECCE 661 Power Electronics (or equivalent).

Introduction to power quality, PQ standards, causes and effects of different power quality phenomena, characteristics and definitions, electrical transients, voltage sags and swells, unbalance, flicker, and harmonics; mitigation techniques, active and passive filters; passive filter design, DSTATCOM, DVR.

 

ECCE 666 Power System Protection (3-0-3)

Prerequisite: ECCE 660 Power System Analysis (or equivalent).

Introduction and general philosophies of power system protection, Symmetrical components, Symmetrical and unsymmetrical fault calculation, CB sizing, Transformer protection, Generator protection, Busbar protection, Line protection, Advanced distance protection, Pilot protection system, System stability and Generator out-of-step protection.

 

ECCE 667 High Voltage Engineering (3-0-3)

Prerequisite: Undergraduate knowledge of electromagnetic, and high voltage engineering (or equivalent).

Materials used in high voltage insulation, including gas insulation and polymeric materials. Mechanisms of breakdown in gases, solids and liquids. Partial Discharge (PD), processes leading to insulation degradation. PD measurement and diagnosis in high voltage equipment. Overvoltages and insulation coordination in high voltage networks. High Voltage circuit breaker technologies. Monitoring of high voltage systems and numerical techniques for electric field computation. Aspects of grounding.

 

ECCE 668 Advanced Electric Machines (3-0-3)

Prerequisite: Undergraduate knowledge of electric machines (or equivalent).

Electromechanical energy conversion, rotating and linear electric machines. Development of analytical techniques for predicting machine characteristics: energy conversion density, efficiency; and of system interaction characteristics: regulation, stability, controllability, and response. Use of electric machines in drive systems. Example problems taken from current research.

 

ECCE 669 Power System Operation (3-0-3)

Prerequisite: ECCE 660 Power Systems Analysis, (or equivalent).

This course deals with modern power system operation and control issues and solution techniques. Topics covered include: Economic dispatch of thermal power generation units, Load frequency control, Unit commitment, Interchange of Power and Energy, Power System Security, Optimal Power Flow, and State Estimation in Power Systems.

ECCE 670 Micro/Nano Processing Technologies (3-0-3)

Prerequisite: Undergraduate knowledge of general chemistry and physics.

This course covers the theory and practice of semiconductor fabrication processing commonly found in the fields of MEMS, electronics and photonics: optical lithography, chemical and physical vapor deposition, spincoating, oxidation and diffusion, layout design, plasma and wet etching, dicing and bonding.

 

ECCE 671 Fabrication of Nano Devices (3-0-3)

Prerequisite: ECCE 670 Micro/Nano Processing Technologies (or equivalent).

The state of the art in the microsystems device fabrication will be covered, from standard CMOS processes to niche advanced prototyping techniques of usage in new areas as photonics, MEMS, OMEMS, thin-film FETs and biosensors. Non-standard techniques such as mixed-lithography, focused ion beam milling, nanostructure self-assembly and interference lithography will also be covered.

 

ECCE 672 Principles and Models of Semiconductor Devices (3-0-3)

Co-requisite: ECCE 670 Micro/Nano Processing Technologies (or equivalent).

The physics of microelectronic semiconductor devices for silicon integrated circuit applications. Carrier generation, transport, recombination, and storage in semiconductors. Physical principles of operation of the p-n junction, heterojunction, metal semiconductor contact, bipolar junction transistor, MOS capacitor, MOS and junction field-effect transistors, and related optoelectronic devices such as CCDs, solar cells, LEDs, and detectors.

 

ECCE 680 Fundamentals of Photonics (3-0-3)

Prerequisite: Electromagnetic wave theory (or permission of instructor).

The field of Photonics describes the use of light to perform functions that are traditionally under the domain of Electronics, such as computing, data storage, information processing and telecommunications. In particular, Silicon Photonics allows the integration of optical and electronic devices on the same integrated microchip. This course covers both fundamental and advanced concepts that are needed for understanding, designing and simulating simple passive building blocks for such photonic integrated circuits (PICs)The course merges optical physics and mathematical tools, including differential equations, differential operators (Laplacian, curl, divergence, gradient), Fourier transforms, coupled-mode theory, and finitedifference time-domain (FDTD) simulations. A quick review of ray and wave optics is presented, along with electromagnetic wave propagation in isotropic media. Planar and two-dimensional dielectric waveguides are then explored, as well as an introduction to photonic crystals. The theory of ring resonators and optical add/drop multiplexers (OADM) is also covered, and simple optical architectures for interconnects, routers and switches are presented. Advanced numerical simulations in MATLAB and MEEP/Lumerical (FDTD software) are also covered. This course is essential for students focusing their research in Photonics.

 

ECCE 681 Semiconductor Optoelectronic Devices (3-0-3)

Prerequisite: ECCE 672 Principles and Models of Semiconductor Devices (or equivalent).

This course covers optical properties of semiconductors; physics of absorption, spontaneous and stimulated emission. Theory and design of semiconductor optoelectronic devices, applications and current state-ofthe-art are covered in depth. Devices covered include photo-detectors (p-i-n, avalanche, MSM), modulators (carrier injection, electro-absorption), light-emitting diodes (LEDs), semiconductor optical amplifiers and semiconductor lasers.

 

ECCE 675 Nanoscale Integrated Circuit Devices and Technology (3-0-3)

Prerequisite: ECCE 672 Principles and Models of Semiconductor Devices (or equivalent).

Practical and fundamental limits to the evolution of the technology of modern MOS devices and interconnects. Modern device architectures and their impact on circuit’s performance. Advanced device materials and associated fabrication challenges and techniques. What are sub-10nm future materials and novel devices to maintain progress in integrated electronics? Impact of nano-scale on device operations, reliability and circuits.

 

ECCE 694 Selected Topics in Electrical and Computer Engineering (3-0-3)

Prerequisite: Will be specified according to the particular topics offered under this course number.

This course covers selected contemporary topics in electrical and computer engineering. The topics will vary from semester to semester depending on faculty availability and student interests. Proposed course descriptions are considered by the Department of Electrical and Computer Science on an ad hoc basis and the course will be offered according to demand. The proposed course content will need to be approved by the Graduate Studies Committee. The Course may be repeated once with change of contents to earn a maximum of 6 credit hours.

 

ECCE 699 Master’s Thesis (minimum 12 credit hours)

Co-requisite: ENGR 695 Seminar in Research Methods, approval of the Department Chair and the Associate Dean for Graduate Studies.

In the Master’s Thesis, the student is required to independently conduct original research-oriented work related to important electrical and computer engineering problems under the direct supervision of a main advisor, who must be a full-time faculty in the Electrical Engineering and Computer Science Department, and at least one other full-time faculty who acts as co-advisor. The outcome of the research should demonstrate the synthesis of information into knowledge in a form that may be used by others and lead to publications in suitable reputable journals/conferences. The student’s research findings must be documented in a formal thesis and defended through a viva voce examination. The student must register for a minimum of 12 credit hours of Master’s Thesis.

 

 

ECCE 701 Power System Modelling and Control (3-0-3)

Prerequisite: Graduate level course in advanced power system analysis.

This course gives depth learning for developing the transient model of power system equipment and FACTS devices. The course covers modeling issues for AC transient, fault, generation units, transformers, Transmission system (OHTL and Cables), FACTS devices, renewable energy systems, distributed generation, power system control as well as power system conceptual studies with practical example serving to illustrate the subject. Several cases will be applied in details to highlight the practical situation encountered in power system.

 

ECCE 703 Embedded Generation Operation and Control (3-0-3)

Prerequisite: Graduate level course in advanced power system analysis.

The course provides an advanced outlook at the technical and economic issues related to distributed generation. A detailed description of the theory of operation of the most dominant renewable energy systems (PV and Wind) will be presented. The impact of DG on the distribution system planning and operation will be presented with emphasis on stochastic planning, Volt/Var control, islanding detection and power quality. A detailed DG connection impact assessment from the regulatory perspective will be presented. The course will focus on advanced techniques and methods used for microgrid operation and control. A detailed economical evaluation for DG integration will be presented.

 

ECCE 706 Power Quality and FACTS Devices (3-0-3)

Prerequisite: Graduate level course in power electronics.

Power Quality is an issue that is becoming increasingly important to power system engineers and electricity consumers at transmission and distribution levels. The worldwide trend of generation of electricity from renewable energy sources, especially connected to low voltage distribution networks, additionally introduces challenges in ensuring adequate quality of power. The course is designed to provide an indepth understanding of the major power quality problems, their analysis and different modern mitigation techniques to overcome the power quality issues.

 

ECCE 710 Analysis of Power Systems Over-voltages and Transients (3-0-3)

Prerequisite: Graduate level course in advanced power system analysis.

This course presents key aspects in analysis of power system transients. It provides students with the theory of numerical simulation tools such as the EMTP and numerical electromagnetic analysis. Procedures and techniques for the determination of transient parameters for the main power components: synchronous machine, overhead line, underground cable, transformer, surge arrester, and circuit breaker. It also presents important aspects in creating an adequate and reliable transient model of each component, including transient and dynamic characteristics of renewable energy systems.

 

ECCE 711 Advanced Power System Grounding and Safety (3-0-3)

Prerequisite: Graduate level course in advanced power system analysis.

The course provides highly specialized material with analytical and computational techniques for the design and testing of grounding systems in high voltage power installations. DC, AC, high frequency and impulse performance of ground electrodes and systems are treated and the course will contain practical elements including laboratory and field-based testing using research-based test equipment.

 

ECCE 714 Application of Heuristic Optimization Techniques to Power Systems (3-0-3)

Prerequisite: Graduate level course in advanced power system analysis.

This course gives an overview of modern heuristic techniques and covers specific applications of heuristic approaches to power system problems, such as optimal power flow, power system scheduling and operational planning, power generation expansion planning, reactive power planning, transmission and distribution planning, and power system control.

 

ECCE 721 Analog Mixed Signal Design Techniques (3-0-3)

Prerequisite: Graduate level course in advanced analog integrated circuits.

This course covers general architecture and circuit level design issues for A/D and D/A converters used in sensors and communication circuits. It introduces different architectures and system level design concepts for A/D and D/A converters followed by circuit level design techniques. System level issues and trade-offs needed for block/circuit level specifications are extensively discussed. The course starts from fundamental concepts like quantization noise, sampling, linearity and will evolve to complete architectures used in A/D and D/A conversion Students will gain a significant amount of experience in simulating A/D and D/A circuits at the transistor level using state of the art EDA (Electronic Design Automation) tools such as Cadence and Synopsys.

 

ECCE 722 Numerical Simulation of Circuits and Systems (3-0-3)

Prerequisite: Graduate level course in engineering mathematical analysis or numerical methods in engineering.

This course covers the theory, algorithms, and best programming practices for the numerical simulation of circuits and systems. Methods for the automatic generation of large-scale circuit netlists are presented, including the nodal, modified nodal, and tableau formulations. Linear DC circuits are solved first using the direct and iterative techniques of numerical linear algebra with emphasis on the sparse nature of the circuit graph. Numerical issues such as stability, pivoting, conditioning, and accuracy are discussed in depth. Next Newton’s algorithm for the DC analysis of non-linear circuits is presented along with the automatic generation of the companion models of nonlinear circuit elements. For transient analysis, the course covers the numerical algorithms for the solution of non-linear ordinary differential equations using first-order and higher-order methods with emphasis on linear multistep methods along with their stability and error theories. Advanced topics related to specialized circuits such as interconnect-dominated or RF circuits will be introduced, and exemplary algorithms from state-of-the-art commercial circuit simulators will be given. This course will appeal to graduate students in both electronics and power engineering.

 

ECCE 723 High Speed Communication Circuits (3-0-3)

Prerequisite: Graduate level course in advanced analog integrated circuits.

This course covers general architecture and circuit level design issues for wired/wireless/fiber optics communication circuits. It introduces different architectures and system level design concepts for wired/wireless/fiber optics communication followed by circuit level design techniques. High Speed Communication Circuits like High Speed Logic are introduced in the first part, followed by Transimpedance amplifiers, Limiters, Laser drivers and Data and Clock Recovery Circuits. In the second part of the course many building blocks needed in a modern Wireless Transceiver are discussed (LNA, PA, Mixers, VCO, PLL’s) and their design equations derived. The specifications for the building blocks is a result of System Level Considerations and trade-offs. Students will gain a significant amount of experience in simulating RF/Broadband circuits at the transistor level using state of the art EDA (Electronic Design Automation) tools such as Cadence and Synopsys.

 

ECCE 730 Advanced Deep Learning (3-0-3)

Prerequisite: Graduate level course in Artificial Intelligence, Deep Learning or Machine Learning.

This course will provide the students with the knowledge and skills required for applying advanced AI models in real-life applications, such as identifying functional modules in biological networks, autonomous driving, and learning from the small number of training samples. Topics include deep social networks, deep reinforcement learning, deep meta-learning, and lifelong learning. It will also cover active research topics in these topics.

 

ECCE 731 Distributed Computing (3-0-3)

Prerequisite: Graduate level course in operating systems and advanced computer networks.

Motivation, models, architectures and enabling technologies of distributed computing systems and their applications. Models for communication, processes, remote invocation, distributed naming, synchronization, replication, consistency, fault tolerance, distributed file systems, and distributed clocks. Cloud and grid computing, storage systems, and peer-to-peer systems. Design and implementation of distributed applications.

 

ECCE 732 Machine Learning and Applications (3-0-3)

Prerequisite: Graduate level advanced data structure, advanced statistics, optimization techniques.

Machine learning, a subset of Artificial Intelligence, aims to create systems that automatically improve with experience. It has many applications, including on-line data analysis, data mining and anomaly detection for cyber-security. Prediction and the study of generalization from data are central topics of Data Analysis and Statistics. These two domains aim at the same goal, that is, gaining insight from data and enabling prediction. This course provides a selection of the most important topics from both of these subjects. The course will start with machine learning algorithms, followed by some statistical learning theory, which provides the mathematical foundation for them. We will then bring this theory into context, providing the transition into Bayesian analysis.

 

ECCE 733 Computer Arithmetic (3-0-3)

Prerequisite: Prior coursework in digital design (or equivalent) and computer organization (or equivalent).

Study the theory and design of high-performance implementations of arithmetic in computers. Various types of numbering systems, computer arithmetic operations: adders, high-speed adders, multi-operand adders, multipliers, and dividers, fixed-point numbers, floating-point numbering system and floating point primitives. Implementation techniques for high-speed VLSI architectures, DSP and cryptographic protocols.

 

ECCE 734 Advanced Computer Architecture (3-0-3)

Prerequisite: Graduate level course in computer architecture.

This course covers advanced topics in computer architecture with focus on emerging advancement in the field. A project will be used to enhance students’ practical capabilities on research, communication, and technical writing.

 

ECCE 735 Advanced Computer Vision Paradigms (3-0-3)

Prerequisite: Graduate level course in image processing and analysis (or equivalent).

Computer systems that automate the analysis and the interpretation of image are getting increasing demand in areas of basic research and industrial applications. Current applications include remote sensing medical diagnosis from radiographic images, control of manufacturing through parts inspection, image recovery from web servers, database management and image archives, automatic digital photo generation, criminal and forensic investigation, to mention just few. This course covers the essential and recent advanced in computer vision paradigms related deep learning and other advance image analysis techniques for solving real work applications.

 

ECCE 736 Advanced Topics in IoT and Blockchain (3-0-3)

Prerequisite: Graduate level course in advanced computer networks.

IoT applications and protocols including MQTT and CoAP; IoT hardware and sensors; IoT deployment within the cloud and fog networks; Cloud platforms for IoT; Democratization of IoT devices using blockchain; Programmability of blockchain using smart contracts; Blockchain-based solutions for IoT; Open research problems in IoT including IoT Security.

 

 

ECCE 737 Network and Information Security (3-0-3)

Prerequisite: Graduate level course in advanced computer networks.

Secure Network Communication: Cryptographic algorithms, Digital Certificates, PKI. Network Entity Authentication and Access Control, Network Reconnaissance, Firewalls, Intrusion Detection and Prevention Systems, Honeynets. Security Protocols: IPsec, SSL, VPN, HTTPS. Application Security: Popular application attacks and countermeasures. Advanced Topics in cyberecurity: IoT Security, Blockchain, Cloud Security, Cyber Physical Systems (CPS) security.

 

ECCE 738 High Performance Computing (3-0-3)

Prerequisite: Graduate level knowledge of operating systems, computer communication, computer architecture, dynamical systems and partial differential equations.

This course is a hands-on introduction to high-performance computing (HPC) for PhD students whose research includes highly complex computational problems. The course will cover the HPC hardware infrastructure and programming models with emphasis on the HPC cluster currently available in KU. The first half of the course will focus on familiarizing the students with the available HPC tools such as the multicore processing nodes, graphics processing nodes, operating system, programming languages, job submission, communication protocols, and programming models. The second half of the course will apply these tools to the solutions of computational problems from various engineering disciplines, including video processing, computer animation, large-scale power grid analysis, deep learning, computational electromagnetics, and computational fluid dynamics. One distinguishing feature of this course is a semester-long project that will result in the implementation of a full, working HPC program and its application to a computational problem in the student’s area of PhD research.

 

ECCE 741 Advanced Digital Communications (3-0-3)

Prerequisite: Graduate level course in communication systems design.

This course discusses the fundamental techniques used in the physical layer of digital communication systems. In particular, it covers topics related to the design and performance of digital communication systems over AWGN and multipath fading channels.

 

ECCE 742 Advanced Concepts in Stochastic Processes, Detection, and Estimation Theory (3-0-3)

Prerequisite: Graduate level course in stochastic processes, detection, and estimation.

The aim of this course is to cover some advanced and important topics in stochastic processes, signal detection, and estimation. The course includes topics such as Detection of Random Signals with Unknown Parameters, Unknown Noise Parameters, Model Change Detection, Complex/Vector Extension, Bayesian Estimation, General Bayesian Estimators, Linear Bayesian Estimators, Estimation for Complex Data and Parameters.

 

 

ECCE 743 Broadband Communication Systems (3-0-3)

Prerequisite: Graduate level courses in communication systems design and/or wireless communications systems.

The course covers topics in single-carrier and multi-carrier OFDM transceivers. It also discusses issues related to multiple-Antenna techniques, relaying and cooperative Communications, spectrum management, the next generation wireless networks, and satellite communication standards.

 

ECCE 744 Optical Wireless Communication Systems (3-0-3)

Prerequisite: Graduate level course in wireless communications systems.

The course covers topics related to optical wireless communications, including, but not limited to, optical light sources and their characteristics, link performance analysis, optical diversity techniques and visible light communications.

 

ECCE 751 Discontinuous Control Systems (3-0-3)

Prerequisite: Graduate level course in advanced linear systems.

Relay feedback and variable-structure systems as two main types of discontinuous control systems. Describing function analysis of self-excited oscillations in discontinuous control systems. Definition of sliding mode, sliding mode in relay and variable structure systems. Multidimensional sliding modes. Design of sliding surface. The use of second Lyapunov’s method for sliding mode control design. Principal and parasitic dynamics. Chattering phenomenon and chattering reduction. LPRS analysis of chattering and closed-loop performance of sliding mode systems. Sliding mode observers. Integral sliding mode. Secondorder and higher-order sliding mode control. Applications of discontinuous control.

 

ECCE 752 Nonlinear Control Systems (3-0-3)

Prerequisite: Graduate level course in advanced linear systems.

Analysis and design of nonlinear control systems. The course will cover advanced topics in nonlinear control including passivity and input-output stability, stability of feedback systems, tracking, regulation, disturbance rejection, observers, and backstepping.

 

ECCE 753 Computational Prototyping of Dynamical Systems (3-0-3)

Prerequisite: Graduate level courses in engineering mathematical analysis or numerical methods in engineering.

This course covers the theory, algorithms, and best programming practices for the numerical simulation of dynamical systems. The course will draw examples from a variety of engineering disciplines, including electrical, mechanical, chemical, and aerospace engineering. Methods for the automatic generation of largescale, state-space descriptions are presented. Direct and iterative techniques from numerical linear algebra are used to compute steady state solutions of linear systems. Numerical issues such as stability, pivoting, conditioning, and accuracy are discussed in depth. Special attention is given to sparse matrix techniques. Newton’s algorithm for finding the equilibrium points of non-linear systems is presented next along with the automatic generation of the companion models of nonlinear elements. For transient analysis, the course covers the numerical algorithms for the solution of non-linear ordinary differential equations using first-order and higher-order methods with emphasis on linear multistep methods. The stability and error theories of such methods are also covered. State-of-the-art topics related to the macromodeling of dynamical systems using model-order reduction methods will wrap up the course. One distinguishing feature of this course is a semester-long project that will result in the implementation of a full, working dynamical system simulator and its application to solve a computational problem in the student’s area of PhD research.

 

ECCE 754 Computational Prototyping of Partial Differential Equations (3-0-3)

Prerequisite: Graduate level courses in engineering mathematical analysis or numerical methods in engineering.

This course covers the theory, algorithms, and best programming practices for the numerical solution of partial differential equations. The course will draw examples from a variety of disciplines, including fluid dynamics, heat and mass transfer, electromagnetics, solid mechanics, and mathematical finance. Algorithms covered include: finite-difference schemes, finite-element methods, boundary-element methods, and random-walk methods. One distinguishing feature of this course is a semester-long project that will result in the implementation of a full, working PDE solver and its application to a computational problem in the student’s area of PhD research.

 

ECCE 755 Cognitive Robotics (3-0-3)

Prerequisite: Graduate level courses in autonomous robotic systems and machine vision and image understanding.

To provide students with an advanced treatment of autonomous systems, how cognitive systems acquire information about the external world through learning and association of interrelationships between the observed world and their contextual frames. To learn how robotics cognitive systems can be designed to produce appropriate responses that make them more intelligent and autonomous.

 

ECCE 756 Robotic Perception (3-0-3)

Prerequisite: Graduate level courses in autonomous robotic systems and machine vision and image understanding.

To provide students with knowledge in the principles and practices of quantitative perception for robotic devices. To study both sensing devices and algorithms that emulates perception and intelligent systems. Learn to critically examine the sensing requirements of typical real world robotic applications. To acquire competences for development of computational models for autonomous robotic systems.

 

ECCE 757 Control of Robotic Systems / Cross-Listed with MEEN 767 (3-0-3)

Prerequisite: Graduate knowledge of engineering mathematics and computation.

This course is designed to teach students concepts and tools for analysis, design and control of robotic mechanisms. Kinematics, statics and dynamics of robotic systems.

 

ECCE 771 Advanced Integrated Circuits Technology (3-0-3)

Prerequisite: Graduate level courses in micro/nano processing technologies and advanced microelectronics devices.

What are the practical and fundamental limits to the evolution of the technology of modern MOS devices and interconnects? How are modern devices and circuits fabricated and what future changes are likely? Advanced techniques and models of devices and back-end (interconnect and contact) processing. What are sub-10nm future structures and materials to maintain progress in integrated electronics? MOS front-end and back-end process integration.

 

ECCE 772 Advanced Microsystem Design (3-0-3)

Prerequisite: Graduate level course in engineering mathematical analysis.

This course covers the design, modeling and characterization of micro-electro-mechanical systems (MEMS) with emphasis on the full microsystem design flow using state-of-the-art computer-aided design (CAD) tools. It addresses the various MEMS sensing and actuation modalities and provides in-depth treatment of the multi-faceted interplay between process, device, and electronic interface with its impact on overall system performance. Throughout the course, repeated use will be made of fundamental multi-domain formulations, CAD tools, and parameterized macromodels. Specific MEMS case studies will be selected from the areas of inertial motion sensing, piezoelectric energy harvesting, ultrasound transduction, RF-MEMS, and optical MEMS.

 

ECCE 773 Photonic Materials and Metamaterials Design for Engineers (3-0-3)

Prerequisite: Graduate knowledge of fundamentals of photonics.

The design of photonic devices and systems requires a strong background on the materials behavior with light. For an engineer there are significant opportunities in designing new metamaterials that provide functionality not found in natural materials, for application in fields such as energy harvesting, sensing, advanced displays, to name a few. The student will learn the modeling concepts and design flow for designing and fabricating novel engineered optical materials.

 

ECCE 774 Advanced Photonic Integrated Circuits (3-0-3)

Prerequisite: Graduate knowledge of fundamentals of photonics.

This course covers optical signal processing for photonic integrated circuits (PICs) and discusses stateof-the-art PIC components. The primary focus is being placed on multi-stage filter design and synthesis. Minimum, maximum, and linear-phase filters, optical lattice filters, Fourier filters, and generalized pole-zero architecture. Techniques such as least squares methods for IIR filter designs will be presented. State-ofthe-art PIC examples including bandpass/bandstop filters, optical gain equalizer, dispersion compensators, and arrayed waveguide grating (AWG) routers will be discussed in depth. Also included Bragg grating synthesis algorithm using coupled-mode approach. System-level application examples to microwave photonics, sensor networks, and coherent optical detection will be given. In addition to learning filter synthesis methods, students will gain a significant amount of experience in optimizing optical circuits at the subsystem level using MATLAB/Simulink and Lumerical software suite. The above techniques will take into consideration process variations, wavelength, and polarization dependence.

 

ECCE 778 Physics and Manufacturability of Advanced Micro and Nano Devices (3-0-3)

Prerequisite: Graduate level courses in micro/nano processing technologies and advanced microelectronics devices.

Explores the impact of physics on nanoscale devices and associated manufacturing challenges. Presents advanced physical models and practical aspects of advanced architecture devices’ front-end microfabrication processes, such as oxidation, diffusion, ion implantation, chemical vapor deposition, atomic layer deposition, etching, and epitaxy. Covers topics relevant to CMOS, bipolar, and optoelectronic device fabrication, including high k gate dielectrics, gate etching, implant-damage enhanced diffusion, advanced metrology, SiGe and fabrication of process-induced strained Si. BEOL Integration and reliability. Studies CMOS process integration concepts for advanced planar and 3D devices with Si, Si-Ge, III-V, 2D material systems. Assess the interaction of device characteristics, processing scheme and the design space. Leading to yield modeling and manufacturability vs. process complexity and the required balancing. Students use modern process simulation tools.

 

ECCE 781 The Physics of Solar Cells (3-0-3)

Prerequisite: Graduate level course in advanced microelectronics devices.

The physics of solar cells: solar history, semiconductor fundamentals, p-n junction physics, monocrystalline solar cells, thin film solar cells, managing light, new novel solar concepts, TCAD solar cells design and simulation, cleanroom fabrication of solar cells.

 

ECCE 794 Selected Topics in Electrical and Computer Engineering (3-0-3)

Prerequisite: Will be specified according to the particular topics offered under this course number.

This course covers selected contemporary topics in electrical and computer engineering. The topics will vary from semester to semester depending on faculty availability and student interests. Proposed course descriptions are considered by the Department of Electrical and Computer Engineering on an ad hoc basis and the course will be offered according to demand. The proposed course content will need to be approved by the Graduate Studies Committee. The Course may be repeated once with change of contents to earn a maximum of 6 credit hours.

 

  • Students must complete a Bachelor’s degree in a relevant discipline with a minimum Cumulative Grade Point Average (CGPA) of 3.0 out of 4.0, or equivalent.

  • Students must submit the English Language Proficiency Test Score.

  • Students with insufficient prior background in the chosen Master’s program may be considered for conditional admission, but will be assigned undergraduate and/or graduate courses and/or specially tailored remedial courses as specified by the relevant program.
     

    The following required documents are required:
     

  • Certified copy of Bachelor’s/Master’s degree certificate (as applicable).

  • Official transcript showing the grading scale.

  • English language proficiency score certificate (GRE, IELTS, TOEFL or EmSAT).

  • Curriculum Vitae (CV).

  • Passport-style photograph on a white background.

  • Valid passport

  • Valid UAE national ID card (for international applicants who currently reside in the UAE).

  • Statement of Purpose (500 to 1,000 words)

A master’s degree in Electrical and Computer Engineering from Khalifa University helps open many career opportunities for future success. The field of electrical and computer engineering is broad, therefore, graduates should have plenty of career opportunities to choose from both locally and internationally. The wide range of industries that utilize electrical and computer engineers include, artificial intelligence and computing systems, information and communication technology, renewable and conventional energy, oil and gas, healthcare, cyber security, banking/finance, robotics and autonomous systems, aerospace, nuclear and transportation. The MSc in ECE program at Khalifa University offers the student an excellent opportunity for interdisciplinary education, which will help them fulfill the requirement of these career paths. Graduates also go through rigorous training and research experience to enable them to pursue their studies at PhD level.

Confused?

We can help you

phone