Masters of Science in Electrical Engineering

Program Contact

Offered within the

Department of Computing and Electrical Engineering

Accredited by the UAE Ministry of Education

An electrical engineering master's degree that merges technology, engineering, and science and applies them to practical, industrial, and business applications.

Overview

Rapidly developing global technologies are changing the way we live. The Master of Science (MS) in Electrical Engineering at RIT Dubai combines theoretical fundamentals and practical applications in this dynamic field and educates students in the practices, methodologies, and cutting-edge techniques used in electrical engineering including Artificial Intelligence. Students will build on experience of engineering and customize a specialty of their choosing while working closely with electrical engineering faculty in a contemporary and applied research area.

The MSEE degree requires the completion of 30 credits divided between core courses, electives (which may be selected according to a focus area), and thesis/graduate paper. The degree requirements are shown in the program chart.

Students can customize their degree plan to meet their preferences under the general MSEE degree option as shown in the leftmost column of the program chart. The chart also demonstrates how students may be able to select a specific subset from the available electives to focus their studies in AI Systems with application to smart energy, control/robotics, or communications if enough of the relevant courses are offered in the desired track during their duration of study. However, the declaration of a focus area is not required and is used for advising purposes to help students initiate research in a focus area, but not required for degree completion and selected focus areas won’t appear on students’ transcripts. All students will be certified against the general MSEE degree requirements. Note that the core courses are common to all options and that the focus area courses are a subset of the general MSEE elective courses.

For the general MSEE option, students are required to take three core courses (EEEE-602, 707, and 709) and choose 5, 6, or 7 elective courses based on their choice of thesis, graduate paper, or comprehensive exam, respectively.

For the MSEE degree with a focus area, students are required to take the above three core courses and advised to take five focus-area courses as shown in the program chart if all focus-area courses are offered during their span of study. Students opting for the thesis option don’t need to take additional courses. Students opting for graduate paper or comprehensive exam option are required to take one or two additional electives, respectively. Note that students are advised to follow a focus area where possible but they need not to declare one for their degree completion. Focus areas won’t appear on students’ transcript.

Mission Statement

The Master of Science in Electrical Engineering program will foster an environment that encourages independent thinking and creativity and prepares students to pursue doctoral degrees in electrical engineering or a related discipline. Graduates of the program will also establish proficiency in a concentrated field of study and develop professional attributes that include communication skills and ethics to deal with the impact of technology and engineering solutions in a global and societal context.

Program Educational Objectives

The Master of Science (MS) in Electrical Engineering Program Educational Objectives (PEO) are broad statements that describe what graduates are expected to attain within a few years of graduation. Program educational objectives are based on the needs of the program’s constituencies. The Electrical Engineering faculty, in conjunction with its constituents, has established the following program educational objectives:

PEO 1: Graduates will have specialized training in a concentrated field of study and develop professional attributes that include communication skills, and ethics to deal with the impact of technology in a global and societal context.

PEO 2: The program will foster an environment that encourages independent thinking and creativity that prepares them to pursue doctoral degrees in electrical engineering or related disciplines.

Program Learning Outcomes

(Independent Thinking) Demonstrate the ability to work independently in developing innovative solutions in Electrical Engineering.
(Discipline Focus) Establish a proficiency in a concentrated course of study and research in a subfield of Electrical Engineering.
(Communication) Demonstrate the ability to communicate effectively in written and oral forms.

Curriculum

Typical Course Sequence

Total Credit Hours - 30

Coure Courses

Course		Sem. Cr. Hrs.
EEEE-602	Random Signals and Noise In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters.
EEEE-707	Engineering Analysis This course trains students to utilize mathematical techniques from an engineering perspective, and provides essential background for success in graduate level studies. An intensive review of linear and nonlinear ordinary differential equations and Laplace transforms is provided. Laplace transform methods are extended to boundary- value problems and applications to control theory are discussed. Problem solving efficiency is stressed, and to this end, the utility of various available techniques are contrasted. The frequency response of ordinary differential equations is discussed extensively. Applications of linear algebra are examined, including the use of eigenvalue analysis in the solution of linear systems and in multivariate optimization. An introduction to Fourier analysis is also provided.
EEEE-709	Advanced Engineering Mathematics Advanced Engineering Mathematics provides the foundations for complex functions, vector calculus and advanced linear algebra and its applications in analyzing and solving a variety of electrical engineering problems especially in the areas of control, circuit analysis, communication, and signal/image processing. Topics include: complex functions, complex integration, special matrices, vector spaces and subspaces, the null space, projection and subspaces, matrix factorization, eigenvalues and eigenvectors, matrix diagonalization, singular value decomposition (SVD), functions of matrices, matrix polynomials and Cayley-Hamilton theorem, state-space modeling, optimization techniques, least squares technique, total least quares, and numerical techniques. Electrical engineering applications will be discussed throughout the course.

General MSEE

Choose 5 courses for the thesis option; 6 courses for the graduate paper option

Course		Sem. Cr. Hrs.
EEEE-622	Electric Power Transmission & Distribution This course deals with the topics related to electric power transmission and distribution. Topics covered in this course include: Three Phase System – Wye and Delta connections, Transformers – equivalent circuit – performance characteristics, Balanced and Unbalanced System Analysis, Transmission and Distribution Line Design Considerations, Transmission Line Protection, Transmission Line Faults and Fault Analysis.
EEEE-624	Advances in Power Systems This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.
EEEE-629	Antenna Theory The primary objective is to study the fundamental principles of antenna theory applied to the analysis and design of antenna elements and arrays including synthesis techniques and matching techniques. Topics include antenna parameters, linear antennas, array theory, wire antennas, microstrip antennas, antenna synthesis, aperture antennas and reflector antennas. A significant portion of the course involves design projects using some commercial EM software such as ADS and developing MATLAB codes from theory for antenna synthesis and antenna array design.
EEEE-636	Biorobotics/Cybernetics Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects.
EEEE-647	Artificial Intelligence Explorations The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem- solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.
EEEE-661	Modern Control Theory This course deals with a complete description of physical systems, its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input- output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for a desired performance, state feedback, observers and their realizations.
EEEE-685	Principles of Robotics An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects.
EEEE-693	Digital Data Communication Principles and practices of modern digital data communication systems. Topics include pulse code transmission and error probabilities, M-ary signaling and performance, AWGN channels, band-limited and distorting channels, filter design, equalizers, optimal detection for channels with memory, synchronization methods, non-linear modulation, and introduction to multipath fading channels, spread spectrum and OFDM. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation.
EEEE-743	Digital Control This course builds on the fundamentals of continuous feedback control to introduce the student to computer (digital) regulation of systems in closed-loop. Discrete-time modeling and stability of signals and systems are discussed. Analog and digital control schemes are compared using s-domain to z- domain conversion, and time- domain response characterization. Closed-loop system design objective specification and evaluation is conducted through numerical simulation and experimental observation. Various discrete-time controller designs are implemented and evaluated using MATLAB/Simulink.
EEEE-765	Optimal Control The course covers different optimization techniques, as applied to feedback control systems. The main emphasis will be on the design of optimal controllers for digital control systems. The major topics are: different performance indices, formulation of optimization problem with equality constraints, Lagrange multipliers, Hamiltonian and solution of discrete optimization problem. Linear Quadratic Regulators (LQR), optimal and suboptimal feedback gains, Riccati equation and its solution, Dynamic Programming - Bellman’s principle of optimality - Optimal controllers for discrete and continuous systems - Systems with magnitude constraints on inputs and states.
EEEE-789	ST-Smart Grids This course introduces topics of smart energy systems, namely, distributed generation, renewable energy sources, energy storage, micro-grids, energy management and communication issues. Topics covered include analysis, modeling, control, and design to provide a working knowledge of smart-grid energy systems. Concepts dealing with computational intelligence, decision support systems, smart metering, optimization, and renewable energy sources will be presented. Graduate students will be required to complete individual advanced level research in an area beyond the scope of the undergraduate requirements that demonstrates a higher level of mastery in the subject matter with additional required deliverables representative of graduate level work.
EEEE-797	Wireless Communications This course covers advanced topics in wireless communications for voice, data and multimedia including evolution of cellular telephony and sensor networks, multiple-access systems, statistical path- loss models for different wireless environments, log- normal shadowing, reflection, diffraction, flat and frequency-selective multipath fading. The course will present the capacity limits of wireless communication channels and the different techniques used to improve the speed and performance of wireless links such as adaptive modulation, diversity, and MIMO. The course will also cover techniques to combat frequency- selective fading through adaptive equalization, space time coding, multicarrier modulation (OFDM), and spread spectrum.

MSEE with Focus Areas

Energy

Course		Sem. Cr. Hrs.
EEEE-622	Electric Power Transmission & Distribution This course deals with the topics related to electric power transmission and distribution. Topics covered in this course include: Three Phase System – Wye and Delta connections, Transformers – equivalent circuit – performance characteristics, Balanced and Unbalanced System Analysis, Transmission and Distribution Line Design Considerations, Transmission Line Protection, Transmission Line Faults and Fault Analysis.
EEEE-624	Advances in Power Systems This course will introduce the details of electric power markets and the techniques to better use the available resources. Topics include the description of steam generation and renewable energy sources. Formulation of the cost associated with the generation and the optimization methods to minimize this cost in the economic dispatch problem. Unit commitment. Optimal power flow formulation and its solution methods. Introduction to smart grid technologies and challenges.
EEEE-647	Artificial Intelligence Explorations The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem- solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.
EEEE-661	Modern Control Theory This course deals with a complete description of physical systems, its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input- output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for a desired performance, state feedback, observers and their realizations.
EEEE-789	ST-Smart Grids This course introduces topics of smart energy systems, namely, distributed generation, renewable energy sources, energy storage, micro-grids, energy management and communication issues. Topics covered include analysis, modeling, control, and design to provide a working knowledge of smart-grid energy systems. Concepts dealing with computational intelligence, decision support systems, smart metering, optimization, and renewable energy sources will be presented. Graduate students will be required to complete individual advanced level research in an area beyond the scope of the undergraduate requirements that demonstrates a higher level of mastery in the subject matter with additional required deliverables representative of graduate level work.

Control and Robotics

Course		Sem. Cr. Hrs.
EEEE-636	Biorobotics/Cybernetics Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects.
EEEE-647	Artificial Intelligence Explorations The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem- solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.
EEEE-661	Modern Control Theory This course deals with a complete description of physical systems, its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input- output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for a desired performance, state feedback, observers and their realizations.
EEEE-685	Principles of Robotics An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects.
EEEE-765	Optimal Control The course covers different optimization techniques, as applied to feedback control systems. The main emphasis will be on the design of optimal controllers for digital control systems. The major topics are: different performance indices, formulation of optimization problem with equality constraints, Lagrange multipliers, Hamiltonian and solution of discrete optimization problem. Linear Quadratic Regulators (LQR), optimal and suboptimal feedback gains, Riccati equation and its solution, Dynamic Programming - Bellman’s principle of optimality - Optimal controllers for discrete and continuous systems - Systems with magnitude constraints on inputs and states.

Communications

Course		Sem. Cr. Hrs.
EEEE-629	Antenna Theory The primary objective is to study the fundamental principles of antenna theory applied to the analysis and design of antenna elements and arrays including synthesis techniques and matching techniques. Topics include antenna parameters, linear antennas, array theory, wire antennas, microstrip antennas, antenna synthesis, aperture antennas and reflector antennas. A significant portion of the course involves design projects using some commercial EM software such as ADS and developing MATLAB codes from theory for antenna synthesis and antenna array design.
EEEE-647	Artificial Intelligence Explorations The course will start with the history of artificial intelligence and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many artificial intelligence techniques have been developed and applied to solve real life problems. This course will explore variety of artificial intelligence techniques, and their applications and limitations. Some of the AI techniques to be covered in this course are intelligent agents, problem- solving, knowledge and reasoning, uncertainty, decision making, learning (Neural networks and Bayesian networks), reinforcement learning, swarm intelligence, Genetic algorithms, particle swarm optimization, applications in robotics, controls, and communications. Students are expected to have any of the following programming skills listed above. Students will write an IEEE conference paper.
EEEE-693	Digital Data Communication Principles and practices of modern digital data communication systems. Topics include pulse code transmission and error probabilities, M-ary signaling and performance, AWGN channels, band-limited and distorting channels, filter design, equalizers, optimal detection for channels with memory, synchronization methods, non-linear modulation, and introduction to multipath fading channels, spread spectrum and OFDM. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation.
EEEE-797	Wireless Communications This course covers advanced topics in wireless communications for voice, data and multimedia including evolution of cellular telephony and sensor networks, multiple-access systems, statistical path- loss models for different wireless environments, log- normal shadowing, reflection, diffraction, flat and frequency-selective multipath fading. The course will present the capacity limits of wireless communication channels and the different techniques used to improve the speed and performance of wireless links such as adaptive modulation, diversity, and MIMO. The course will also cover techniques to combat frequency- selective fading through adaptive equalization, space time coding, multicarrier modulation (OFDM), and spread spectrum.

Electives

No additional courses are required for the thesis option Choose 1 course for graduate the paper option

Energy

Course		Sem. Cr. Hrs.
EEEE-636	Biorobotics/Cybernetics Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects.
EEEE-743	Digital Control This course builds on the fundamentals of continuous feedback control to introduce the student to computer (digital) regulation of systems in closed-loop. Discrete-time modeling and stability of signals and systems are discussed. Analog and digital control schemes are compared using s-domain to z- domain conversion, and time- domain response characterization. Closed-loop system design objective specification and evaluation is conducted through numerical simulation and experimental observation. Various discrete-time controller designs are implemented and evaluated using MATLAB/Simulink.

Control and Robotics

Course		Sem. Cr. Hrs.
EEEE-743	Digital Control This course builds on the fundamentals of continuous feedback control to introduce the student to computer (digital) regulation of systems in closed-loop. Discrete-time modeling and stability of signals and systems are discussed. Analog and digital control schemes are compared using s-domain to z- domain conversion, and time- domain response characterization. Closed-loop system design objective specification and evaluation is conducted through numerical simulation and experimental observation. Various discrete-time controller designs are implemented and evaluated using MATLAB/Simulink.
EEEE-789	ST-Smart Grids This course introduces topics of smart energy systems, namely, distributed generation, renewable energy sources, energy storage, micro-grids, energy management and communication issues. Topics covered include analysis, modeling, control, and design to provide a working knowledge of smart-grid energy systems. Concepts dealing with computational intelligence, decision support systems, smart metering, optimization, and renewable energy sources will be presented. Graduate students will be required to complete individual advanced level research in an area beyond the scope of the undergraduate requirements that demonstrates a higher level of mastery in the subject matter with additional required deliverables representative of graduate level work.

Communications

Course		Sem. Cr. Hrs.
EEEE-636	Biorobotics/Cybernetics Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects.
EEEE-685	Principles of Robotics An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects.
EEEE-789	ST-Smart Grids This course introduces topics of smart energy systems, namely, distributed generation, renewable energy sources, energy storage, micro-grids, energy management and communication issues. Topics covered include analysis, modeling, control, and design to provide a working knowledge of smart-grid energy systems. Concepts dealing with computational intelligence, decision support systems, smart metering, optimization, and renewable energy sources will be presented. Graduate students will be required to complete individual advanced level research in an area beyond the scope of the undergraduate requirements that demonstrates a higher level of mastery in the subject matter with additional required deliverables representative of graduate level work.

Thesis and Graduate Paper

Course		Sem. Cr. Hrs.
EEEE-790	Thesis Thesis is an independent engineering project or research problem to demonstrate professional maturity. A formal written thesis and an oral defense are required. The student must obtain the approval of an appropriate faculty member to guide the thesis before registering for the thesis. A thesis may be used to earn a maximum of 6 credits.	6
EEEE-792	Graduate Paper This course is used to fulfill the graduate paper requirement under the non-thesis option for the MS degree in electrical engineering. The student must obtain the approval of an appropriate faculty member to supervise the paper before registering for this course.	3

Notes

a) Students are advised to follow a focus area where possible but they need not to declare one for their degree completion. Focus areas won’t appear on students’ transcript.

b) The graduate paper (EEEE-792) must be completed in one semester and is graded with letter grades F- A. Students may not split the graduate paper credits over multiple semesters.

c) Students must maintain a cumulative GPA of 3.0 or above at all times. A student with a cumulative GPA of less than 3.0 will be placed on probation (which may lead to suspension). University rules on probation and suspension apply.

d) The minimum passing grade for graduate-level courses is “C”. Students are required to achieve a grade of “C” or above on all core courses.

e) The graduate work must be completed within seven years, starting from the time the first course is applied towards the MSEE degree.

f) All courses are three (3) semester credit hours.

g) Graduate elective courses from the main campus can be taken with the department approval.

To graduate, students need to complete all the requirements as listed in the curriculum graduation policy

Program Laboratories

Smart Energy Lab (SEL)

The Smart Energy Lab consists of an integrated system of Electrical Energy subsystems. Built by Lucas Nuelle, a German leading provider in education and training, the lab deploys smart electric gird including generation, transmission, distribution, and load management. In addition, the lab includes security and communication modules. The lab is based on the grid edge concept of clean energy smart grid configuration. The lab is used for undergraduate as well as graduate education and research. Students use the lab to design experiments required for their theses.

AI/Robotics Lab

The AI/Robotics lab supports varieties of activities in the area of AI and Robotics. Different use cases and projects in AI and Robotics can be developed with state-of-the-art equipment to support applications related to path planning, navigation, SLAM, Pick and Place. Moreover, the AI/Robotics lab incorporates the AI/Robotics student group whose mission is to support the industry and other stakeholders with smart solutions to problems in various disciplines. The AI/ Robotics lab consists of several ROSbot 2.0 Pro, PhantomX AX Metal Hexapod MK-III, Ubiquity Magni-Silver, WidowX 250 Robot Arm, Bioloid GP Humanoid Robots, and ABB dual-arm robot.

Computing Security Lab

To meet the growing demand for security applications, the computing security lab provides students with PCs loaded with software and advanced capabilities and with access to the DTLAB in order to support a wide range of applications in computing security. Experiments related to computer system security, penetration testing and frameworks, computer system forensics, and others. Using this lab, students design experiments in vulnerable environments, conduct various attacks, acquire information related to these attacks, and then develop techniques to mitigate them. The students can also run digital forensics tools such as Magnet Axiom and FTK to conduct investigations, reveal insights from the data collected, and practice incident response handling. Access to the DTLAB provides students with private cloud support that allow them to spin various virtual machines, connect them via a network, and study various security issues.

Computer Networking Lab

The lab offers capability to build and configure local area networks, both wired and wireless. This lab is divided into four clusters and each cluster has two sub-clusters with three adjacent PCs. A cabinet with at least one server, firewall, two routers, two IP Phones, access points, WLC, and five switches is dedicated for each cluster. UTP and Console cables are available for students to connect these devices to a LAN, WAN, and they can connect the event to the internet to install any additional required software or tool. This lab service is mainly used for some courses such as NSSA 241 Introduction to Routing the Switching, CSEC 462 Network Security and Forensics, NSSA 245 Network Services, and many other courses.

TDRA-ICTFund Digital Transformation Lab

This advanced lab in digital transformation was established in 2018 and funded by TDRA/ICTFund. The aim of this lab is to research and develop secure and smart solutions across a number of verticals that support digitization for government, enterprise, and education. The Lab is equipped with advanced computing capabilities, sensor devices, robots, components and facilities which allows electrical engineering and computing students to innovate, design and build technologies towards any type of digital transformation. Solutions developed in this lab will not only fulfil the demand of society but also look at the impact of these technologies on the evolution of society. The use of artificial intelligence, robotics, blockchain, augmented and virtual reality, softwarization of networks and radio will change the way automation will define the job market and shape the careers of the future generations.