Electrical & Computer Engineering
Dr. Robert Mauro
Chair, Department of Electrical and Computer Engineering
Vision Statement
The Electrical and Computer Engineering programs will be recognized for educating highly-valued engineers grounded in fundamental principles who are leaders in developing innovative solutions to engineering challenges.
Mission Statement
The mission of the Electrical and Computer Engineering programs is to bring together students from diverse backgrounds, provide them with an excellent technical education based on the fundamental principles of discovery and collaboration, foster an appreciation of ethical, environmental, and economic concerns, and develop within them an understanding of the importance of life-long learning. Graduates of the program will be prepared to become successful and socially responsible professionals and community leaders.
Central to the programs are certain principles, including the importance of collaboration, the discovery and sharing of knowledge, the appreciation of ethical, safety, and economic concerns, and the need for life-long learning and advanced study.
Program Educational Objectives
Graduates of the Electrical Engineering and the Computer Engineering programs will be valued by the engineering community. Graduates will be recognized for:
• Practicing electrical and computer engineering in a broad range of industries and technical skills in professional or advanced academic settings.
• Committing to the engineering profession and to expanding their knowledge and skill set with increasing independence and responsibility,
• Conducting themselves in a responsible, professional, and ethical manner.
• Participating in activities that support humanity and economic development nationally and globally, developing as leaders in their fields of expertise.
Student Outcomes
In order to prepare our students to meet these objectives after graduation the Electrical and Computer Engineering department has adopted the ABET 1 to 7 criteria as the appropriate student outcomes that our curriculum is designed to foster in our students:
SO 1: An ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics
SO 2: An ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors
SO 3: An ability to communicate effectively with a range of audiences
SO 4: An ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts
SO 5: An ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives
SO 6: An ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions
SO 7: An ability to acquire and apply new knowledge as needed, using appropriate learning strategies.
Electrical and Computer Engineering
Electrical engineers and computer engineers work at the frontier of high technology and are involved in research, the creation of new ideas, the design and development of new products and technologies, manufacturing, and marketing activities.
Coursework in both the Electrical Engineering and Computer Engineering programs emphasizes understanding of electrical circuits, software, and electromagnetic theory as a framework for later courses in electronics, bioelectrical engineering, cybersecurity systems, computer visualization, power grids and green energy engineering, the Internet-of-Things (IoT), communications, space systems, and mobile programming.
Embedded laboratory work associated with the lecture materials provides design experience and stresses the accuracy and limitations of electrical instruments and measuring devices. Senior multidisciplinary research and undergraduate concentrations offer opportunities for creative work with faculty guidance.
Electrical and computer engineers are some of the most in-demand professionals in the U.S. and the world, and today it is clear that most of these employment opportunities will require an increased understanding of Artificial Intelligence concepts. As a result, because the ECE Department has a commitment to ensure that our students are properly prepared for their jobs at graduation, we have developed a strong AI component in our ECE programs. In particular, we currently offer courses in ECE Applications of Artificial Intelligence, Unmanned Autonomous Vehicles, Applied Bioinformatics, Robotics, and Applied Data Mining for Engineers.
The ECE Department offers the following programs:
Electrical Engineering
Electrical engineering is a creative field that leads in the design, development, testing, and supervision of equipment, technologies, and devices that use electricity and electromagnetism. Wide in scope and variety, electrical engineering ranges from design of solid-state devices and increasingly complex microcircuits to the development of communication systems and power generation and transmission equipment to meet society's accelerating demand for clean energy. The fundamental principles of information processing and control systems inherent in an electrical engineer's background also find applications in such diverse areas as medicine, aerospace, transportation, artificial intelligence, financial systems, cybersecurity, telecommunications, consumer products, entertainment, green energy, and power systems.
The Electrical Engineering Program is accredited by the Engineering Accreditation Commission of ABET, under the General Criteria and the Electrical Engineering Program Criteria.
Computer Engineering
Computers continue to advance at a staggering pace and are being embedded into every kind of technology including consumer products, transportation, space systems, medical products, and military systems. Manhattan College's Computer Engineering Program offers a comprehensive analysis and design curriculum in computer systems, concentrating on both hardware and software, in order to provide an outstanding, cutting-edge education. This program incorporates the latest developments in electronics, communications, and control systems and programming in a variety of emerging areas, such as Cyber Security, Parallel Computers, Image Processing, Wireless Networks, VLSI (Very Large-Scale Integration), Big Data, Data Mining, and Artificial Intelligence.
Graduates in Computer Engineering will be prepared to develop digital systems for a variety of platforms such as supercomputers, smartphones, laptops, servers, IoT devices, and robots. Many of our graduates currently hold positions in the manufacturing, research, financial services, health, and government sectors.
The Computer Engineering Program is accredited by the Engineering Accreditation Commission of ABET, under the General Criteria and the Computer Engineering Program Criteria.
Four-Year Electrical Engineering and Computer Engineering Programs
The ECE department offers two-degree programs: Computer Engineering and Electrical Engineering. The curriculum for the first year is common to all engineering disciplines within the School of Engineering. Additionally, ECE students complete a common sophomore year in which basic concepts of both fields are presented. This common approach provides flexibility and permits a student to delay the choice of major within the ECE department.
Discipline-specific courses are taken in both the junior and senior years. Here, both Computer and Electrical Engineering majors choose from a variety of technical electives and “concentration courses” to enhance individual educational and career objectives. The four-year programs for both majors are summarized below.
Electrical Engineering Program
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| ENGS 115 | 3 | ENGS 116 | 3 |
| MATH 1851 | 4 | MATH 1861 | 4 |
| CHEM 101/103 or PHYS 101/1911 | 4 | CHEM 101/103 or PHYS 101/1911 | 4 |
| ENGL 110 or RELS 110 | 3 | ENGL 110 or RELS 110 | 3 |
| GEN ED ELEC | 3 | GEN ED ELEC | 3 |
| 17 | 17 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| EECE 2012 | 3 | EECE 2032 | 3 |
| EECE 210 | 3 | EECE 232 | 3 |
| EECE 229 | 3 | MATH 2861 | 3 |
| MATH 2851 | 4 | PHYS 1021 | 3 |
| ENGL ELECTIVE | 3 | PHYS 1921 | 1 |
| GEN ED or RELS ELEC | 3 | ||
| 16 | 16 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| EECE 303 | 3 | EECE 306 | 4 |
| EECE 305 | 4 | EECE 312 | 3 |
| EECE 311 | 3 | EECE 316 | 3 |
| EECE 321 | 3 | EECE 326 | 3 |
| GEN ED ELEC | 3 | EECE 329 | 3 |
| 16 | 16 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| EECE 410 | 3 | EECE 411 | 3 |
| EECE 477 | 3 | EECE 425 | 3 |
| TECHNICAL ELECTIVE | 3 | EECE 474 | 3 |
| TECHNICAL ELECTIVE | 3 | TECHNICAL ELECTIVE | 3 |
| TECHNICAL ELECTIVE | 3 | TECHNICAL ELECTIVE | 3 |
| GEN ED or RELS ELEC | 3 | GENERAL EDUCATION ELECTIVE | 3 |
| 18 | 18 | ||
| Total Credits: 134 | |||
Computer Engineering Program
| Freshman | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| ENGS 115 | 3 | ENGS 116 | 3 |
| MATH 1851 | 4 | MATH 1861 | 4 |
| CHEM 101/103 or PHYS 101/1911 | 4 | CHEM 101/103 or PHYS 101/1911 | 4 |
| ENGL 110 or RELS 110 | 3 | ENGL 110 or RELS 110 | 3 |
| GEN ED ELEC | 3 | GEN ED ELEC | 3 |
| 17 | 17 | ||
| Sophomore | |||
| Fall | Credits | Spring | Credits |
| EECE 2012 | 3 | EECE 2032 | 3 |
| EECE 210 | 3 | EECE 232 | 3 |
| EECE 229 | 3 | MATH 2861 | 3 |
| MATH 2851 | 4 | PHYS 1021 | 3 |
| ENGL ELECTIVE | 3 | PHYS 1921 | 1 |
| GEN ED or RELS ELEC | 3 | ||
| 16 | 16 | ||
| Junior | |||
| Fall | Credits | Spring | Credits |
| EECE 303 | 3 | EECE 306 | 4 |
| EECE 305 | 4 | EECE 312 | 3 |
| EECE 311 | 3 | EECE 316 | 3 |
| EECE 321 | 3 | EECE 320 | 3 |
| GEN ED ELEC | 3 | EECE 329 | 3 |
| 16 | 16 | ||
| Senior | |||
| Fall | Credits | Spring | Credits |
| EECE 410 | 3 | EECE 411 | 3 |
| EECE 476 | 3 | EECE 473 | 3 |
| TECHNICAL ELECTIVE | 3 | EECE 475 | 3 |
| TECHNICAL ELECTIVE | 3 | TECHNICAL ELECTIVE | 3 |
| TECHINCAL ELECTIVE | 3 | TECHNICAL ELECTIVE | 3 |
| GEN ED or RELS ELEC | 3 | GENERAL EDUCATION ELECTIVE | 3 |
| 18 | 18 | ||
| Total Credits: 134 | |||
- Notes:
1. Students must earn a grade of C (2.0) or better in calculus I, II, III, differential equations, chemistry and physics.
2. EECE 201 Fundamentals of Electrical Systems Analysis I and EECE 203 Fundamentals of Electrical Systems Analysis II must be completed with a grade of C (2.0) of better.
Undergraduate Concentrations
The integrative curriculum prepares students to identify, formulate, and execute solutions to real-world problems. Students learn how to combine engineering principles with science and use engineering tools with activities that reinforce the concepts learned in the classroom. As part of these efforts, concentration study areas have been approved by the New York State Education Department (NYSED). Paired with the rigorous curricula and a hands-on project-based approach, concentrations reinforce the broad relevance of the powerful problem-solving methodologies of engineering and illuminate enabling technologies for applications of technology.
- The Electrical Engineering program offers the following three concentrations:
- Applied Artificial Intelligence in Electrical Engineering (9 credits)
- Cybersecurity (9 credits)
- Power Grids and Green Energy Engineering (9 credits)
- The Computer Engineering program offers the following two concentrations:
- Applied Artificial Intelligence in Computer Engineering (9 credits)
- Cybersecurity (9 credits)
Applied Artificial Intelligence Concentration
The Applied Artificial Intelligence (AI) in Electrical Engineering and in Computer Engineering Bachelor of Science concentrations are designed to provide ECE students with the fundamental concepts of artificial intelligence from an engineering perspective. A major component of these concentrations is the design and development of AI-based intelligent systems such as IoT, robotics, power-grids, and controls.The course listings for both the Electrical Engineering and the Computer Engineering programs are the same and are indicated below:
Plan of Study for the Applied Artificial Intelligence Concentration - 9 credits total
Students who plan to take the Applied Artificial Intelligence Concentration in Electrical Engineering and Computer Engineering must take the following required course:
| EECE 471 | Artificial Intelligence Applications in Electrical & Computer Engineering | 3 |
and select two elective courses from the following list*:
| EECE 442 | Computer Vision & Imaging | 3 |
| EECE 448 | Applied Machine Learning for Electrical & Computer Engineering | 3 |
| EECE 449 | Unmanned Autonomous Vehicles | 3 |
| EECE 457 | Bioinspired Robotic Vision Systems | 3 |
| EECE 460 | Big Data, & Deep Learning for Electrical & Computer Engineering | 3 |
| EECE 494 | Special Topics in Artificial Intelligence (AI) in Electrical and Computer Engineering | 3 |
| EECE 469 | Introduction to Remote Sensing | 3 |
- Note:
*Elective courses may be substituted by the Chairperson as the need arises.
Cybersecurity Concentration
The cybersecurity undergraduate concentration equips students with the knowledge and skills to protect and defend digital systems against cyber threats, ensuring the security and integrity of critical information.
Plan of Study for Cybersecurity Concentration - 9 credits total
Students who plan to take the Cybersecurity Concentration must select a total of three elective courses from the following list*:
| EECE 409 | Ethical Hacking and Penetration Testing | 3 |
| EECE 417 | Mobile App. & Cybersecurity | 3 |
| EECE 461 | Network Security Systems | 3 |
| EECE 462 | Data & Applications Security | 3 |
| EECE 488 | Cyber-Physical Systems Security | 3 |
| EECE 490 | Cybersecurity Systems Fundamentals | 3 |
| EECE 493 | Special Topics in Cybersecurity | 3 |
| EECE 448 | Applied Machine Learning for Electrical & Computer Engineering | 3 |
| Or | ||
| EECE 460 | Big Data, & Deep Learning for Electrical & Computer Engineering | 3 |
| EECE 458 | Cybersecurity Systems | 3 |
- Note:
-
*Elective courses may be substituted by the Chairperson as the need arises.
Electrical Engineering Concentration in Power Grids and Green Energy Engineering
The concentration in Power Grids and Green Energy Engineering provides a broad background in the principles, analysis, and design of large electric power and green energy systems, smart grids, electric energy conversion, and the application of electronic devices at high power levels.
Plan of Study for Power Grids and Green Energy Engineering Concentration - 9 credits total
Students who plan to take the Power Grids and Green Energy Engineering Concentration must take the following required course:
| EECE 477 | Power & Energy Systems | 3 |
and select two elective courses from the following list*:
| EECE 400 | Industrial Electric Drives (IED) | 3 |
| EECE 416 | NERC Standards & Operation | 3 |
| EECE 418 | Intro to Power Electronics | 3 |
| EECE 434 | Bulk Power System Operation | 3 |
| EECE 439 | Protective Relays | 3 |
| EECE 466 | Green Energy Sources | 3 |
| EECE 482 | Grid Integration of Wind Energy | 3 |
| EECE 492 | Special Topics in Power Systems | 3 |
- Note:
*Elective courses may be substituted by the Chairperson as the need arises
Courses
EECE 201. Fundamentals of Electrical System Analysis I. 3 Credits.
This course is an introduction to basic concepts of Electrical Networks, including Kirchoff’s Laws, fundamental analysis of resistive networks using nodal and loop analysis, Superposition, Thevenin and Norton Theorems. Introduction to operational amplifiers as well as capacitive and inductive networks. Transient analysis of first-order systems. PSPICE will be employed for the analysis of electrical networks. Three hours of lecture per week and three-four lab sessions during the semester. Prerequisite: MATH 185 with a Minimum Grade of C. Corequisite: MATH 186.
EECE 203. Fundamentals of Electrical System Analysis II. 3 Credits.
Building on the concepts in EECE 201, this course is an introduction to the transient behavior of 1st and 2nd order systems; AC steady state analysis in the frequency domain; power considerations in single and polyphase circuits; and transformers and magnetically coupled networks. PSpice will be employed for the analysis of electrical networks. Three lecture hours per week and three-four lab sessions during the semester.
Prerequisite: EECE 201 Minimum Grade is C.
EECE 210. Applied Software Engineering I. 3 Credits.
This course introduces hardware-software design applications and computer software development. Students will work with hands-on projects where they will gain an understanding of the relationship between the software they write and the hardware it is controlling. The software portion of the course covers the fundamentals of programming and is divided into three modules that introduce students to the C, C++, and Python programming languages.
EECE 229. Introduction to Digital Systems. 3 Credits.
This course introduces the fundamental principles of the design of digital systems. The material includes number representations, switching algebra, and logic systems for the analysis and synthesis of combinational and sequential circuits. Basic design concepts and implementation technology, and the use of HDL and computer-based design tools are also covered. The course will include a course-embedded laboratory component.
Prerequisite: MATH 185 -Minimum Grade is C.
EECE 232. Computer System, Organization & Design. 3 Credits.
This course presents register transfer systems and datapaths, microprocessors, microprocessor architecture and operation, instruction formats, assembly language programming, procedures and parameter passing, system bus timing, interfacing memory and I/O ports, serial and parallel data transfer, and interrupts. C-language programming for hardware device interfacing is introduced. A course-embedded laboratory will be included.
Prerequisite: EECE 229.
EECE 303. Signals and Systems I. 3 Credits.
Modeling and analysis of continuous-time systems. Convolution of signals and representation of linear time invariant systems. Fourier series. The Fourier Transform and its applications. The Laplace Transform and its applications to continuous-time systems. Stability of continuous time systems.
Prerequisite: EECE 203 - Minimum Grade is C.
EECE 304. Signals and Systems II. 4 Credits.
The Sampling Theorem. The Z-Transform and discrete-time systems analysis. Stability of discrete-time systems. Design of analog and digital filters. The Discrete Fourier Transform and its applications. The Fast Fourier Transform. State-space analysis. Four hours of lecture per week and three-four lab sessions during the semester. Prerequisite: EECE 303.
EECE 305. Electronic Systems I. 4 Credits.
Terminal characteristics of solid-state devices. Power supply design. Transistor circuit biasing. Graphical analysis of transistor circuits. Small signal transistor circuit models and gain analysis. Selected lab sessions during the semester. Prerequisite: EECE 201- Minimum Grade is C.
EECE 306. Electronic Systems II. 4 Credits.
Multistage transistor circuit analysis and design. Frequency response of electronic circuits. Operational amplifiers. Power amplifiers. Digital electronic circuits. Selected lab sessions during the semester. Prerequisite: EECE 305.
EECE 307. Mathematical Methods for Electrical & Computer Engineering. 4 Credits.
Application of the basic principles of Vector Calculus and Linear Algebra to representative areas of Electrical and Computer Engineering. Subject matter includes review of vector and matrix methods and techniques with subsequent utilization in areas of Circuit Analysis, Linear and Control Systems, Power Systems, and Electromagnetics, with specific consideration given to the role of vector operators in Maxwell’s Equations Prerequisites: MATH 186; Minimum grade is C.
EECE 308. Mathematical Methods for Electrical and Computer Engineering. 3 Credits.
Application of the basic principles of Vector Calculus and Linear Algebra to representative areas of Electrical and Computer Engineering. Subject matter includes review of vector and matrix methods and techniques with subsequent utilization in areas of Circuit Analysis, Linear and Control Systems, Power Systems, and Electromagnetics, with specific consideration given to the role of vector operators in Maxwell's Equations. Prerequisite: MATH-186 with a minimum grade of C.
EECE 311. Applied Electromagnetics. 3 Credits.
An introduction to the principles of Electromagnetics with particular emphasis on waves and their applications. Topics will be chosen from: nature of electromagnetism; fields; transmission lines (lumped parameter models, lossless lines, open- and short-circuit models, standing wave ratios, transient responses, impedance matching); radiation; fiber optics; telecommunication systems. Prerequisites: EECE 307 and MATH 286.
EECE 312. Signals & Systems II. 3 Credits.
The Sampling Theorem. The Z-Transform and discrete-time systems analysis. Stability of discrete-time systems. Design of analog and digital filters. The Discrete Fourier Transform and its applications. The Fast Fourier Transform. State-space analysis. Prerequisite: EECE 303.
EECE 315. Probability and Statistics for Electrical & Computer Engineering. 4 Credits.
Application of the basic principles of Probability and Statistics to areas of Electrical and Computer Engineering involving uncertainty and randomness in Signals and Systems. Principles include discrete and continuous random variables and their distributions, moments, and characteristic functions; empirical distribution functions; parameter estimation; confidence limits; linear regression; hypothesis testing; and statistical approaches to engineering decisions. Topics for consideration are taken from Communications, Power Systems, Signal Processing, Image Processing, and Control Systems and are used to both develop and illustrate principles and their application. Prerequisites: MATH 285; Minimum grade is C.
EECE 316. Probability and Statistics for Electrical and Computer Engineering. 3 Credits.
Application of the basic principles of Probability and Statistics to areas of Electrical and Computer Engineering involving uncertainty and randomness in Signals and Systems. Principles include discrete and continuous random variables and their distributions, moments, and characteristic functions; empirical distribution functions; parameter estimation; confidence limits; linear regression; hypothesis testing; and statistical approaches to engineering decisions. Topics for consideration are taken from Communications, Power Systems, Signal Processing, Image Processing, and Control Systems and are used to both develop and illustrate principles and their application. Prerequisites: MATH 285 with a minimum grade of C.
EECE 320. Applied Software Engineering II. 3 Credits.
This course gives an introduction to the concepts of object-oriented software development, and hardware integration. The course covers the basics of object-oriented Java programming, interaction of JAVA with hardware. It also introduces the student to the Integrated Development Environments, Agile Software Development, and UML (Unified Modeling Language). Pre-Requisite EECE 210 and EECE 229.
EECE 321. Embedded Systems Design. 3 Credits.
This software-hardware oriented course emphasizes the components and techniques used in the embedded systems with applications in Wireless Sensor Networks (WSN) and Internet of Things (IoT) systems. Topics include embedded system architectures, WSN topologies and implementation techniques, IoT system architecture, and software implementation using the C programming language. Prerequisite: EECE 232.
Cross-listed with ECEG 721.
EECE 326. Instrumentation Systems. 3 Credits.
Detection, acquisition, and analysis of information from the environment. Topics will include: sensors and measurement methods, instrumentation and transducers for the measurement of signals, information conditioning, computer control of data acquisition, and interpretation of results. Pre-requisites: EECE 303, EECE 305.
EECE 329. Modeling Techniques in Electrical & Computer Engineering. 3 Credits.
The application of computing techniques for the simulation and modeling of complex electrical, electromechanical and computer hardware systems. Models will be developed that will allow simulation to replace the cost of expensive experimental work on the actual engineering systems. The course will focus on efficient mathematical modeling of real-world engineering systems.
EECE 400. Industrial Electric Drives (IED). 3 Credits.
Hands-on experiments and demonstrations in industrial electric drives, requirements placed by mechanical systems on electric drives, and their role in various applications such as flexible production systems, energy conservation, renewable energy and transportation. Power electronics in drives using switch-mode converters and pulse width modulation to synthesize the voltages in dc and ac motor drives. Design of a controller using Matlab /Simulink. Prerequisites: EECE 303, EECE 305.
Cross-listed with ECEG 700.
EECE 403. Trustworthy AI Applications in Electrical & Computer Engineering. 3 Credits.
Recently ethical, legal, and privacy consequences on humanity and environment of Artificial Intelligence (AI) have received increased attention. This course examines the trustworthiness of AI foundations related to data preparation, algorithm design, systems development, and deployment in electrical and computer engineering applications. Legal frames are investigated on how AI’s processes of development and deployment could be adapted for safety goals. As case studies, AI electrical and computer engineering applications will be examined for ethical aspects, fairness, privacy, and liability.
Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 703.
EECE 404. Bioinstrumentation. 3 Credits.
Design principles of biomedical devices, bioelectronics, medical nanodevices, transducers, sensors, interface electronics, microcontrollers, and engineering programming. Signal modalities, bioelectrical signal monitoring, acquisition, analysis, and processing. Case studies and platform-based designs of medical devices, and instrumentation. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 704.
EECE 409. Ethical Hacking and Penetration Testing. 3 Credits.
This course provides students with essential skills in performing penetration testing, vulnerability identification, and risk mitigation. Students will utilize advanced tools to detect and exploit vulnerabilities in target network environments.
Prerequisites: EECE 210 and EECE 321.
EECE 410. Capstone Design I. 3 Credits.
This course is the first semester of a year-long effort in which senior ECE students, working in teams or individually, complete a project under the direction of a faculty coordinator and mentor. The project must address a question of importance related to electrical and/or computer engineering. In this first semester, students will: identify the problem to be investigated; research the associated topics including relevant literature; develop the engineering tools (e.g., application software, HLLs) as needed or appropriate; develop a comprehensive plan for completion of the project; and complete any necessary preliminary testing or feasibility studies. The plan must reflect those normally produced by professional engineers in similar assignments. The team members will meet frequently with the faculty mentor to discuss and evaluate progress. The faculty mentor will lecture on those topics common to such projects as well as any technical material that is necessary.
Prerequisite: EECE 304 & EECE 306 & EECE 315 & (EECE 320 or EECE 326).
EECE 411. Capstone Design II. 3 Credits.
Students will complete the engineering design undertaken in EECE 410. The outcomes to be achieved are consistent with those specified in the ABET general engineering criteria. In particular, when completed, students will have: understood modeling associated with a design; demonstrated skills in using a computer in the course of an engineering design; exhibited critical thinking; have solved an open-ended problem; successfully functioned on an interdisciplinary team; completed a successful engineering design; shown that they can communicate effectively; have understood ethical implications of their efforts; and understood how continued learning is important in refinement of the enterprise. To meet these outcomes, students will be required to make a presentation before the faculty of the department. In addition, students or teams must submit a final report that will be evaluated by members of the department or invited reviewers.
Prerequisite: EECE 410.
EECE 416. NERC Standards & Operation. 3 Credits.
North American Electric Reliability Corporation (NERC) standards and related compliance concerns in relationship to operational principles of the power systems. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 737.
EECE 417. Mobile App. & Cybersecurity. 3 Credits.
The proliferation of smart mobile systems gives rise to new areas of security vulnerability. This course explores the security considerations associated with smart consumer mobile devices, smartphones, mobile telecommunication systems, and sensor networks. Topics include smartphone security, mobile location privacy, and wireless sensor security. Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 717.
EECE 418. Intro to Power Electronics. 3 Credits.
Topics of importance in Power Electronics including techniques for the design of Electric Vehicles, highly efficient power supplies, power factor correction and motor control systems . High voltage DC to AC power conversion methods . Vehicle battery design and charging issues. Laboratory experience with semiconductor electronic switching devices and different motor types.
Prerequisite: EECE 304 and EECE 306. Cross-listed with ECEG 718.
EECE 419. Senior Project A. 1-3 Credit.
Independent investigation, under the guidance of an approved advisor and the sponsorship of an electrical engineering faculty member, terminating in a final report, and when feasible, a tested design. Prerequisites: EECE 304 and EECE 306.
EECE 420. Senior Project B. 1-3 Credit.
Independent investigation, under the guidance of an approved advisor and the sponsorship of an electrical engineering faculty member, terminating in a final report, and when feasible, a tested design. Prerequisites: EECE 304 and EECE 306.
EECE 423. Imaging & Inverse Problems in Electrical & Computer Engineering Systems. 3 Credits.
This course addresses the foundations of inverse problems in robotics and IoT systems. The list of topics includes image sensors; different types of noise; Gaussian and Poisson distributions; estimation techniques; denoising; total variation regularizations; weak signals and photon limit; variance stabilizing transforms; motion estimation under noise; noise estimation. Foundations covered in this course are emphasized with a class project.
Prerequisites: EECE 304 and EECE 306.
EECE 424. Hardware/Software Design Trade off Techniques. 3 Credits.
This course is designed to promote the skills of computer engineering students in the areas of software and hardware integration and related ECE applications. This course focuses on the use of .NET Gadgeteer to program and configure hardware. Microsoft Foundation Class (MFC) library and Windows programming will be highlighted. Several topics will be covered, including Windows architecture, message-driven programming, dialog-based application development, SDI and MDI applications, Device Contexts, and database access. At the end of the course, a comprehensive project covering key concepts in hardware programming is assigned.
Prerequisites: EECE 210 and EECE 321.
EECE 425. Control Systems Design. 3 Credits.
Principles of linear feedback control systems. System modeling. Transient response and steady-state error analysis. Stability and analysis of systems from Routh-Hurwitz, Nyquist, and Root Locus viewpoints. Controller design and compensation techniques. Prerequisite: EECE 303.
EECE 427. DSP System Design. 3 Credits.
The design of modern digital signal processing software and hardware using actual DSP devices, analog interfacing to DSP hardware. A review of Signal processing concepts, design of FIR & IIR filters, design of algorithms for computing the FFT and Inverse FFT, analog interfacing hardware on the DSK board, the use of the MatLab Signal Processing package as a part of the overall DSP system design process. Prerequisites: EECE 304.
EECE 433. Photonics. 3 Credits.
Introduction to Optical Engineering. Principles of reflection and refraction of light. Geometrical Optics: lenses and optical instruments. Elements of Lasers, Light Modulators and Detectors. Optics from a systems perspective, Diffraction and Interference of light waves. Coherent optical signal processing. Prerequisites: EECE 304 and EECE 306.
EECE 434. Bulk Power System Operation. 3 Credits.
Operation of the bulk electric power system in North America. Basic types of high voltage equipment and station configurations. Methods and equipment to control power flow and voltage levels on the power system. Prerequisite: EECE 203 Minimum Grade is C.
Cross-listed with ECEG 734.
EECE 436. Computer Graphics. 3 Credits.
Basic concepts of computer graphics systems include display devices, graphics software and the display of solid objects. Point plotting procedures, line drawing algorithms and circle generators. Displays and controllers, storage and refresh devices. Two dimensional transformations; clipping and windowing. Graphics software; windowing functions, display files; geometric models. Interactive raster graphics. Three-dimensional graphics including surface display, perspective and hidden surface removal. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 765.
EECE 437. Introduction To Quantum Concepts and Computing. 3 Credits.
Classical and quantum bits (Qubits). Quantum states as Hilbert space vectors and their matrix representations. Operators, Eigenvalues and Eigenvectors. Bloch sphere representation of a qubit. Quantum postulates and elements of quantum dynamics. Evolution of a two state system. Quantum gates and elements of system architecture. Criteria for successful quantum computation. Some current problems in system realization.
Prerequisite: EECE 307.
EECE 438. Multimedia Techniques. 3 Credits.
Introduction to multimedia, PC architecture and assembly language basics. Color TV and video concepts. PC audio standards, the MIDI music standard, and audio signal processing. Multimedia presentation and authoring techniques. HTML authoring and the fundamentals of the World Wide Web. Prerequisites: EECE 304 and EECE 306.
EECE 439. Protective Relays. 3 Credits.
Analysis of faulted power systems, symmetrical and asymmetrical systems, transient stability, emergency control and system protection.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 738.
EECE 441. Robotics. 3 Credits.
Introduction to the operation of industrial manipulators. Robotic theory includes homogeneous coordinate transformations, kinematics and dynamics of articulated manipulator arms, and elements of feedback control theory. The design of hardware and software used for motion control. Introduction to computer vision and artificial intelligence. Prerequisites: EECE 304 and EECE 306.
EECE 442. Computer Vision & Imaging. 3 Credits.
Detection, image formation, and engineering design of vision and imaging sensors and systems. Unmanned aerial and underwater imaging systems, biomedical image recognition, medical image understanding, inspection, and robotics applications. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 742.
EECE 443. Biomedical Imaging Systems. 3 Credits.
Engineering and physical principles of biomedical modalities, as applied to clinical diagnostics and pharmaceutics, gene arrays and Omics imaging technologies central to the detection process, system design, data analysis and classification. Clinical examples. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 743.
EECE 445. Medical Device Miniaturization. 3 Credits.
Engineering design of miniaturized medical devices, operating on electrical, and quantum principles, with reduced form factor and weight, while reducing power consumption and boosting performance. Integration trends, functionality, scalability, reconfigurability. Case studies and platform-based designs of miniaturized medical devices, such as medical implantable devices, heart monitors, pacemakers, video cameras.
Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 745.
EECE 447. Image Processing & Pattern Recognition. 3 Credits.
Digital image processing for manipulation and enhancement of images, development of advanced techniques for object recognition, object classification, image reconstruction, image compression, and feature extraction. Computational analytic and interpretive approaches to optimize extraction and use of imaging data. Prerequisites: EECE 304 and EECE 306. Cross-listed with ECEG 747.
EECE 448. Applied Machine Learning for Electrical & Computer Engineering. 3 Credits.
Fundamental concepts, methods, and technologies of machine learning. Design, modelling, implementation, and optimization of hardware architectures for machine learning systems.
Machine/deep learning for signal detection, channel modeling, estimation, interference mitigation, and decoding. Performance analysis and evaluation of machine learning techniques in
communication and networks systems. Machine learning for emerging communication systems and applications, such as drone systems, IoT, autonomous navigation and robotics, edge computing, smart cities, and vehicular networks.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 748.
EECE 449. Unmanned Autonomous Vehicles. 3 Credits.
History of the UAV, basics of mechatronic design, common sensor payloads, high-definition cameras, sonars, lidars, vision and imaging design parameters. Major design challenges, laws and regulations, operations and safety. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 749.
EECE 453. Applied Bioinformatics. 3 Credits.
Bioinformatics principles applied to microscopic and biomedical image acquisition methods and applications, methods and applications of image analysis and related machine learning, pattern recognition and data mining techniques, image oriented multidimensional. Methods and applications for the analysis of post-translational modifications, proteomic, mass spectroscopic, and chemoinformatic data. Prerequisite: EECE 315.
Cross-listed with ECEG 753.
EECE 455. Bionanophotonics. 3 Credits.
Nanoparticles for optical bioimaging, optical diagnostics and light guided and activated therapy. Use of nanoparticles platforms for intracellular diagnostics and targeted drug delivery, PEBBLE nanosensors. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 755.
EECE 456. Drug Delivery Systems. 3 Credits.
Instrumentation, devices, and techniques to characterize the physiochemical, optical properties, and in vitro immunological, biological, and stability characteristics of drugs delivery, proteins, and nanomaterials. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 756.
EECE 457. Bioinspired Robotic Vision Systems. 3 Credits.
Animal vision combined with human vision and cognition can provide a source of inspiration for the design and development of novel computational, efficient, and robust electro-optical vision systems. The underlying philosophy of this course is to introduce new evolutionary cognitive vision systems that use artificial neurons to mimic the functions and characteristics of the human brain and drive improvements in costs, efficiency, and processing.
Students taking this course will develop an integrative knowledge of bioinspired vision systems and artificial intelligence algorithms as well as the impact of biomimetic vision on a large gamma of imaging and robotic vision systems, and modalities. As a result, the students will be inspired towards the conception, and design of novel bio-inspired vision robotic applications, systems, and techniques for different segments of industry, autonomous systems, healthcare, defense, and consumer electronics.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 757.
EECE 458. Cybersecurity Systems. 3 Credits.
Cybersecurity as it relates to systems and then on the engineering principles for secure systems. The course focuses on the differences between threats and vulnerabilities, examples of cybersecurity attacks and events, frameworks, requirements and principles for securing systems.
Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 758.
EECE 459. Quantum Cryptography. 3 Credits.
Methods that seeks to solve the problem of how to securely send cryptographic keys between two parties by encoding them within light particles, or photons. Quantum cryptography and key distribution technique. Prerequisites: EECE 304 and EECE 306.
EECE 460. Big Data, & Deep Learning for Electrical & Computer Engineering. 3 Credits.
This class will focus of how to extract actionable, non-trivial knowledge from unstructured, heterogenous, massive number of data sets using machine learning and deep learning techniques. On the tool's side, we will cover the basic systems and techniques to store large volumes of data and modern systems for cluster computing based on MapReduce patterns such as Hadoop MapReduce, Apache Spark, and Flink. FPGAs, GPUs, and neuromorphic processors with emphasis on edge, fog, and cloud computing architectures, industry, manufacturing communications, autonomous navigation systems, IoT, systems, remote sensing.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 767.
EECE 461. Network Security Systems. 3 Credits.
Theoretical and practical aspects of network security. Security of TCP/IP applications; firewalls; wireless LAN security; denial-of-service defense.
Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 761.
EECE 462. Data & Applications Security. 3 Credits.
Explore principles, technologies, tools, and trends in data and application security within software and hardware systems. Topics include data security fundamentals, access control models and policies, secure applications development practices, secure software and hardware architectures, trusted computing principles. Prerequisite: EECE210 and EECE321.
Cross-listed with ECEG 760.
EECE 464. Database Management Systems (DBMS). 3 Credits.
Software and hardware design problems for DBMS; an overview of database systems, data manipulation languages, normal forms, machine architectures. This course will focus on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions. Case studies on open-source and commercial database systems are used to illustrate these techniques and trade-offs. More topics can be added by the instructor. Prerequisites: EECE 304 and EECE 306.
EECE 465. Quantum Computing. 3 Credits.
This course provides a theoretical and practical treatment of quantum computing. Topics covered include brief quantum mechanics history, and the postulates of quantum theory. Dirac notation, quantum operators, composition, entanglement, and measurements. Quantum Computing via quantum circuit model: Description of qubit and universal set of gates. Simple quantum protocols: teleportation, superdense coding. The Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm. Grover’s algorithm for searching. Entanglement and Bell’s theorem. Quantum communications and quantum error correction. Applications in cybersecurity, cryptography, financial modeling, drug development and artificial intelligence; Senior Status
Cross-listed with ECEG 777.
EECE 466. Green Energy Sources. 3 Credits.
This course presents basic information on Energy outlook, interconnection issues of distributed alternate energy resources, efficiency of power production, electric energy conversion and storage (fossil fuel, nuclear, hydro, solar, fuel cells, wind, and batteries). This course also explores the different energy link integration methodologies using Matlab/Simulink
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 768.
EECE 467. Physical Electronics. 3 Credits.
Exploring the operation of electrical and electronic devices, focusing on the internal physical laws that determine their utility and limitations. Thermal, optical, electrical, magnetic and quantum properties; energy audit, waves. Transducers, heat sinks, diodes, solar cell, LED, TEDs, FET, memories, nanostructure. Three lectures. Prerequisite: PHYS 101 and PHYS 102 with Minimum Grade is C.
EECE 469. Introduction to Remote Sensing. 3 Credits.
This course is intended to provide an introduction to remote sensing of objects with applications in defense and environment. The course covers the basic principles of image sensors and techniques, image interpretation, remote sensing theory, and digital image analysis in relation to optical, thermal and microwave remote sensing systems. Examples of remote sensing applications will be presented along with methods for obtaining quantitative information from remote sensing imagery.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 769.
EECE 470. Introduction to Space Systems. 3 Credits.
This course is intended to provide the fundamental principles of space systems, in terms of electro-optical sensing, robotic vision, and imaging. Critical space missions such as monitoring of the integrity of spacecraft structures, detection of debris, object recognition and classification will be presented and discussed. Defense and commercial applications will be introduced and discussed.
Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 770.
EECE 471. Artificial Intelligence Applications in Electrical & Computer Engineering. 3 Credits.
This course introduces methods for designing computer visualization, robotics, and IoT systems utilizing artificial intelligence, and machine vision. The following topics specifically related to the area of electrical and computer engineering will be covered: classification algorithms, information transference human/machines, single-agents and multi-agent Systems (MAS), expert systems, engineering knowledge presentation, automated planning, uncertain knowledge, reasoning in engineering design, simple and complex decision making, and time varying systems.
Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 729.
EECE 472. Computer Networks. 3 Credits.
The course describes and investigates Local and Wide Area Networks. Description of topologies and protocols for ETHERNET and TOKEN RING. The OSI model and applicability to LANs. IPX/SPX and TCP/IP protocols. Protocols stacks for PC'S. Server based and peer to peer networks. Network operating systems including NETWARE and NT Server Connectivity devices, hubs, bridges, switches, and routers. The Internet and Internet access. WANs including ATM, SONET, ISDN, and other high speed networks. Prerequisites: EECE 304 and EECE 306.
EECE 473. Operating Systems for Computer Engineering. 3 Credits.
A study of the modular design of operating systems and device drivers. Demand paging and virtual memory; scheduling algorithms, race conditions between processes; file systems, real time operating systems analytic tools for the evaluation of operating systems. Computer engineering applications. Prerequisite: EECE-232 or equivalent. Lecture with embedded lab.
Cross-listed with ECEG 728.
EECE 474. Modern Communication Systems. 3 Credits.
Digital and analog wireless and wired communications systems, including satellite communications and personal mobile communication systems. Techniques used in modern communication systems such as source coding, channel coding, multiplexing, multiple access, spread spectrum, cellular concepts. Passband digital transmission, and basics of cognitive and software radio. Lecture +Labs. Prerequisites: EECE 303 and EECE 315.
EECE 475. Computer Network Architecture. 3 Credits.
This course focuses on providing the skills and knowledge necessary to install, operate, and troubleshoot a small branch office Enterprise network, including configuring a switch, a router, and connecting to a WAN and implementing network security. A Student should be able to complete configuration and implementation of a small branch office network. Finally, this course will link the contents to the modern networking elements such as Network Function Virtualization and the Software Defined Networks.
Prerequisites: EECE 210 and EECE 321.
Cross-listed with ECEG 727.
EECE 476. Object-Oriented Programming and Data Structures for Computer Engineering. 3 Credits.
Objected-oriented programming, classes, objects, abstraction, inheritance, polymorphism. Data structures, list, trees, stacks, queues, search trees, hash tables, sorting algorithms. Applications to computer engineering problems. Labs. Prerequisites: EECE 210 and EECE 321.
EECE 477. Power & Energy Systems. 3 Credits.
Modern power system/energy conversion operation. Models for interconnected power grids, transmission lines, transformers, and power flow analysis. Development of basic power flow digital simulation programs and run power labs. Prerequisites: EECE 303 and EECE 305.
Cross-listed with ECEG 736.
EECE 478. Applied Data Mining for Engineers. 3 Credits.
This course will provide students with an understanding of fundamental data mining methodologies and with the ability to formulate and solve problems with them. Special emphasis attention will be paid to practical, efficient and statistically sound techniques. Hands-on experience with data mining software, primarily R, to allow development of basic execution skills. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 705.
EECE 482. Grid Integration of Wind Energy. 3 Credits.
The objective of this course is to familiarize students with various essential aspects in harnessing wind energy and its conversion and delivery as electricity. A broad understanding of essential elements in wind-electric systems: turbines, wind- plant development and their integration into the utility grid, environmental impacts, wind forecasting and more. Prerequisites: EECE 304 and EECE 306.
Cross-listed with ECEG 782.
EECE 488. Cyber-Physical Systems Security. 3 Credits.
Cyber-Physical Systems (CPS) integrate physical components and computational capabilities, connected through networks, to interact and collaborate with each other and with the physical world. Security architectures for CPS, secure communication protocols, mitigation strategies, intrusion detection and prevention, and case studies on CPS security incidents. Prerequisites: EECE 210 and EECE 321.
EECE 490. Cybersecurity Systems Fundamentals. 3 Credits.
This course provides a broad introduction and understanding of fundamental principles, concepts and techniques in cybersecurity to develop secure systems and protect sensitive information. It covers various topics, including cryptography, access control, network security, system security in both software and hardware aspects. Prerequisites: EECE 210 and EECE 321.
EECE 491. Special Topics in Electrical and/or Computer Engineering. 3 Credits.
Topics of current interest to senior electrical engineering students. Subject matter will be announced in advance of semester offering. Written permission of the chair is required. Prerequisites: EECE 304 and EECE 306.
EECE 492. Special Topics in Power Systems. 3 Credits.
Topics of current interest to senior electrical engineering students focusing on power systems. Subject matter will be announced in advance of semester offering. Prerequisites: EECE 304 and EECE 306.
EECE 493. Special Topics in Cybersecurity. 3 Credits.
This course offers an in-depth exploration of emerging and specialized areas within the field of cybersecurity. Topics of current interest to senior electrical engineering and computer engineering students. Subject matter will be announced in advance of semester offering.
Prerequisites: EECE 210 and EECE 321.
EECE 494. Special Topics in Artificial Intelligence (AI) in Electrical and Computer Engineering. 3 Credits.
On a variety of levels, the course explores Artificial Intelligence (AI): systems and tool chains for AI engineers in depth. Topics of current interest to senior electrical engineering and computer engineering students. The subject matter of the course will be announced in advance of the semester.
Prerequisites: EECE 304 and EECE 306.