Electrical Engineering/Telecommunications Engineering

Master of Science in Electrical/Telecommunications Engineering (MSEET): 45.0 - 48.0 quarter credits
Doctor of Philosophy: 90.0 quarter credits

About the Program

Fueled by the rapid spread of technologies such as electronic mail, cellular and mobile phone systems, interactive cable television, and the information superhighway, Drexel's program in Telecommunications Engineering responds to the growing demand for engineers with telecommunications expertise. The program combines a strong foundation in telecommunications engineering with training in other important issues such as global concerns, business, and information transfer and processing.

Drexel University's program in Telecommunications Engineering combines the expertise of its faculty in electrical and computer engineering, business, information systems, and humanities. Through its interdisciplinary approach, Drexel's Telecommunications Engineering program trains and nurtures the complete telecommunications engineer.

The MS in Electrical Engineering/Telecommunications Engineering degree is awarded to students who demonstrate in-depth knowledge of the field. The average time required to complete the master's degree is two year of full-time or three years of part-time study.

For more information, visit the Department of Electrical and Computer Engineering’s web site.

Admission Requirements

Applicants must meet the general requirements for graduate admission, which include at least a 3.0 GPA for the last two years of undergraduate study and for any graduate level study undertaken, and are required to hold a bachelor of science degree in electrical engineering or a related field. Applicants whose undergraduate degrees are not in the field of electrical engineering may be required to take a number of undergraduate courses. The GRE General Test is required of applicants for full-time MS and PhD programs. Applicants whose native language is not English and who do not have a previous degree from a US institution are required to take the Test of English as a Foreign Language (TOEFL).

For additional information on how to apply, visit Drexel's Admissions page for Electrical-Telecommunications Engineering.

MS in Electrical and Telecommunications Engineering

The Master of Science in Electrical and Telecommunications Engineering curriculum encompasses 45.0 or 48.0 (with the Graduate Co-Op) approved credit hours, chosen in accordance with the following requirements and a plan of study arranged with the departmental graduate advisor in consultation with the student's research advisor (if applicable). This plan of study must be filed in the Department of Electrical and Computer Engineering and approved with the departmental graduate advisor before the end of the first quarter for a full-time student, or by the end of the first year for a part-time student. 

Degree Requirements

A total of at least 30.0 credit hours must be taken from among the graduate course offerings of the Department of Electrical and Computer Engineering. These credits must be taken at Drexel University. No transfer credit may be used to fulfill these requirements, regardless of content equivalency.

Telecommunications Engineering (ECET) Courses6.0
Telecommunications Engineering Elective (ECEC, ECEE, ECES, ECET) Courses15.0
General Electrical and Computer Engineering (ECEC, ECEE, ECEP, ECES, ECET) Courses9.0
Elective Courses15.0
Total Credits45.0

 

With the remaining required 15.0 credit hours, students may take graduate coursework, subject to the approval of the departmental graduate advisor, in electrical and computer engineering, mathematics, physics or other engineering disciplines.

In addition, students pursuing an MS in Electrical and Telecommunications Engineering are allowed and strongly encouraged to take the following course as part of their required 15.0 credit hours:

  • COM 650 Telecommunications Policy in the Information Age


Although not required, students are encouraged to complete a master’s thesis as part of the MS studies. Those students who choose the thesis option may count up to 9.0 research/thesis credits as part of their required credit hour requirements.

Graduate Co-Op Program

Students may choose to participate in the Graduate Co-Op Program, where 6.0 credit hours can be earned for a six month cooperative education experience in industry, working on curriculum related projects. The total number of required credit hours is increased to 48 for those students who choose to pursue the Graduate Co-Op option. This change represents an increase in non-departmental required credit hours to a total of 18.0 credit hours, 6.0 of which are earned from the cooperative education experience.

Please note that ECEC 500 (Fundamentals of Computer Hardware) and ECEC 600 (Fundamentals of Computer Networks) do not count toward the credit requirements to complete the MS in Electrical Engineering degree program.

For more information on curricular requirements, visit the Department of Electrical and Computer Engineering’s web site.

PhD in Electrical Engineering

General Requirements
The following general requirements must be satisfied in order to complete the PhD in Electrical Engineering:

  • 90.0 credit hours total
  • candidacy examination
  • research proposal
  • dissertation defense

Students entering with a master’s degree in electrical or computer engineering or a related field will be considered a post-masters PhD student and will only be required to complete a total of 45.0 credit hours, in accordance with University policy.

Curriculum

Appropriate coursework is chosen in consultation with the student’s research advisor. A plan of study must be developed by the student to encompass the total number of required credit hours. Both the departmental graduate advisor and the student’s research advisor must approve this plan.

Candidacy Examination

The candidacy examination explores the depth of understanding of the student in his/her specialty area. The student is expected to be familiar with, and be able to use, the contemporary tools and techniques of the field and to demonstrate familiarity with the principal results and key findings.

The student, in consultation with his/her research advisor, will declare a principal technical area for the examination. The examination includes the following three parts:

  • A self-study of three papers from the archival literature in the student’s stated technical area, chosen by the committee in consultation with the student.
  • A written report (15 pages or less) on the papers, describing their objectives, key questions and hypotheses, methodology, main results and conclusions. Moreover, the student must show in an appendix independent work he/she has done on at least one of the papers – such as providing a full derivation of a result or showing meaningful examples, simulations or applications.
  • An oral examination which takes the following format:
    • A short description of the student’s principal area of interest (5 minutes, by student).
    • A review of the self-study papers and report appendix (25-30 minutes, by student).
    • Questions and answers on the report, the appendix and directly related background (40-100 minutes, student and committee).

In most cases, the work produced during the candidacy examination will be a principal reference for the student’s PhD dissertation; however, this is not a requirement.

Research Proposal

After having attained the status of PhD Candidate, each student must present a research proposal to a committee of faculty and industry members, chosen with his/her research advisor, who are knowledgeable in the specific area of research. This proposal should outline the specific intended subject of study, i.e., it should present a problem statement, pertinent background, methods of study to be employed, expected difficulties and uncertainties and the anticipated form, substance and significance of the results.

The purpose of this presentation is to verify suitability of the dissertation topic and the candidate's approach, and to obtain the advice and guidance of oversight of mature, experienced investigators. It is not to be construed as an examination, though approval by the committee is required before extensive work is undertaken. The thesis proposal presentation must be open to all; announcements regarding the proposal presentation must be made in advance.

The thesis advisory committee will have the sole responsibility of making any recommendations regarding the research proposal. It is strongly recommended that the proposal presentation be given as soon as possible after the successful completion of the candidacy examination. The student must be a PhD candidate for at least one year before he/she can defend his/her doctoral thesis.

Dissertation Defense

Dissertation Defense procedures are described in the Office of Graduate Studies policies regarding Doctor of Philosophy Program Requirements. The student must be a PhD candidate for at least one year before he/she can defend his/her doctoral thesis.

Dual Degree

The ECE Department offers outstanding students the opportunity to receive two diplomas (BS and MS) at the same time. The program requires five (5) years to complete. Participants, who are chosen from the best undergraduates students, work with a faculty member on a research project and follow a study plan that includes selected graduate classes. This program prepares individuals for careers in research and development; many of its past graduates continued their studies toward a PhD.

For more information on eligibility, academic requirements, and tuition policy visit the Engineering Combined BS/MS page.


Facilities

Drexel University and the Electrical and Computer Engineering Department are nationally recognized for a strong history of developing innovative research. Research programs in the ECE Department prepare students for careers in research and development, and aim to endow graduates with the ability to identify, analyze, and address new technical and scientific challenges. The ECE Department is well equipped with state-of-the-art facilities in each of the following ECE Research laboratories:  

Research Laboratories at the ECE Department

Adaptive Signal Processing and Information Theory Research Group

The Adaptive Signal Processing and Information Theory Research Group conducts research in the area of signal processing and information theory. Our main interests are belief/expectation propagation, turbo decoding and composite adaptive system theory. We are currently doing projects on the following topics:
i) Delay mitigating codes for network coded systems,
ii) Distributed estimation in sensor networks via expectation propagation,
iii) Turbo speaker identification,
iv) Performance and convergence of expectation propagation,
v) Investigating bounds for SINR performance of autocorrelation based channel shorteners.

Applied Networking Research Lab

Applied Networking Research Lab (ANRL) projects focus on modeling and simulation as well as experimentation in wired, wireless and sensor networks. ANRL is the home of MuTANT, a Multi-Protocol Label Switched Traffic Engineering and Analysis Testbed composed of 10 high-end Cisco routers and several PC-routers, also used to study other protocols in data networks as well as automated network configuration and management. The lab also houses a sensor network testbed.

Bioimage Laboratory

Uses computer gaming hardware for enhanced and affordable 3-D visualization, along with techniques from information theory and machine learning to combine the exquisite capabilities of the human visual system with computational sensing techniques for analyzing vast quantities of image sequence data.

Data Fusion Laboratory

The Data Fusion Laboratory investigates problems in multisensory detection and estimation, with applications in robotics, digital communications, radar, and target tracking. Among the projects in progress: computationally efficient parallel distributed detection architectures, data fusion for robot navigation, modulation recognition and RF scene analysis in time-varying environments, pattern recognition in biological data sequences and large arrays, and hardware realizations of data fusion architectures for target detection and target tracking.

Drexel Network Modeling Laboratory

The Drexel Network Modeling Laboratory investigates problems in the mathematical modeling of communication networks, with specific focus on wireless ad hoc networks, wireless sensor networks, and supporting guaranteed delivery service models on best effort and multipath routed networks. Typical methodologies employed in our research include mathematical modeling, computer simulation, and performance optimization, often with the end goal of obtaining meaningful insights into network design principles and fundamental performance tradeoffs.

Drexel Power-Aware Computing Laboratory

The Power-Aware Computing Lab investigates methods to increase energy efficiency across the boundaries of circuits, architecture, and systems. Our recent accomplishments include the Sigil profiling tool, scalable modeling infrastructure for accelerator implementations, microarchitecture-aware VDD gating algorithms, an accelerator architecture for ultrasound imaging, evaluation of hardware reference counting, hardware and operating system support for power-agile computing, and memory systems for accelerator-based architectures.

Drexel University Nuclear Engineering Education Laboratory

The field of nuclear engineering encompasses a wide spectrum of occupations, including nuclear reactor design, medical imaging, homeland security, and oil exploration. The Drexel University Nuclear Engineering Education Laboratory (DUNEEL) provides fundamental hands on understanding for power plant design and radiation detection and analysis. Software based study for power plant design, as well as physical laboratory equipment for radiation detection, strengthen the underlying concepts used in nuclear engineering such that the student will comprehend and appreciate the basic concepts and terminology used in various nuclear engineering professions. Additionally, students use the laboratory to develop methods for delivering remote, live time radiation detection and analysis. The goal of DUNEEL is to prepare students for potential employment in the nuclear engineering arena. 

Drexel VLSI Laboratory

The Drexel VLSI Laboratory investigates problems in the design, analysis, optimization and manufacturing of high performance (low power, high throughput) integrated circuits in contemporary CMOS and emerging technologies. Suited with industrial design tools for integrated circuits, simulation tools and measurement beds, the VLSI group is involved with digital and mixed-signal circuit design to verify the functionality of the discovered novel circuit and physical design principles. The Drexel VLSI laboratory develops design methodologies and automation tools in these areas, particularly in novel clocking techniques, featuring resonant clocking, and interconnects, featuring wireless interconnects.

Drexel Wireless Systems Laboratory

The Drexel Wireless Systems Laboratory (DWSL) contains an extensive suite of equipment for constructing, debugging, and testing prototype wireless communications systems. Major equipment within DWSL includes:

  • three software defined radio network testbeds (HYDRA, USRP, and WARP) for rapidly prototyping radio, optical and ultrasonic communications systems,
  • a TDK RF anechoic chamber and EMSCAN desktop antenna pattern measurement system,
  • a materials printer and printed circuit board milling machine for fabricating conformal antennas and
  • wireless protocol conformance testing equipment from Aeroflex.

The lab is also equipped with network analyzers, high speed signal generators, oscilloscopes, and spectrum analyzers as well as several Zigbee development platforms for rapidly prototyping sensor networks.

DWSL personnel also collaborate to create wearable, fabric based transceivers through collaboration with the Shima Seiki Haute Laboratory in the Drexel ExCITe Center. The knitting equipment at Drexel includes sixteen SDS-ONE APEX3 workstations and four state-of-the-art knitting machines. The workstations accurately simulate fabric construction and provide researchers and designers the opportunity to program, create and simulate textile prototypes, import CAD specifications of final products, and produce made-to-measure or mass-produced pieces on Shima Seiki knitting machines. For testing smart textiles for biomedical, DWSL personnel also have collaborators in the Center for Interdisciplinary Clinical Simulation and Practice (CICSP) in the Drexel College of Medicine which provides access to medical mannequin simulators.

Ecological and Evolutionary Signal-processing and Informatics Laboratory

The Ecological and Evolutionary Signal-processing and Informatics Laboratory (EESI) seeks to solve problems in high-throughput genomics and engineer better solutions for biochemical applications. The lab's primary thrust is to enhance the use of high-throughput DNA sequencing technologies with pattern recognition and signal processing techniques. Applications include assessing the organism content of an environmental sample, recognizing/classifying potential and functional genes, inferring environmental factors and inter-species relationships, and inferring microbial evolutionary relationships from short-read DNA/RNA fragments. The lab also investigates higher-level biological systems such as modeling and controlling chemotaxis, the movement of cells.

Electric Power Engineering Center

This newly established facility makes possible state-of-the-art research in a wide variety of areas, ranging from detailed theoretical model study to experimental investigation in its high voltage laboratories. The mission is to advance and apply scientific and engineering knowledge associated with the generation, transmission, distribution, use, and conservation of electric power. In pursuing these goals, this center works with electric utilities, state and federal agencies, private industries, nonprofit organizations and other universities on a wide spectrum of projects. Research efforts, both theoretical and experimental, focus on the solution of those problems currently faced by the electric power industry. Advanced concepts for electric power generation are also under investigation to ensure that electric power needs will be met at the present and in the future.

Electronic Design Automation Facility

Industrial-grade electronic design automation software suite and intergrated design environment for digital, analog and mixed-signal systems development. Field Programmable Gate Array (FPGA) development hardware. Most up-to-date FPGA/embedded system development hardware kits. Printed circuit board production facility. Also see Drexel VLSI Laboratory.

Microwave-Photonics Device Laboratories

The laboratory is equipped with test and measurement equipment for high-speed analog and digital electronics and fiber optic systems. The test equipment includes network analyzers from Agilent (100kHz- 1.3 GHz and 45 Mhz-40 GHz), and Anritsu (45 MHz-6 GHz); spectrum analyzers from Tektronix, HP, and Agilent with measurement capability of DC to 40 GHz and up to 90 GHz using external mixers; signal generators and communication channel modulators from HP, Rhode-Schwartz, Systron Donner, and Agilent; microwave power meter and sensor heads, assortment of passive and active microwave components up to 40 GHz ; data pattern generator and BER tester up to 3Gb/s; optical spectrum analyzer from Anritsu and power meters from HP; single and multimode fiber optic based optical transmitter and receiver boards covering ITU channels at data rates up to 10Gb/s; passive optical components such as isolator, filter, couplers, optical connectors and fusion splicer; LPKF milling machine for fabrication of printed circuit boards; wire-bonding and Cascade probe stations; Intercontinental test fixtures for testing of MMIC circuits and solid-state transistors; state-of-the-art microwave and electromagnetic CAD packages such as Agilent ADS, ANSYS HFSS, and COMSOL multi-physics module.

Music and Entertainment Technology Laboratory

The Music and Entertainment Technology Laboratory (MET-lab) is devoted to research in digital media technologies that will shape the future of entertainment, especially in the areas of sound and music. We employ digital signal processing and machine learning to pursue novel applications in music information retrieval, music production and processing technology, and new music interfaces. The MET-lab is also heavily involved in outreach programs for K-12 students and hosts the Summer Music Technology program, a one-week learning experience for high school students. Lab facilities include a sound isolation booth for audio and music recording, a digital audio workstation running ProTools, two large multi-touch display interfaces of our own design, and a small computing cluster for distributed processing.

NanoPhotonics+ Lab

Our research is primarily in the area of nanophotonics with a focus on the nanoscale interaction of light with matter. Interests include: liquid crystal/polymer composites for gratings, lenses and HOEs; liquid crystal interactions with surfaces and in confined nanospaces; alternative energy generation through novel photon interactions; ink-jet printed conducting materials for RF and photonic applications; and the creation and development of smart textiles technologies including soft interconnects, sensors, and wireless implementations.

Opto-Electro-Mechanical Laboratory

This lab concentrates on the system integration on optics, electronics, and mechanical components and systems, for applications in imaging, communication, and biomedical research. Research areas include: Programmable Imaging with Optical Micro-electrical-mechanical systems (MEMS), in which microscopic mirrors are used to image light into a single photodetector; Pre-Cancerous Detection using White Light Spectroscopy, which performs a cellular size analysis of nuclei in tissue; Free-space Optical Communication using Space Time Coding, which consists of diffused light for computer-to-computer communications, and also tiny lasers and detectors for chip-to-chip communication; Magnetic Particle Locomotion, which showed that particles could swim in a uniform field; and Transparent Antennas using Polymer, which enables antennas to be printed through an ink-jet printer.

Plasma and Magnetics Laboratory

Research is focused on applications of electrical and magnetic technologies to biology and medicine. This includes the subjects of non-thermal atmospheric pressure plasma for medicine, magnetic manipulation of particles for drug delivery and bio-separation, development of miniature NMR sensors for cellular imaging and carbon nanotube cellular probes.

Power Electronics Research Laboratory

The Power Electronics Research Laboratory (PERL) is involved in circuit and design simulation, device modeling and simulation, and experimental testing and fabrication of power electronic circuits. The research and development activities include electrical terminations, power quality, solar photovoltaic systems, GTO modeling, protection and relay coordination, and solid-state circuit breakers. The analysis tools include EMPT, SPICE, and others, which have been modified to incorporate models of such controllable solid-state switches as SCRs, GTOs, and MOSFETs. These programs have a wide variety and range of modeling capabilities used to model electromagnetics and electromechanical transients ranging from microseconds to seconds in duration. The PERL is a fully equipped laboratory with 42 kVA AC and 70 kVA DC power sources and data acquisition systems, which have the ability to display and store data for detailed analysis. Some of the equipment available is a distribution and HV transformer and three phase rectifiers for power sources and digital oscilloscopes for data measuring and experimental analysis. Some of the recent studies performed by the PERL include static VAR compensators, power quality of motor controllers, solid-state circuit breakers, and power device modeling which have been supported by PECO, GE, Gould, and EPRI.

RE Touch Lab

The RE Touch Lab is investigating the perceptual and mechanical basis of active touch perception, or haptics, and the development of new technologies for stimulating the sense of touch, allowing people to touch, feel, and interact with digital content as seamlessly as we do with objects in the real world.  We study the scientific foundations of haptic perception and action, and the neuroscientific and biomechanical basis of touch, with a long-term goal of uncovering the fundamental perceptual and mechanical computations that enable haptic interaction. We also create new technologies for rendering artificial touch sensations that simulate those that are experienced when interacting with real objects, inspired by new findings on haptic perception. 

Testbed for Power-Performance Management of Enterprise Computing Systems

This computing testbed is used to validate techniques and algorithms aimed at managing the performance and power consumption of enterprise computing systems. The testbed comprises a rack of Dell 2950 and Dell 1950 PowerEdge servers, as well as assorted desktop machines, networked via a gigabit switch. Virtualization of this cluster is enabled by VMWare's ESX Server running the Linux RedHat kernel. It also comprises of a rack of ten Apple Xserve machines networked via a gigabit switch. These servers run the OS X Leopard operating systems and have access to a RAID with TBs of total disk capacity.

Elec & Comp Engr-Computers Courses

ECEC 500 Fundamentals Of Computer Hardware 3.0 Credits

Covers computer organization and architecture; elements of computer hardware, processors, control units, and memories; hardware for basic mathematical operations; tradeoffs between speed and complexity; examples of embedded systems; microcontrollers; systems modeling.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 501 Computational Principles of Representation and Reasoning 3.0 Credits

This course presents fundamentals of discrete mathematics as applied within the computer engineering and manufacturing environment. Students are given the theoretical background in representation and reasoning for a broad variety of engineering problems solving situations. Entity-relational techniques of representation are demonstrated to evolve into the object-oriented approach. Various search techniques are applied in the cases of representing engineering systems by using theory of automata techniques.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 502 Principles of Data Analysis 3.0 Credits

This course presents theoretical methods and techniques of model development applicable within the computer engineering design and manufacturing environment. Students are given the theoretical background in data analysis (including "data mining"). Emphasis is on hybrid systems and discrete events systems. Various methods of recognizing regularities in data will be presented. Elements of the theory of clustering and classification will be dealt with for the paradigm of software and hardware problems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 503 Principles of Decision Making 3.0 Credits

This course presents theoretical fundamentals and engineering techniques of decision making and problem solving applicable within the computer engineering design and manufacturing environment. Students are given the theoretical background in optimization methods for a broad variety of situation. Elements of the theory of planning and on-line control of systems are presented within the scope of software and hardware computer design and control.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 511 Combinational Circuit Design 3.0 Credits

Representing arithmetic. Logic and syntax data for machine processing. Switching algebra: Boolean and multiple values. Identification and classification of functions. Realizing completely specified and incompletely specified Boolean functions. Issues in designing large communication/control Boolean functions. Fault and testing of Boolean function.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 512 Sequential Circuit Design 3.0 Credits

Finite automata and their realization by sequential machines, capabilities, transformation, and minimization of finite automata, linear finite automata. Clocked pulsed and level mode sequential circuits. Malfunctions in sequential circuits: hazards, races, lockouts, metastability. Issues of state assignment. Evolution of memory elements design: ROM vs. RAM vs. associative memory.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 511 [Min Grade: C]

ECEC 513 Design for Testability 3.0 Credits

Economics vs. Complexity vs. Strategy of Testing; Fault Models; Test Generation; Testability Analysis & Designing Testable Circuits; Testing Microprocessors, Memories and Computer Components; Test Data Compression; Fault Tolerant Hardware; Reliably vs. Availability; Redundancy and Error Correcting Codes.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 511 [Min Grade: C] and ECEC 512 [Min Grade: C]

ECEC 520 Dependable Computing 3.0 Credits

Fundamental design issues involved in building reliable, safety-critical, and highly available systems. Topics include testing and fault-tolerant design of VLSI circuits, hardware and software fault tolerance, information redundancy, and fault-tolerant distributed systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 541 Robotic Computer Interface Controls I 3.0 Credits

Covers sensors, actuators, mechanical components of robots, kinematics, inverse kinematics, dynamics, and equations of motion.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 542 Robotic Computer Interface Controls II 3.0 Credits

Covers the robot control problem, including PD, PID, position, force and hybrid controllers, resolved rate and acceleration control, and multiprocessor architecture.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 641 [Min Grade: C] and ECES 643 [Min Grade: C] and ECEC 541 [Min Grade: C]

ECEC 543 Robotic Computer Interface Controls III 3.0 Credits

Covers non-linear control techniques, FLDT, and advanced topics.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 542 [Min Grade: C]

ECEC 571 Introduction to VLSI Design 3.0 Credits

This is an introductory course where systematic understanding, design and analysis of digital VLSI integrated circuits will be covered. The course will begin with a review of CMOS transistor operation and semiconductor processes. Logic design with CMOS transistor and circuit families will be described. Specifically, layout, design rules, and circuit simulation will be addressed. Performance metrics will be analyzed in design and simulation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 572 Custom VLSI Design & Analysis I 3.0 Credits

This is the first of two courses offered on Custom VLSI circuit and systems design and analysis. An understanding of VLSI integrated circuits is achieved through circuit design and analysis. This course focuses exclusively on high performance digital CMOS VLSI circuit and systems design, although some topics on mixed-signal circuits are also addressed. Design and analysis of VLSI integrated circuits will be covered from the circuits and systems design perspectives. First, a thorough analysis of interconnect networks is presented. The second part of the class focuses on synchronization of high performance ICs.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 571 [Min Grade: C]

ECEC 573 Custom VLSI Design & Analysis II 3.0 Credits

This is the second of two courses offered on Custom VLSI circuit and systems design and analysis. An understanding of VLSI integrated circuits is achieved through circuit design and analysis. This course focuses exclusively on high performance digital CMOS VLSI circuit and systems design, although some topics on mixed-signal circuits are also addressed. The primary focus is on-chip power management. Power generation techniques are discussed and different power converters are analyzed. Power distribution networks are presented with a focus on the different distribution architectures and output impedance characteristics. Techniques to reduce power supply noise are also provided. A secondary focus examines substrate noise in mixed-signal systems and techniques to reduce substrate noise.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 571 [Min Grade: C]

ECEC 574 ASIC Design I 3.0 Credits

This course will focus exclusively on digital CMOS Application Specific Integrated Circuit (ASIC) systems design and automation. The ASIC physical design flow, including logic synthesis, floorplanning, placement, clock tree synthesis, routing and verification will be presented. These back-end physical design flow steps will also be covered through hands-on practice using industrial VLSI CAD tools. Contemporary design practices will be reviewed and presented in experiments.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 575 ASIC Design II 3.0 Credits

Design and analysis of VLSI integrated circuits will be covered from a systems design perspective. System timing, arithmetic building block and memory block design processes will be presented. Design tasks in a quarter-long, small-complexity processor design project will cover the back-end of the IC design flow range, from RTL synthesis to timing and power analysis. Projects will be performed in a hierarchical group, similar to an industrial setting, with other graduate and undergraduate students.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 572 [Min Grade: C]

ECEC 600 Fundamentals of Computer Networks 3.0 Credits

Fundamentals design principles of ATM, Internet and local area networks; protocol layers and the Internet Architecture; medium access protocols; application protocols and TCP/IP utilities; basic principles and virtual circuit switching; naming and addressing; flow and congestion control protocols; routing algorithms; Quality-of-Service in computer networks; security issues in networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 621 High Performance Computer Architecture 3.0 Credits

Maximizing single processor performance. Concepts and techniques for design of computer systems. Processor design, instruction set architecture design and implementation, memory hierarchy, pipelines processors, bus bandwidth, processor/memory interconnections, cache memory, virtual memory, advanced I/O systems, performance evaluation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 622 Parallel Computer Architecture 3.0 Credits

Advanced techniques of computer design. Use of parallel processing to achieve high performance levels. Fine and coarse grained parallelism. Multiple CPU parallelism, through multiprocessors, array and vector processors. Dataflow architectures and special purpose processors. Design implications of memory latency and bandwidth limitations. Speedup problems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 621 [Min Grade: C]

ECEC 623 Advanced Topics in Computer Architecture 3.0 Credits

This course teaches advanced concepts of modern computer architecture and introduces the current challenges faced by computer architects. These challenges include power consumption, transistor variability, and processor heterogeneity. Students develop their research skills through a self directed research project with a final presentation and conference style writeup.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 621 [Min Grade: C]

ECEC 631 Principles of Computer Networking 3.0 Credits

Principles of circuit switching, packet switching and virtual circuits; protocol layering; application layer protocols for e-mail and web applications; naming and addressing; flow control and congestion avoidance with TCP; Internet Protocol (IP); routing algorithms; router architectures; multicast protocols; local area network technologies and protocols; issues in multimedia transmissions; scheduling and policing; Quality-of-Service and emerging Internet service architectures; principles of cryptography.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 632 Performance Analysis of Computer Networks 3.0 Credits

Covers probability theory and its applications to networks, random variable and random processes; Markov chains, multi-dimensional Markov chains; M/M/1, M/M/m, M/M/m/m, M/G/1 and G/G/1 queueing systems and their applications in computer networks; analysis of networks of queues: Kleinrock Independence Approximation; Time-reversibility and Burke's theorem; Jackson's theorem; the phenomenon of long-range dependence and its implications in network design and traffic engineering.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 631 [Min Grade: C]

ECEC 633 Advanced Topics in Computer Networking 3.0 Credits

perspectives in the areas of switch/router architectures, scheduling for best-effort and guaranteed services, QoS mechanisms and architectures, web protocols and applications, network interface design, optical networking, and network economics. The course also includes a research project in computer networking involving literature survey, critical analysis, and finally, an original and novel research contribution.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 631 [Min Grade: C] and ECEC 632 [Min Grade: C]

ECEC 654 Knowledge Engineering I 3.0 Credits

Covers conceptual modeling, including an overview of knowledge representation. Includes semantic networks, reduced semantic networks, logic of incomplete knowledge bases, extensional semantic networks, and applications of conceptual models.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 655 Knowledge Engineering II 3.0 Credits

Covers expert systems, including language and tools of knowledge engineering. Includes reasoning about reasoning, design and evaluation, heuristics in expert systems, expert systems for decision support, and expert systems in conceptual design.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 654 [Min Grade: C]

ECEC 656 Knowledge Engineering III 3.0 Credits

Covers information-intensive systems, including information representation in autonomous systems. Includes clauses and their validation; clustering in linguistic structures; linguistic and pictorial knowledge bases; discovery in mathematics, including am; and methods of new knowledge generation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 655 [Min Grade: C]

ECEC 661 VLSI Design 3.0 Credits

Covers CMOS design styles, techniques, and performance; VLSI computer hardware, arithmetic units, and signal processing systems; and cat tools for layout design and simulation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 662 VLSI Array Processors I 3.0 Credits

Covers VLSI testing, including design for testability and parallel computer architectures; signal and image processing algorithms and mapping algorithms onto array structures; and systolic array processors.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 661 [Min Grade: C]

ECEC 663 VLSI Array Processors II 3.0 Credits

Covers wavefront array processors; matching hardware to arrays; hardware design, systems design, and fault-tolerant design; and implementations and VLSI design projects.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 662 [Min Grade: C]

ECEC 671 Electronic Design Automation for VLSI Circuits I 3.0 Credits

This course focuses on the electronic design automation problems in the design process of VLSI integrated circuits. In this first quarter of the course, algorithms, techniques and heuristics structuring the foundations of contemporary VLSI CAD tools are presented. Boolean algebra, graph theory, logic minimization and satisfiability topics are presented.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECEC 672 Electronic Design Automation for VLSI Circuits II 3.0 Credits

This course focuses on the electronic design automation problems in the design process of VLSI integrated circuits. In this second quarter of the course, physical VLSI design steps of technology mapping, floor planning, placement, routing and timing and presented individual and team-based small-to-medium scale programming projects are assigned.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 671 [Min Grade: C]

ECEC 673 Deep Sub-Micron Integrated Circuit Design 3.0 Credits

This course focuses on the design challenges of digital VLSI integrated circuits in deep sub-micron manufacturing technologies. Automation challenges and high-performance circuit design techniques such as low-power and variation-aware design are presented. The course material is delivered in a lecture format structured on recent presentations, articles, and tutorials.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECEC 671 [Min Grade: C]

ECEC 690 Special Topics Computer Engineering 9.0 Credits

Covers special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 697 Research in Computer Engineering 1.0-12.0 Credit

Research in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 699 Supervised Study in Computer Engineering 9.0 Credits

Supervised study in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 890 Advanced Special Topics in Computer Engineering 1.0-9.0 Credit

Covers advanced special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 891 Advanced Topics in Computer Engineering 0.5-9.0 Credits

Advanced topics in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 898 Master's Thesis in Computer Engineering 1.0-12.0 Credit

Master's thesis in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 997 Dissertation Research in Computer Engineering 1.0-12.0 Credit

Graded Ph.D. dissertation in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECEC 998 Ph.D. Dissertation in Computer Engineering 1.0-12.0 Credit

Ph.D. dissertation in computer engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

Elec & Computer Engr-Systems Courses

ECES 510 Analytical Methods in Systems 3.0 Credits

This course is intended to provide graduate student in the field of signal and image processing with the necessary mathematical foundation, which is prevalent in contemporary signal and image processing research and practice.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 511 Fundamentals of Systems I 3.0 Credits

Core course. Covers linear operators, including forms and properties (differential equations, transfer function, state space, causality, linearity, and time invariance); impulse response, including convolution, transition matrices, fundamental matrix, and linear dynamical system; definition, including properties and classification; representation, including block diagrams, signal flow, and analog and digital; properties, including controllability and observability; and eigenstructure, including eigenvalues and eigenvector and similarity transformations.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 512 Fundamentals of Systems II 3.0 Credits

Core course. Covers realization and identification, including minimal realization, reducibility and equivalence of models, and identification of systems; stability, including bounded input-bounded output, polynomial roots, and Lyapunov; and feedback compensation and design, including observers and controllers and multi-input/multi-output systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 511 [Min Grade: C]

ECES 513 Fundamentals of Systems III 3.0 Credits

Core course. Covers multivariable systems, numerical aspects of system analysis and design, design of compensators, elements of robustness, and robust stabilization.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 521 Probability & Random Variables 3.0 Credits

Probability concepts. Single and multiple random variables. Functions of random variables. Moments and characteristic functions. Random number and hypothesis testing. Maximum likelihood estimation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 522 Random Process & Spectral Analysis 3.0 Credits

Random Process. Poisson Process, Shot Noise. Gaussian Process. Matched Filters. Kalman Filters. Power Spectral Density. Autocorrelation and cross correlation. PSD estimation. Entropy. Markov Processes. Queuing Theory.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C]

ECES 523 Detection & Estimation Theory 3.0 Credits

Detection of signals in noise. Bayes criterion. NP criterion. Binary and M_ary hypotheses. Estimation of signal parameters. MLE and MAP estimation. 1D and 2D signals. ROC Analyses. Decision fusion.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C] and ECES 522 [Min Grade: C]

ECES 558 Digital Signal Processing for Sound & Hearing 3.0 Credits

Introduction to the computational modeling of sound and the human auditory system. Signal processing issues, such as sampling, aliasing, and quantization, are examined from an audio perspective. Covers applications including audio data compression (mp3), sound synthesis, and audio watermarking.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 559 Processing of the Human Voice 3.0 Credits

Introduction to the computational modeling of the human voice for analysis, synthesis, and recognition. Topics covered include vocal physiology, voice analysis-synthesis, voice data coding (for digital communications, VoIP), speaker identification, speech synthesis, and automatic speech recognition.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C] and ECES 558 [Min Grade: C]

ECES 561 Medical Robotics I 3.0 Credits

This course will introduce the emerging, multidisciplinary field of medical robotics. Topics include: introduction to robot architecture, kinematics, dynamics and control; automation aspects of medical procedures; safety, performance limitations; regulatory and economics and future developments.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 562 Medical Robotics II 3.0 Credits

This course will continue the introduction to the emerging, multidisciplinary field of medical robotics. Topics include: medical procedure automation; robot testing and simulation techniques; This is a project based course that will afford students the opportunity to work with existing medical robotic systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 561 [Min Grade: C]

ECES 604 Optimal Estimation & Stochastic Control 3.0 Credits

Introduction to control system problems with stochastic disturbances; linear state space filtering, Kalman Filtering, Non-linear systems; extended Kalman Filtering. Robust and H-infinity methods.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C] and ECES 521 [Min Grade: C]

ECES 607 Estimation Theory 3.0 Credits

General characteristics of estimators. Estimators: least squares, mean square, minimum variance, maximum a posteriori, maximum likelihood. Numerical solution. Sequential estimators.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 614 Passive Network Synthesis 3.0 Credits

An introduction to approximation theory; driving point functions; realizability by lumped-parameter circuits; positive real functions; properties of two and three element driving point functions and their synthesis; transfer function synthesis; all-pass networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 615 Analysis & Design of Linear Active Networks 3.0 Credits

DC and AC models of bipolar transistors and FETs; design of differential operational amplifiers; optimal design of broad-band IC amplifiers; design of tuned amplifiers; design for optimal power gain, distortion, and efficiency; noise in transistor circuits.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 621 Communications I 3.0 Credits

Covers modulation techniques: baseband PAM, passband PAM, QAM, and PSK; orthogonal signaling: FSK; symbol/vector detection: matched filter and correlation detector; sequence detection: ISI; equalization: adaptive and blind; carrier synchronization; and timing recovery.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 622 Communications II 3.0 Credits

Covers shot noise, noise in detectors, analog fiberoptic systems, carrier and subcarrier modulation, digital systems bit error rates for NRZ and RZ formats, coherent optical communication systems-heterodyne and homodyne systems, wavelength division multiplexing, system design concepts, power budgets, rise time budgets, and optical switching networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 623 Communications III 3.0 Credits

Covers fundamentals of information theory: information measure, entropy, and channel capacity; source encoding and decoding; rate distortion theory; linear codes; block codes; convolutional codes, Viterbi algorithm; encryption and decryption; and spread spectrum communications.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 631 Fundamentals of Deterministic Digital Signal Processing 3.0 Credits

Fundamentals of Deterministic Digital Signal Processing. This course introduces the fundamentals of deterministic signal processing.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 632 Fundamentals of Statistical Digital Signal Processing 3.0 Credits

Fundamentals of Statistical Deterministic Digital Signal Processing. The course covers topics on statistical signal processing related to data modeling, forecasting and system identification.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 640 Genomic Signal Processing 3.0 Credits

This course focuses on signal processing applied to analysis and design of biological systems. This is a growing area of interest with many topics ranging from DNA sequence analysis, to gene prediction, sequence alignment, and bio-inspired signal processing for robust system design.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 642 Optimal Control 3.0 Credits

Introduces the concept of optimal control first by static optimization for state space formulated systems. The concept is expanded as the linear quadratic regulator problem for dynamic systems allowing solution of the optimal control and suboptimal control problems for both discrete and continuous time. Additional topics include the Riccati equation, the tracking problem, the minimum time problem, dynamic programming, differential games and reinforcement learning. The course focuses on deriving, understanding, and implementation of the algorithms.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 643 Digital Control Systems Analysis & Design 3.0 Credits

Covers analysis and design of sampled-data control system using Z-transform and state-variable formulation, sampling, data reconstruction and error analysis, stability of linear and non-linear discrete time systems by classical and Lyapunov's second method, compensator design using classical methods (e.g., rootlocus) and computer-aided techniques for online digital controls, optimal control, discrete-time maximum principle, sensitivity analysis, and multirate sampled-data systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 513 [Min Grade: C]

ECES 644 Computer Control Systems 3.0 Credits

Introduction to the fundamentals of real-time controlling electromechanical dynamic systems, including modeling, analysis, simulation, stabilization and controller design. Control design approaches include: pole placement, quadratic and robust control performances.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 651 Intelligent Control 3.0 Credits

Concepts of Intelligence in Engineering Systems, Learning Automation, Principles of Knowledge Representation. Levels of Resolution and Nestedness. Organization of Planning: Axioms and Self-Evident Principles.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 660 Machine Listening and Music IR 3.0 Credits

This course introduces methods for the computational analysis, recognition, and understanding of sound and music from the acoustic signal. Covered applications include sound detection and recognition, sound source separation, artist and song identification, music similarity determination, and automatic transcription.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 670 Seminar in Systems I 2.0 Credits

Involves presentations focused on recent publications and research in systems, including communications, controls, signal processing, robotics, and networks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 671 Seminar in Systems II 2.0 Credits

Continues ECES 670.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 672 Seminar in Systems III 2.0 Credits

Continues ECES 671.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 681 Fundamentals of Computer Vision 3.0 Credits

Develops the theoretical and algorithmic tool that enables a machine (computer) to analyze, to make inferences about a "scene" from a scene's "manifestations", which are acquired through sensory data (image, or image sequence), and to perform tasks.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 682 Fundamentals of Image Processing 3.0 Credits

The course introduces the foundation of image processing with hands-on settings. Taught in conjunction with an imaging laboratory.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 631 [Min Grade: C]

ECES 684 Imaging Modalities 3.0 Credits

This course is intended to produce students and image processing with a background on image formation in modalities for non-invasive 3D imaging. The goal is to develop models that lead to qualitative measures of image quality and the dependence of quality imaging system parameters.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 685 Image Reconstruction Algorithms 3.0 Credits

This course is intended to provide graduate students in signal and image processing with an exposure to the design and evaluation of algorithms for tomographic imaging.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 684 [Min Grade: C] and BMES 621 [Min Grade: C]

ECES 690 Special Topics in Systems Engineering 9.0 Credits

Covers special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 697 Research In Systems Engineering 1.0-12.0 Credit

Research in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 699 Supervised Study in Systems Engineering 9.0 Credits

Supervised study in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 801 Advanced Topics in Systems I 3.0 Credits

Familiarizes students with current research results in their field of interest, specifically in works reported in such journals as The IEEE Transactions.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 802 Advanced Topics in Systems II 3.0 Credits

Continues ECES 801.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 803 Advanced Topics in Systems III 3.0 Credits

Continues ECES 802.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 811 Optimization Methods for Engineering Design 3.0 Credits

Applications of mathematical programming and optimization methods in engineering design problems such as networks, control, communication, and power systems optimization. Optimization problem definition in terms of objective function, design variables, and design constraints. Single variable and multivariable search methods for unconstrained and constrained minimization using Fibonacci, gradient, conjugate gradient, Fletcher-Powell methods and penalty function approach. Classical optimization--Lagrange multiplier, Kuhn-Tucker conditions. Emphasis is on developing efficient digital computer algorithms for design.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 812 Mathematical Program Engineering Design 3.0 Credits

Approximation theory projection theorem. Solution of nonlinear equations by Newton-Raphson method. Resource allocation, linear programming, and network flow problems. Integer programming for digital filter design and power system expansion problems. Gamory's algorithm.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 813 Computer-Aided Network Design 3.0 Credits

Simulation and circuit analysis programs: PCAP, MIMIC, GASP. Data structures. Algorithms. Languages. Interactive I/O. Integration of subroutines for simulation.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 817 Non-Linear Control Systems 3.0 Credits

Covers key topics of feedback linearization, sliding mode control, model reference adaptive control, self-tuning controllers and on-line parameter estimation. In addition additional no n-linear topics such as Barbalat’s Lemma, Kalman-Yakubovich Lemma, passivity, absolute stability, and establishing boundedness of signals are presented. The focus of the course is the understanding each of these algorithms in detail through derivation and their implementation through coding in Matlab and Simulink.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 513 [Min Grade: C]

ECES 818 Machine Learning & Adaptive Control 3.0 Credits

System identification and parameter estimation, gradient search, least squares and Neural Networks methods. Closed loop implementation of system learning and self-organizing controllers. Random searching learning systems.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 512 [Min Grade: C]

ECES 821 Reliable Communications & Coding I 3.0 Credits

Covers fundamentals of information theory, including measures of communication, channel capacity, coding for discrete sources, converse of coding system, noisy-channel coding, rate distortion theory for memoryless sources and for sources with memory, and universal coding.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C] and ECES 522 [Min Grade: C]

ECES 822 Reliable Communications & Coding II 3.0 Credits

Introduces algebra of coding, including groups, rings, fields, and vector fields. Covers finite fields, decoding circuitry, techniques for coding and decoding, linear codes, error-correction capabilities of linear codes, dual codes and weight distribution, important linear block codes, perfect codes, and Plotkin's and Varshamov's bounds.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 821 [Min Grade: C]

ECES 823 Reliable Communications & Coding III 3.0 Credits

Continues techniques for coding and decoding. Covers convolutional codes; Viterbi algorithm; BCH, cyclic, burst-error-correcting, Reed-Solomon, and Reed-Muller codes; and elements of cryptography.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 822 [Min Grade: C]

ECES 890 Advanced Special Topics in Systems Engineering 1.0-9.0 Credit

Covers advanced special topics of interest to students and faculty.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 898 Master's Thesis in Systems Engineering 1.0-12.0 Credit

Master's thesis in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 921 Reliable Communications & Coding I 3.0 Credits

College/Department: College of Engineering
Repeat Status: Not repeatable for credit

ECES 997 Dissertation Research in Systems Engineering 1.0-12.0 Credit

Graded Ph.D. dissertation in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

ECES 998 Ph.D. Dissertation in Systems Engineering 1.0-12.0 Credit

Ph.D. dissertation in systems engineering.

College/Department: College of Engineering
Repeat Status: Can be repeated multiple times for credit

Electrical and Computer Engineering Faculty

Fernand Cohen, PhD (Brown University). Professor. Surface modeling; tissue characterization and modeling; face modeling; recognition and tracking.
Kapil Dandekar, PhD (University of Texas-Austin) Director of the Drexel Wireless Systems Laboratory (DWSL); Associate Dean of Research, College of Engineering. Professor. Cellular/mobile communications and wireless LAN; smart antenna/MIMO for wireless communications; applied computational electromagnetics; microwave antenna and receiver development; free space optical communication; ultrasonic communication; sensor networks for homeland security; ultrawideband communication.
Afshin Daryoush, ScD (Drexel University). Professor. Digital and microwave photonics; nonlinear microwave circuits; RFIC; medical imaging.
Bruce A. Eisenstein, PhD (University of Pennsylvania) Interim Dean, College of Engineering. Professor. Pattern recognition; estimation; decision theory.
Adam K. Fontecchio, PhD (Brown University) Electrical and Computer Engineering. Professor. Electro-optics; remote sensing; active optical elements; liquid crystal devices.
Gary Friedman, PhD (University of Maryland-College Park). Professor. Biological and biomedical applications of nanoscale magnetic systems.
Eli Fromm, PhD (Jefferson Medical College) Roy A. Brothers University Professor / Director for Center of Educational Research. Professor. Engineering education; academic research policy; bioinstrumentation; physiologic systems.
Edwin L. Gerber, PhD (University of Pennsylvania) Assistant Department Head for Evening Programs. Professor. Computerized instruments and measurements; undergraduate engineering education.
Allon Guez, PhD (University of Florida). Professor. Intelligent control systems; robotics, biomedical, automation and manufacturing; business systems engineering.
Mark Hempstead, PhD (Harvard University) Junior Colehower Chair. Assistant Professor. Computer engineering; power-aware computing; computer architecture; low power VLSI Design; wireless sensor networks.
Peter R. Herczfeld, PhD (University of Minnesota) Lester A. Kraus Professor/Director, Center for Microwave/Lightwave Engineering. Professor. Lightwave technology; microwaves; millimeter waves; fiberoptic and integrated optic devices.
Leonid Hrebien, PhD (Drexel University) Graduate Advisor and Assistant Department Head for Graduate Affairs. Professor. Tissue excitability; acceleration effects on physiology; bioinformatics.
Paul R. Kalata, PhD (Illinois Institute of Technology). Associate Professor. Stochastic and adaptive control theory; identification and decision theory; Kalman filters.
Moshe Kam, PhD (Drexel University) Robert G. Quinn Professor of Electrical and Computer Engineering and Department Head. Professor. Decision fusion and sensor fusion; mobile robots (especially robot navigation); pattern recognition (especially in handwriting applications); optimization and control.
Nagarajan Kandasamy, PhD (University of Michigan). Associate Professor. Embedded systems, self-managing systems, reliable and fault-tolerant computing, distributed systems, computer architecture, and testing and verification of digital systems.
Bruce Katz, PhD (University of Illinois). Adjunct Professor. Speech communication and computer science; artificial intelligence.
Youngmoo Kim, PhD (MIT). Associate Professor. Audio and music signal processing, voice analysis and synthesis, music information retrieval, machine learning.
Timothy P. Kurzweg, PhD (University of Pittsburgh). Associate Professor. Optical MEM modeling and simulation; system-level simulation; computer architecture.
Karen Miu, PhD (Cornell University). Professor. Power systems; distribution networks; distribution automation; optimization; system analysis.
Bahram Nabet, PhD (University of Washington) Associate Dean for Special Projects, College of Engineering; Electrical and Computer Engineering. Professor. Optoelectronics; fabrication and modeling; fiber optic devices; nanoelectronics; nanowires.
Prawat Nagvajara, Ph.D. (Boston University). Associate Professor. System on a chip; embedded systems; power grid computation; testing of computer hardware; fault-tolerant computing; VLSI systems; error control coding.
Dagmar Niebur, Ph.D. (Swiss Federal Institute of Technology). Associate Professor. Intelligent systems; dynamical systems; power system monitoring and control.
Chika Nwankpa, PhD (Illinois Institute of Technology). Professor. Power system dynamics; power electronic switching systems; optically controlled high power switches.
Karkal S. Prahbu, PhD (Harvard University). Auxiliary Professor. Computer and software engineering; advanced microprocessors and distributed operating systems.
Gail L. Rosen, PhD (Georgia Institute of Technology). Associate Professor. Signal processing, signal processing for biological analysis and modeling, bio-inspired designs, source localization and tracking.
Kevin J. Scoles, PhD (Dartmouth College) Associate Dean, College of Engineering, Office of Student Services. Associate Professor. Microelectronics; electric vehicles; solar energy; biomedical electronics.
Harish Sethu, PhD (Lehigh University). Associate Professor. Protocols, architectures and algorithms in computer networks; computer security; mobile ad hoc networks; large-scale complex adaptive networks and systems.
P. Mohana Shankar, PhD (Indian Institute of Technology) Allen Rothwarf Professor of Electrical and Computer Engineering. Professor. Wireless communications; biomedical ultrasonics; fiberoptic bio-sensors.
Baris Taskin, PhD (University of Pittsburgh). Associate Professor. Electronic design automation (EDA) of integrated circuits, high-performance VLSI circuits and systems, sequential circuit timing and synchronization, system-on-chip (SOC) design, operational research, VLSI computer-aided design.
Lazar Trachtenberg, DSc (Israel Institute of Technology). Professor. Fault tolerance; multi-level logic synthesis; signal processing; suboptimal filtering.
Oleh Tretiak, ScD (MIT) Robert C. Disque Professor of Electrical and Computer Engineering. Professor. Image processing; tomography; image registration; pattern recognition.
John MacLaren Walsh, PhD (Cornell University). Assistant Professor. Performance and convergence of belief/expectation propagation and turbo decoding/equalization/synchronization, permeation models for ion channels, composite adaptive systems theory.
Steven Weber, PhD (University of Texas-Austin) Assistant Department Head for Graduate Affairs, Electrical and Computer Engineering. Associate Professor. Mathematical modeling of computer and communication networks, specifically streaming multimedia and ad hoc networks.
Jaudelice Cavalcante de Oliveira, PhD (Georgia Institute of Technology). Associate Professor. Next generation Internet; quality of service in computer communication networks; wireless networks.

Interdepartmental Faculty

Dov Jaron, PhD (University of Pennsylvania) Calhoun Distinguished Professor of Engineering in Medicine. Professor. Mathematical, computer and electromechanical simulations of the cardiovascular system.
Jeremy R. Johnson, PhD (Ohio State University). Professor. Computer algebra; parallel computations; algebraic algorithms; scientific computing.
John Lacontora, PhD (New Jersey Institute of Technology). Associate Research Professor. Service engineering; industrial engineering.
Ryszard Lec, PhD (University of Warsaw Engineering College). Professor. Biomedical applications of visoelastic, acoustoptic and ultrasonic properties of liquid and solid media.
Spiros Mancoridis, PhD (University of Toronto) Sr. Associate Dean for Computing and CCI Academic Affairs. Professor. Software engineering; software security; code analysis; evolutionary computation.
Karen Moxon, PhD (University of Colorado). Associate Professor. Cortico-thalamic interactions; neurobiological perspectives on design of humanoid robots.
Paul Y. Oh, PhD (Columbia University) Associate Department Head for External Affairs, Department of Mechanical Engineering and Mechanics. Professor. Smart sensors servomechanisms; machine vision and embedded microcomputers for robotics and mechatronics.
Banu Onaral, Ph.D. (University of Pennsylvania) H.H. Sun Professor / Director, School of Biomedical Engineering Science and Health Systems. Professor. Biomedical signal processing; complexity and scaling in biomedical signals and systems.
Kambiz Pourrezaei, PhD (Rensselaer Polytechnic University). Professor. Thin film technology; nanotechnology; near infrared imaging; power electronics.
William C. Regli, PhD (University of Maryland-College Park). Professor. Artificial intelligence; computer graphics; engineering design and Internet computing.
Arye Rosen, PhD (Drexel University) Biomedical Engineering and Electrical Engineering. Microwave components and subsystems; utilization of RF/microwaves and lasers in therapeutic medicine.
Jonathan E. Spanier, PhD (Columbia University). Associate Professor. Electronic, ferroic and plasmonic nanostructures and thin-film materials and interfaces; scanning probe microscopy; laser spectroscopy, including Raman scattering.
Aydin Tozeren, PhD (Columbia University) Distinguished Professor and Director, Center for Integrated Bioinformatics, School of Biomedical Engineering, Science & Health Systems. Professor. Breast cell adhesion and communication, signal transduction networks in cancer and epithelial cells; integrated bioinformatics, molecular profiling, 3D-tumors, bioimaging.
Aspasia Zerva, PhD (University of Illinois). Professor. Earthquake engineering; mechanics; seismicity; probabilistic analysis.

Emeritus Faculty

Richard L. Coren, PhD (Polytechnic Institute of Brooklyn). Professor Emeritus. Electromagnetic fields, antennas, shielding, RFI, cybernetics of evolving systems.
Robert Fischl, PhD (University of Michigan) John Jarem Professor Emeritus / Director, Center for Electric Power Engineering. Professor Emeritus. Power: systems, networks, controls, computer-aided design, power systems, solar energy.
Vernon L. Newhouse, PhD (University of Leeds) Disque Professor Emeritus. Professor Emeritus. Biomedical and electrophysics: ultrasonic flow measurement, imaging and texture analysis in medicine, ultrasonic nondestructive testing and robot sensing, clinical engineering.
Hun H. Sun, PhD (Cornell University) Ernest O. Lange Professor Emeritus. Professor Emeritus. Systems and signals in biomedical control systems.
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