Courses

The listing of a course description here does not guarantee a course’s being offered in a particular semester. Please refer to the published schedule of classes on the Student Link for confirmation a class is actually being taught and for specific course meeting dates and times.

• ENG BE 811: Part-Time PhD Internship in Biomedical Engineering
  This course provides BME PhD Students the opportunity to include a part-time (15-20 hours/week, for at least 12 weeks) paid internship experience as part of their professional training. The internship must be related to the student's area of study. International students require CPT authorization. Written summary required. Graded P/F. Prerequisite: Permission of advisor and an approved, part-time internship offer, at least two complete semesters in the BME PhD program.
• ENG BE 900: PhD Research
  Prerequisite: restricted to pre-prospectus PhD students. Participation in a research project under the direction of a faculty advisor leading to the preparation and defense of a PhD prospectus.
• ENG BE 951: Independent Study
  A course of reading under the direction of a faculty advisor covering subject matter not available in a lecture course. Final report or examination normally required.
• ENG BE 952: Mentored Project
  Students who are pursuing a project to satisfy their practicum requirement for the MS degree will register for up to 4 credits of this course. The course may be taken more than once for up to four credits (ex. two credits in Fall, two credits in Spring). Students will select a suitable project with a mentor that can be completed in 4 credits. The BME Graduate Committee must approve all proposed projects. Each student must write a project report and/or deliver a formal presentation at the end of the course that will be graded by their project mentor. All reports and presentation materials must be received by the BME Graduate Committee.
• ENG BE 954: MS Thesis
  Participation in a research project under the direction of a faculty advisor leading to the preparation of an original MS thesis. For students pursuing an MS thesis to satisfy the practicum requirement for the MS degree.
• ENG BE 991: PhD Dissertation
  Participation in a research project under the direction of a faculty advisor leading to the preparation and defense of an original PhD dissertation.
• ENG EC 311: Introduction to Logic Design
  Introduction to hardware building blocks used in digital computers. Boolean algebra, combinatorial and sequential circuits: analysis and design. Adders, multipliers, decoders, encoders, multiplexors. Programmable logic devices: read- only memory, programmable arrays, Verilog. Counters and registers. Includes lab.
• ENG EC 327: Introduction to Software Engineering
  This course aims to introduce students to software design, programming techniques, data structures, and software engineering principles. The course is structured bottom up, beginning with basic hardware followed by an understanding of machine language that controls the hardware and the assembly language that organizes that control. It then proceeds through fundamental elements of functional programming languages, using C as the case example, and continues with the principles of object-oriented programming, as principally embodied in C++ but also its daughter languages Java, C#, and objective C. The course will conclude with an introduction to elementary data structures and algorithmic analysis. Throughout, the course develops core competencies in software engineering, including programming style, optimization, debugging, compilation, and program management, utilizing a variety of Integrated Development Environments and operating systems.
• ENG EC 330: Applied Algorithms for Engineers
  Introduction to the general concept of algorithms. Efficiency and run-time of algorithms. Graph algorithms, priority queues, search trees. Various approaches to design of algorithms and data structures, together with their applications to numerical and non-numerical problems.
• ENG EC 381: Probability Theory in Electrical and Computer Engineering
  Introduction to modeling uncertainty in electrical and computer systems. Experiments, models, and probabilities. Discrete and continuous random variables. Reliability models for circuits. Probability distributions. Moments and expectations. Random vectors. Functions of random variables. Sums of random variables and limit theorems. Signal detection and estimation. Basic stochastic processes. Discrete-time Markov chains. State-diagrams. Applications to statistical modeling and interpretation of experimental data in computer, communication, and optical systems.
• ENG EC 400: Undergraduate Special Topics in Electrical & Computer Engineering
  Coverage of a specific topic in electrical and computer engineering at the undergraduate level. Subject matter varies from semester to semester; not offered every semester.
• ENG EC 401: Signals and Systems
  Cannot be taken for credit in addition to ENG BE 401. Continuous-time and discrete-time signals and systems. Convolution sum, convolution integral. Linearity, time-invariance, causality, and stability of systems. Frequency domain analysis of signals and systems. Filtering, sampling, and modulation. Laplace transform, z-transform, pole-zero plots. Linear feedback systems. Includes lab. Cannot be taken for credit in addition to ENG BE 403.
• ENG EC 402: Control Systems
  Analysis of linear feedback systems, their characteristics, performance, and stability. The Routh-Hurwitz, root-locus, Bode, and Nyquist techniques. Design and compensation of feedback control systems. Cannot be taken for credit in addition to ENG ME 403, ENG ME 404, or ENG BE 404.
• ENG EC 410: Introduction to Electronics
  Principles of diode, BJT, and MOSFET circuits. Graphical and analytical means of analysis. Piecewise linear modeling; amplifiers; digital inverters and logic gates. Biasing and small-signal analysis, microelectronic design techniques. Time-domain and frequency domain analysis and design. Includes lab.
• ENG EC 412: Analog Electronics
  Continuation of ENG EC 410. Topics include detailed analysis of differential amplifiers, design and principles of operational amplifier including multistage circuit structure, BJT, MOSFET, CMOS, and BiCMOS design principles, active filters and oscillators, negative and positive feedback, and power devices. Includes lab.
• ENG EC 413: Computer Organization
  Introduction to the fundamentals and design of computer systems. Topics covered include computer instruction sets, assembly language programming, arithmetic circuits, CPU design (data path and control, pipelining), performance evaluation, memory devices, memory systems including caching and virtual memory, and I/O. Project using design automation tools. Includes lab.
• ENG EC 414: Introduction to Machine Learning
  Linear regression. Maximum likelihood and maximum a posteriori estimation. Classification techniques, including na?ve Bayes, k-nearest neighbors, logistic regression, and support vector machines. Data visualization and feature extraction, including principal components analysis and linear projections. Clustering. Introduction to neural networks and deep learning. Discussion of other modern analysis methods.
• ENG EC 415: Software Radios
  Signal analysis and transmission: amplitude modulation, angle modulation, pulse- amplitude and pulse-code modulation; amplitude shift-keying, frequency shift- keying, phase-shift keying. Case studies of practical communication systems. Includes lab.
• ENG EC 416: Introduction to Digital Signal Processing
  Introduces techniques of digital signal processing and application to deterministic as well as random signals. Topics include representation of discrete-time random signals, A/D conversion, D/A conversion, frequency domain and z-domain analysis of discrete-time signals and systems, discrete-time feedback systems, difference equation and FFT based realizations of digital filters, design of IIR Butterworth filters, window-based FIR filter design, digital filtering of random signals, FFT-based power spectrum analysis. Includes lab.
• ENG EC 417: Electric Energy Systems: Adapting to Renewable Resources
  This course will present a detailed perspective of electric power systems from generation, transmission, storage, to distribution to end users. Significant emphasis will be placed on methodologies for reliable and efficient transmission and distribution of power over the grid including challenges for adapting to renewable resources such as photovoltaics and wind. Conventional approaches will be presented with emphasis to future technology such as the "smart grid". Analysis of 3-phase power will be presented using numerous examples. Items such as power system stability, security, reliability will be covered. Optimization methods, models, simulation techniques, monitoring and control, grid storage technologies, and micro-grids will also be discussed. Power electronics will be introduced specifically in reference to high voltage circuits. Finally, planning for large numbers of electric vehicles will present new challenges to the effective distribution of power which will be discussed from both centralized and decentralized approaches.

Back to full list of College of Engineering