67 courses with the subject ECE, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
ECE 101Electrical and Computer Engineering in the Real World1.0
This seminar introduces students to highly visible and compelling applications of ECE through the use of familiar real-world applications. The course will highlight some of the high-impact advances of ECE and the importance of ECE in our daily lives. Fundamental concepts, such as electricity, light, computing, networking, and signal processing will be introduced in this context and explained at an introductory level. This course is intended to inspire students to pursue ECE and will lead them directly into ECE 102.
ECE 102Applications of Electrical and Computer Engineering2.0
Introduces the basic fundamentals of ECE through the use of real-world applications. The course will introduce Signals and Systems, Analog electronic basics, as well as Digital numbers and systems. The course will introduce students to basic ECE material, preparing the students for ECE 200 and ECE 201.
This course will cover advanced usage and understanding of programming concepts using Python. By the end of the course, students will not only possess strong programming capabilities but will also have a firm grasp on scientific computing fundamentals.
Number systems and representation, two's complement arithmetic, digital logic devices, switching algebra, truth tables, minimization of Boolean functions, combinational logic design and analysis, sequential circuit analysis and design.
Covers basic electric circuit concepts and laws; circuit theorems; mesh and node methods; analysis of first-order electric circuits; forced and natural response; sinusoidal steady state analysis; complex frequency.
Fundamentals of computer organization; rudiments of programming including data types, arithmetic and logical expressions, conditional statements, control structures; problem solving techniques for engineers using programming; object-oriented programming; arrays; simulation of engineering systems; principles of good programming practice.
Provides an overview of systems and modeling; specifically using linear algebra for model definition. Specific emphasis will be placed on developing models of engineering systems and the use of computational tools and techniques. Computing component focuses on the use of Python for solving contemporary engineering problems.
Provides an overview of dynamic systems and modeling; specifically using differential equations to model physical or first principle systems. Specific emphasis is placed on developing models of engineering systems and using computational tools for finding solutions to these problems. Conventional solutions of linear and non-linear differential equations/systems will be covered as well as state space formulation and solutions using eigenvalues and vectors. The Laplace transforms is introduced to solve Linear Time Invariant systems. Basic feedback control laws are introduced in the context of state variable formulation.
A project-based course on design and implementation of mixed signal systems with embedded processors (digital, analog and software) with applications in signal processing, control, wireless and Internet of Things.
This course has an emphasis on measurement systems, and develops both theory and application. The software and digital and analog hardware used are relevant to both electrical and computer engineers. Multi-week design projects and design teams are used to prepare students for Senior Design work.
This course will teach students the various steps involved in the construct a fundamental remote monitoring and control system over a local area network and Bluetooth/Bluetooth Low Energy, from the ground up. The course will use hardware and software to accomplish this goal to enhance the student learning experience.
This course emphasizes how to gather data then train, test, and deploy practical machine learning systems using modern software libraries, with an emphasis on scikit-learn, Keras on TensorFlow, and TensorFlow Agents. After garnering working familiarity with learning architectures including linear regression, support vector machines, decision trees, and deep neural networks, students will shift to practicing techniques that leverage state of the art published models via transfer learning. This is a hands-on project-focused course integrating coding activities into lectures. To provide the broadest applicability, datasets will range from rich text, to financial time series, to sound, images, and video, as well as data garnered through game play.
This course will teach students the various steps involved in the construction of a full-fledged computer system, both hardware and software aspects, from the ground up. The course will use hardware and software projects to accomplish this goal. Students will design and simulate a hardware processing pipeline. A virtual machine, compiler, and assembler, for a simple object-based language will also be developed.
ECE 361Probability and Data Analytics for Engineers4.0
This course will cover topics related to probability and statistics. Probability topics include sample space and probability, discrete and continuous random variables (single and multiple), and their properties and applications to modeling, and the central limit theorem. Topics in statistics will include parametric and non-parametric hypothesis testing, data analytics and related topics, computational approaches and bootstrapping.
Covers mobility; electrons/holes; conductivity; drift; diffusion; recombination and generation; continuity equation; basic theory of PN junctions; forward and reverse biases; I-V relation; switching behavior; ac operation; capacitance of a PN junction; applications of PN junctions to solar cells, rectifiers, and photodetectors; basic operation of a BJT; regions of operation, calculation of I-V relations; switching behavior, small signal models; basic operation of metal oxide semiconductor (MOS); operation of MOSFETs and JFETS.
ECE 371Foundations of Electromagnetics for Computing & Wireless Systems3.0
This course focuses on physical laws that govern electromagnetic field distributions and related RF (radio frequency) and transmission line circuits. It covers electrostatic and magnetostatic fields, circuit modeling concepts including inductance and capacitance, and distributed electrical circuits and transmission lines.
Covers single-phase, steady-state, lossless circuit models of generation, transformer, lines, loads, electric power systems, integration of renewable energy and interfaces between AC and DC systems.
ECE 391Introduction to Engineering Design Methods1.0
Introduces the design process, including information retrieval, problem definition, proposal writing, patents, and design notebooks. Includes presentations on problem areas by experts from industry, government, and education.
The computer in the loop is examined for binary inputs and outputs taking into account processing and actuator delays. The concept of stability is introduced and the inherent delay introduced by computer systems and software on stability is explored. The use of interrupts to implement fixed-rate sampling is introduced along with practical implementation of PID controllers. The Kalman filter is introduced as a stochastic state observer under measurement uncertainty as well as the extended Kalman filter to address non-linear systems. Students will perform laboratory projects and present a final group project.
This course will cover the principles of neuromorphic computing. Topics will cover 1) fundamentals of spiking neural network (SNN), which mimics the computation in mammalian brain; 2) supervised and unsupervised learning algorithms for SNN; 3) novel applications of SNN, including in vision and time series processing; 4) architectures for implementing SNN in hardware, aka neuromorphic hardware; 5) introduction to non-volatile memory technologies to implement synaptic processing in neuromorphic hardware; 6) software stacks for neuromorphic computing; and 7) design challenges in dependable neuromorphic computing.
Reinforcement Learning (RL) has emerged as a powerful paradigm for creating intelligent, autonomous agents capable of learning from their interactions with the environment. This course provides a comprehensive understanding of key theoretical concepts, and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of theoretical lectures and hands-on coding projects, students will learn key concepts in RL, including MDPs, dynamic programming, deep RL, and the latest advances in model-based and model-free RL algorithms.
This laboratory course takes a Software-Defined Radio (SDR) implementation approach to learn about modern analog and digital communication systems. Software defined radio uses general purpose radio hardware that can be programmed in software to implement different communication standards. The course covers basic principles of wireless radio frequency transmissions and leverage this knowledge to build analog and digital communication systems. Knowledge of these techniques and systems will provide a platform that can be used in the class project for further exploration of wireless networking topics such as cybersecurity, cognitive radio, smart cities, and the Internet of Things.
This course discusses the physics of the operation of modern transistors. It covers the operational principles of Bipolar Junction Transistors (BJTs), Heterojunction Bipolar Transistors (HBTs), Field Effect Transistors, (FETs), starting with MOSFETs. High Electron Mobility Transistors (HEMT) will also be discussed. Students will perform independent individual research on an (opto)electronic device of their choice, which they present to class through written and oral reports.
This course will teach students the principles that underline the interaction of light and matter, leading to the understanding of the basis of operation of photonic devices such as lasers, LEDs, solar cells, and photodetectors. The course starts with how understanding of light spectrum that is generated due to heat started the development of the field of quantum mechanics by Max Planck. This is then to include a quantum theory of light, on which basis absorption, stimulated and spontaneous emission are explained. Interaction of light with semiconductors is analyzed and shows how lasers, LEDs and photodetectors work, and how modern photonics is able to solve great challenges of humanity, such as lighting or optical data communication.
ECE 434Introduction to Multimedia Forensics and Security3.0
This course introduces students to fundamental concepts in multimedia forensics and security. Topics covered include an introduction to digital image processing and compression, information hiding, watermarking, image and video forgery detection, and source identification.
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.
This is the first of two courses offered on Custom Very Large Scale Integration (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.
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.
This course will teach students the concepts of reflection and transmission on distributed transmission lines of TEM, quasi-TEM, and TE/TM from electromagnetic fields and related to phenomenological scalar V/I in frequency and time domains, while the graphical technique using Smith Chart is employed for design of narrowband and broadband distributed/lumped impedance matching techniques. Multi-port network concepts of S/Z/Y matrices and utility of ABCD and T matrices are introduced from network perspective. Design, analysis, and synthesis of power dividers, dual directional couplers, and variety of filter design using insertion loss technique and their distributed realization at RF frequencies.
This course will teach students the concepts of RF transceivers using active microwave circuits and discuss their interactions with radiating systems. The course covers linear RF electronic circuits of switches, phase shifters (dispersive and non-dispersive), limiters, amplifiers, oscillators (fixed and variable frequency), Mixer (single and balanced), and multipliers (resistive and reactive) realized using semiconductor devices (diodes/transistors). CAD simulations are a practical learning component and is designed to prepare students for various industrial design and telecommunication applications.
This course will teach students the concepts of antennas and photonics using electromagnetics and discuss their utility in design of telecommunications and remote sensing. Antennas are introduced in terms of E&M fields (radiation pattern, polarization), circuits (radiation impedance/admittance, efficiency, bandwidth), and system concepts (directive gain). Examples of line antenna (short electric/magnetic dipoles, arbitrary length dipoles, small loops, travelling and standing wave), array antennas (linear and planar), and aperture radiators (slot, patch, and reflectors). Lightwave propagation in optical fibers (step index, graded index, polarization maintaining) and dielectric structures (slabs, ridge waveguide, buried waveguides) are discussed.
Introduces the design process, including information retrieval, problem definition, proposal writing, patents, and design notebooks. Includes presentations on problem areas by experts from industry, government, and education.
Continues ECE 492. Requires written and oral final reports, including oral presentations by each design team at a formal Design Conference open to the public and conducted in the style of a professional conference.
This power electronics course is for graduate students in the area of power engineering, focusing on advanced knowledge and technology in the research and applications of power electronics. It will introduce the latest achievements in power electronics and provides future trend of technology developments.
Circuit laws, transfer functions, convolution, transform techniques, systems engineering. This series of courses may be used to meet the admission prerequisites to ECE graduate program. One credit per term is creditable to the M.S.E.E. degree.
ECE 502Topics In Communications, Controls and Computers3.0
Modulation theory, noise, feedback theory, stability, computer engineering fundamentals, computers in communication and controls. This series of courses may be used to meet the admission prerequisites to the ECE graduate program. One credit per term is creditable to the M.S.E.E. degree.
ECE 503Topics in Mathematical Techniques In Electrical and Computer Engineering3.0
Complex variables in communication and control, matrix methods in circuits and systems, vector calculus in fields, two-dimensional image processing. This series of courses may be used to meet the admission prerequisites to the ECE graduate program. One credit per term is creditable to the M.S.E.E. degree.
This course makes use of a remote platform, an open infrastructure for networking research and education that spans multiple testbeds of real networking equipment around the United States and abroad. Students will gain hands-on experience designing, configuring and analyzing real networks. We will work in small groups on lab exercises by creating a shared ``networking slice” for each group. We will explore several networking protocols, from basic home gateway services (DHCP, DNS, NAT), to TCP/IP Protocol layers (Link State Routing, TCP congestion control), network security basics (network reconnaissance, DNS spoofing scenarios) to more advanced topics, such as programmable switches and software defined networking.
Fundamentals of modern wireless systems. Fundamentals of radio propagation and link performance. Cellular concept: interference, base stations and cell sites, handoffs, system capacity. Fading environments: multipath propagation, delay spread, Doppler Spread, statistically fading channel models. Multiple-access schemes: FDMA, TDMA, CDMA, SDMA. Emerging methodologies: phases/adaptive antenna array, multi-array (MIMO) communication systems.
This course will provide an introduction to radar systems, range equation and radar signal processing techniques as well as the nature of physical observables and propagators, the effects of the propagation medium on sensor performance, the relationship between signals and noise, and the characteristics of critical sensor functions (including detection and tracking). Radar subsystems will be studied, including antennas, transmitters, receivers, and signal processors. This class will also feature a project component for students to identify, research and present open problems that are relevant to radar systems.
This course will provide an introduction to electronic warfare (EW), including electronic support, electronic attack, and electronic protection. Topics include jamming/anti-jamming techniques, low probability of intercept waveforms, EW detection, EW identification, and EW countermeasures. This class will also feature a project component for students to identify, research and present open problems that are relevant to EW systems.
This course teaches the underlying physics of the operation of modern bipolar and unipolar transistors which are used in modern electronics. This background is helpful for a) courses related to digital microelectronics, logical gates, memories, and sub circuits, and VLSI circuits; b) courses in analog electronics; and c) courses in microwave electronic systems.
This course will teach students the principles that underline the interaction of light and matter, leading to the understanding of the basis of operation of photonic devices such as lasers, LEDs, solar cells, and photodetectors. The course starts with how understanding of light spectrum that is generated due to heat started the development of the field of quantum mechanics by Max Planck. This is then expanded by Einstein to include a quantum theory of light, on which basis absorption, stimulated and spontaneous emission are explained. Building on that work, we analyze light interaction with semiconductors and show how lasers, LEDs and photodetectors work, and how modern photonics is able to solve great challenges of humanity.
A project-based course on design concepts, tools and implementation of systems with embedded processors, library IP (Intellectual Property) cores and custom IP cores, synthesis and Field Programmable Gate Array (FPGA) implementation.
ECE 572Techniques of Electrical and Computer Engineering Research0.0
Techniques for making technical presentations: oral and written modes. Meeting organization, audience awareness. Writer-to-reader communication. Required of all full-time graduate students.
ECE 573Presentation of Electrical and Computer Engineering Research0.0
Conference attendance and critique. Student presentation and critique. Topics of concern: professional ethics, liability, etc. Required of all full-time graduate students.
Students will learn the underlying background/theory to simulate and physically implement a two axis scara robot that can draw a straight line or follow curves. Key topics include: creation of motion profiles, forward and inverse kinematics, Devevit-Haretnburg matrices, Lagrangian dynamisc, position servo systems and determining required bandwith of servo system to ensure tracking of desired waveforms as well as fundamentals of robot motion programming. In addition students will also explore commercial codes used for robot design and analysis.
This course focuses on the practical aspects of implementing Computer Control using microcontrollers in such applications as: Automated Equipment, Robotics, Motor Control, Process Control and Aerospace. The course is essentially divided into two parts: (1) the computer in the loop and (2) addressing noisy measurements.
Implementation of computing and information processing depends on physical laws. Laws of quantum theory could make it possible to circumvent some current limitations of classical computers and information processing methods. This course introduces the concepts of quantum computing and informatics with a particular emphasis on comparison with its classical counterparts. The course does not discuss technology or devices that can be used to implement quantum computers. Instead, it is meant to provide logical foundation for the quantum computation paradigm culminating in the discussion, analysis and simulation of simple quantum computing algorithms and information transfer fundamentals.
Robots today are equipped with sophisticated computing, communication, and sensing resources. It is becoming increasingly important to develop efficient decision-making algorithms that make full use of the robot's capabilities. In this course, we will discuss the state-of-the-art algorithms that aim for that. Topics range from Information-theoretic Planning, Planning under Uncertainty, Adversarial Planning, to Learning and Perception.
This course will focus on sensing, control, and motion planning for mobile (ground and aerial) robots. It will be a mix of theoretical and applied subject matter where the students will learn about the fundamental mobile sensing and planning concepts and apply them through programming assignments and class projects. We will cover basic techniques such as state estimation, recursive filtering, localization, control, and planning algorithms that robots use to perceive, understand, and act in the environment.
This course introduces students to topics in modern machine learning, along with applications of machine learning to problems in engineering. Introductory topics will include an overview of classification, overfitting, cross-validation, and dimensionality reduction. Supervised classification approaches will be covered including linear classifiers, generative and discriminative models, non-probabilistic classification approaches, kernel methods, and neural networks. Topics in unsupervised learning will also be covered if time permits.
This course emphasizes how to gather data then train, test, and deploy practical machine learning systems using modern software libraries, with an emphasis on Keras on TensorFlow. Complementing the other department courses emphasizing the mathematics behind machine learning algorithms and the ways these can be tailored to specific computing architectures, this project-focused course emphasizes the practice of rapidly prototyping and testing multiple learning structures. To provide the broadest applicability, datasets will range from rich text, to financial time series, to sound, images, and video.
This course will cover the principles of neuromorphic computing. Topics will cover 1) fundamentals of spiking neural network (SNN); 2) supervised and unsupervised learning algorithms for SNN; 3) novel applications of SNN, including in vision and time series processing; 4) architectures for implementing SNN in hardware; 5) introduction to non-volatile memory technologies to implement synaptic processing in neuromorphic hardware; 6) software stacks for neuromorphic computing; and 7) design challenges in dependable neuromorphic computing. Software projects in this course will involve developing SNN models and training algorithms in PyTorch, a Python based machine learning framework. Hardware projects will be based on Verilog HDL and prototyped on FPGA. Familiarity with PyTorch and/or Verilog is required.
Reinforcement Learning (RL) has emerged as a powerful paradigm for creating intelligent, autonomous agents capable of learning from their interactions with the environment. This course provides a comprehensive understanding of key theoretical concepts, and students will learn about the core challenges and approaches, including generalization and exploration. Through a combination of theoretical lectures and hands-on coding projects, students will learn key concepts in RL, including MDPs, dynamic programming, deep RL, and the latest advances in model-based and model-free RL algorithms.
This laboratory course takes a Software-Defined Radio (SDR) implementation approach to learn about modern analog and digital communication systems. Software defined radio uses general purpose radio hardware that can be programmed in software to implement different communication standards. Discussion of the basic principles of wireless radio frequency transmissions and leverage this knowledge to build analog and digital communication systems. Knowledge of these techniques and systems will provide a platform that can be used in the class project for further exploration of wireless networking topics such as cybersecurity, cognitive radio, smart cities, and the Internet of Things.
This course introduces a systematic design of fault-tolerant hardware and software systems. Key topics to be covered includes 1) fault classification, 2) use of information redundancy, 3) basic measures of fault tolerance, 4) fault tolerance of networked and cryptographic systems, 4) fault tolerance for machine learning and deep learning systems, and 4) fault-based side-channel attacks.
ECE 695Research Rotations in Cybersecurity1.0-12.0
The research rotation course allows students to gain exposure to cybersecurity-related research that cuts across conventional departmental barriers and traditional research groups, prior to identifying and focusing on a specific interdisciplinary project or thesis topic. Students selecting to participate in research rotations would participate in the research activities of two labs for each three credits of research rotation they undertake.
