Department of Electrical and Systems Engineering > Undergraduate Programs > Undergraduate Courses

# Undergraduate Courses

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Dept | # | Course Name :
[Course Description On]
| Credits |

ESE | 100 | Independent Study | 0 |

ESE | 101 | Introduction to Engineering Tools: MATLAB and SIMULINK | 1 |

Matlab and Simulink are important tools in quickly analyzing different designs in many engineering disciplines and are also perhaps the most used software in many engineering schools. Gain skills in the basics of the array-based language Matlab to write programs, including scripts and functions, to calculate and display variables and images. Learn the basics of Simulink to build and simulate models from standard blocks. Discover both Matlab and Simulink in an environment with supervised practice and hands-on experience. Practice problems are chosen from different engineering fields as well as from a few socio-economic fields so that students can see the software being exploited in real life applications. This is a pass/fail course. Prerequisite: Freshman standing | |||

ESE | 103 | Introduction to Electrical Engineering | 1 |

A hands-on introduction to electrical engineering to put the FUN into the electrical engineering FUNdamentals. Experiments are designed to be easy to conduct and understand. Some of the technologies explored are used in a variety of applications including the iPod, Ultrasound Imaging, Computed Tomography, Radar, DC Motors and Credit Card Readers. Students work in groups of two in the newly renovated Bryan 316 laboratory. Each station is equipped with a Quad-Core computer and an integrated Data Acquisition system. Using this lab equipment, students design and build solutions to the exercises. The students also learn to program the computer in LabVIEW to control the Data Acquisition system. Also, throughout the semester, presentations are given by the EE faculty about their research. | |||

ESE | 141 | Introductory Robotics | 1 |

A hands-on introduction to robotics. Project-oriented course where students build and program a robot guided by upper-division students. Friendly competition at the end of semester. Students will gain electrical lab experience, programming experience, and a guided introduction into the field of robotics. Recommended to freshmen and sophomores. This is a pass/fail course. | |||

ESE | 230 | Introduction to Electrical and Electronic Circuits | 4 |

Electron and ion motion, electrical current and voltage. Electrical energy, current, voltage, and circuit elements. Resistors, Ohm's Law, power and energy, magnetic fields and dc motors. Circuit analysis and Kirchhoff's voltage and current laws. Thevenin and Norton transformations and the superposition theorem. Measuring current, voltage, and power using ammeters and voltmeters. Energy and maximum electrical power transfer. Computer simulations of circuits. Reactive circuits, inductors, capacitors, mutual inductance, electrical transformers, energy storage, and energy conservation. RL, RC and RLC circuit transient responses, biological cell action potentials due to Na and K ions. AC circuits, complex impedance, RMS current and voltage. Electrical signal amplifiers and basic operational amplifier circuits. Inverting, non-inverting, and difference amplifiers. Voltage gain, current gain, input impedance, and output impedance. Weekly laboratory exercises related to the lectures are an essential part of the course. Prerequisites: Phys 118A. Corequisite: Math 217. | |||

ESE | 231 | Electrical & Electronic Circuits Laboratory | 1 |

This course is limited to students who have taken JEE 230. Introduction to electronic meters, instruments, and power supplies to create and measure electrical current and voltage. Use of PSPICE and Multisim circuit simulation software to design and analyze electrical circuits. Construction, operation and measurement of electronic circuits comprising resistors, inductors, capacitors, transformers, audio speakers, DC motors, and basic inverting and non-inverting operational amplifier circuits. Weekly attendance at laboratory exercises is required. Prerequisite: ESE 230 and Math 217 or equivalent. | |||

ESE | 260 | Introduction to Digital Logic and Computer Design | 3 |

Introduction to design methods for digital logic and fundamentals of computer architecture. Boolean algebra and logic minimization techniques; sources of delay in combinational circuits and effect on circuit performance; survey of common combinational circuit components; sequential circuit design and analysis; timing analysis of sequential circuits; use of computer-aided design tools for digital logic design (schematic capture, hardware description languages, simulation); design of simple processors and memory subsystems; program execution in simple processors; basic techniques for enhancing processor performance; configurable logic devices. Prerequisites: CSE 131 or comparable programming experience. | |||

ESE | 297 | Introduction to ESE Undergraduate Research Projects | 2 |

This course is offered to students at all levels from all departments. The course is designed to give students some hands-on experience by implementing projects that use the lab PCs, the sbRIO robots from National Instruments, acoustic sensors, bio-medical sensors and 3D cameras. These projects are implemented in LabVIEW and Matlab and should prepare the students to work on topics that include the Robotic Sensing Undergraduate Research Projects in subsequent semesters. Note that under ESE 497 Undergraduate Research, students may select the Robotic Sensing Projects as well as other projects. Working in groups, students will implement algorithms that run on PCs and our wireless robotic platforms to track a moving audio source. Also, they will use an EEG system to implement a Brain Computer Interface (BCI) project and work with the new Kinect camera from Microsoft. Corequisite: CSE131 or equivalent | |||

ESE | 318 | Engineering Mathematics A | 3 |

Laplace transforms; matrix algebra; vector spaces; eigenvalues and eigenvectors; vector differential calculus and vector integral calculus in three dimensions. Prerequisites: Math 233 and Math 217 or their equivalents. | |||

ESE | 319 | Engineering Mathematics B | 3 |

Power series and Frobenius series solutions of differential equations; Legendre's equation; Bessel's equation; Fourier series and Fourier transforms; Sturm-Liouville theory; solutions of partial differential equations; wave and heat equations. Prerequisites: Math 233 and Math 217 or their equivalents. | |||

ESE | 326 | Probability and Statistics for Engineering | 3 |

Study of probability and statistics together with engineering applications. Probability and statistics: random variables, distribution functions, density functions, expectations, means, variances, combinatorial probability, geometric probability, normal random variables, joint distribution, independence, correlation, conditional probability, Bayes theorem, the law of large numbers, the central limit theorem. Applications: reliability, quality control, acceptance sampling, linear regression, design and analysis of experiments, estimation, hypothesis testing. Examples are taken from engineering applications. Prerequisites: Math 233 or equivalent. | |||

ESE | 330 | Engineering Electromagnetics Principles | 3 |

Electromagnetic theory as applied to electrical engineering: vector calculus; electrostatics and magnetostatics; Maxwell's equations, including Poynting's theorem and boundary conditions; uniform plane-wave propagation; transmission lines, TEM modes, including treatment of general lossless lines, and pulse propagation; introduction to guided waves; introduction to radiation and scattering concepts. Pre-requisite: (1) 317 En Math or (2) Pre-requisite: ESE 318 En Math A and Co-requisite: ESE 319 En Math B. | |||

ESE | 331 | Electronics Laboratory | 3 |

Laboratory exercises provide students with a combination of hands-on experience in working with a variety of real instruments and in working in a simulated "virtual" laboratory setting. A sequence of lab experiments provide hands-on experience with grounding and shielding techniques, signal analysis, realistic operation amplifier (op amp) characterization, op amp based active filters characterization, MOSFET chopper/amplifier behavior, measurement of pulses propagating on a transmission line with various terminations, experience with both AM and FM modulation. Students will gain experience in working with: sampling oscilloscopes, various signal generators, frequency counters, digital multimeters, spectrum analyzers, and contemporary connection boards. The course concludes with a hands-on project to design and demonstrate an electronic component. Prerequisite: ESE 230. | |||

ESE | 337 | Electronic Devices and Circuits | 3 |

Introduction to semiconductor electronic devices: transistors and diodes. Device electrical DC and high-frequency characteristics. Bipolar transistors, field-effect transistors, and MOS transistors for analog electronics applications. Transistor fabrication as discrete devices and as integrated-circuit chips. Large-signal analysis of transistor amplifiers: voltage gain, distortion, input resistance and output resistance. Analysis of multitransistor amplifiers: Darlington, Cascode, and coupled-pair configurations. Half-circuit concepts, differential-mode gain, common-mode gain, and differential-to-single-ended conversion. Transistor current sources, active loads, and power-amplifier stages. Applications to operational amplifiers and feedback circuits. Prerequisite: ESE 232. | |||

ESE | 351 | Signals and Systems | 3 |

Introduction to concepts and methodology of linear dynamic systems in relation to discrete- and continuous-time signals. Representation of systems and signals. Fourier, Laplace, and Z-transforms and convolution. Input-output description of linear systems: impulse response, transfer function. State-space description of linear systems: differential and difference equation description, transition matrix. Time-domain and frequency-domain system analysis: transient and steady-state responses, system modes, stability, frequency spectrum. System design: filter, modulation. Continuity is emphasized from analysis to synthesis and implementation. Use of Matlab. Prerequisites: The notion of Matrix algebra and Math 217 or equivalent, Physics 117A-118A. Corequisite: ESE 318 or 317. | |||

ESE | 400 | Independent Study | 3 |

Opportunities to acquire experience outside the classroom setting and to work closely with individual members of the faculty. A final report must be submitted to the department. Not open to first-year or graduate students. Consult adviser. Hours and credit to be arranged. | |||

ESE | 403 | Operations Research | 3 |

Introduction to the mathematical aspects of various areas of operations research, with additional emphasis on problem formulation. This is a course of broad scope, emphasizing both the fundamental mathematical concepts involved, and also aspects of the translation of real-world problems to an appropriate mathematical model. Subjects to be covered include linear and integer programming, network problems, and dynamic programming. Prerequisites: Math 217 and familiarity with matrix or linear algebra, or permission of instructor. | |||

ESE | 405 | Reliability and Quality Control | 3 |

An integrated analysis of reliability and quality control function in manufacturing. Statistical process control, acceptance sampling, process capability analysis, reliability prediction, design, testing, failure analysis and prevention, maintainability, availability, and safety are discussed and related. Qualitative and quantitative aspects of statistical quality control and reliability are introduced in the context of manufacturing. Prerequisite: ESE 326 or equivalent. | |||

ESE | 408 | A System Dynamics Approach to Designing Sustainable Policies and Programs | 3 |

Principles and practice of modeling dynamic systems in the sciences, engineering, social sciences, and business. Model structure and its relationships to prior knowledge and assumptions, measurable quantities, and ultimate use in solving problems in application areas. Problems considered are in the areas of intervention, policy-making, business, and engineering systems. Model verification. The basic theory and practice of system dynamics. Quantitative methods are emphasized. Senior or graduate standing. | |||

ESE | 425 | Random Processes and Kalman Filtering | 3 |

Probability and random variables; random processes; linear dynamic systems and random inputs; autocorrelation; spectral density; the discrete Kalman filter; applications; the extended Kalman filter for nonlinear dynamic systems. Kalman filter design using a computer package, mean square estimation; maximum likelihood; Wiener filtering and special factorization, LQG/LTR control. Prerequisite: ESE 326 and ESE 351 or equivalent. | |||

ESE | 427 | Financial Mathematics | 3 |

This course is a self-contained introduction to financial mathematics at the undergraduate level. Topics to be covered include pricing of the financial instruments such as options, forwards, futures and their derivatives along with basic hedging techniques and portfolio optimization strategies. The emphasis is put on using of discrete, mostly binary models. The general, continuous case including the concepts of Brownian motion, stochastic integral, and stochastic differential equations, is explained from intuitive and practical point of view. Among major results discussed are the Arbitrage Theorem and Black-Scholes differential equations and their solutions. Prerequisites: ESE 318 and 319 or ESE 317 and ESE 326 or the consent of the instructor. | |||

ESE | 428 | Probability | 3 |

ESE | 434 | Solid State Power Circuits and Applications | 3 |

Study of the strategies and applications power control using solid-state semiconductor devices. Survey of generic power electronic converters. Applications to power supplies, motor drives, and consumer electronics. Introduction to power diodes, thyristors, and MOSFETs. Prerequisites: ESE 232, 351. | |||

ESE | 438 | Applied Optics | 3 |

Topics relevant to the engineering and physics of conventional as well as experimental optical systems and applications explored. Items addressed include geometrical optics, Fourier optics such as diffraction and holography, polarization and optical birefringence such as liquid crystals, and nonlinear optical phenomena and devices. Prerequisite: ESE 330 or equivalent. | |||

ESE | 441 | Control Systems | 3 |

Introduction to theory and practice of automatic control for continuous-time systems. Representations of the system: transfer function, block diagram, signal flow graph, differential state equation and output equation. Analysis of control system components. Transient and steady-state performance. System analysis: Routh-Hurwitz, root-locus, Nyquist, Bode plots. System design: PID controller, and lead-lag compensators, pole placement via state feedback, observer, stability margins in Nyquist and Bode plots. Emphasis on design principles and their implementation. Design exercises with a MATLAB package for specific engineering problems. Prerequisites: ESE 351 or MASE 431. | |||

ESE | 444 | Sensors and Actuators | 3 |

The course provides engineering students with basic understanding of two of the main components of any modern electrical or electromechanical system; sensors as inputs and actuators as outputs. The covered topics include transfer functions, frequency responses and feedback control. Component matching and bandwidth issues. Performance specification and analysis, Sensors: analog and digital motion sensors, optical sensors, temperature sensors, magnetic and electromagnetic sensors, acoustic sensors, chemical sensors, radiation sensors, torque, force and tactile sensors. Actuators: stepper motors, DC and AC motors, hydraulic actuators, magnet and electromagnetic actuators, acoustic actuators. Introduction to interfacing methods: bridge circuits, A/D and D/A converters, microcontrollers. This course is useful for those students interested in control engineering, robotics and systems engineering. Prerequisites: one of the following 5 conditions:(1) prerequisite of ESE 230 and corequisite of ESE 351;(2) prerequisites of ESE 230, ESE 317 and MEMS 255 (Mechanics II); (3) prerequisites of ESE 230, ESE 318 and MEMS 255 (Mechanics II); (4) prerequisites of ESE 105/251 and ESE 351; (5) permission of instructor. | |||

ESE | 447 | Robotics Laboratory | 3 |

Introduces the students to various concepts such as modeling, identification, model validation and control of robotic systems. The course focuses on the implementation of identification and control algorithms on a two-link robotic manipulator (the so-called pendubot) that will be used as an experimental testbed. Topics include: Introduction to the mathematical modeling of robotic systems; nonlinear model, linearized model; Identification of the linearized model: input-output and state-space techniques; Introduction to the identification of the nonlinear model: energy-based techniques; model validation and simulation; stabilization using linear control techniques; a closer look at the dynamics; stabilization using nonlinear control techniques. Prerequisite: ESE 351 or MEMS 431. | |||

ESE | 448 | Systems Engineering Laboratory | 3 |

Experimental study of real and simulated systems and their control. Identification, input-output analysis, design and implementation of control systems. Noise effects. Design and implementation of control laws for specific engineering problems. Corequisite: ESE 441 and knowledge of a programming language. | |||

ESE | 455 | Quantitative Methods for Systems Biology | 3 |

Application of computational mathematical techniques to problems in contemporary biology. Systems of linear ordinary differential equations in reaction-diffusion systems, hidden Markov models applied to gene discovery in DNA sequence, ordinary differential equation and stochastic models applied to gene regulation networks, negative feedback in transcription and metabolic pathway regulation. Prerequisites: (1) Math 217, Differential Equations and (2) a programming course and familiarity with Matlab. | |||

ESE | 465 | Digital Systems Laboratory | 3 |

ESE | 482 | Digital Signal Processing | 3 |

Introduction to analysis and synthesis of discrete-time linear time-invariant (LTI) systems. Discrete-time convolution, discrete-time Fourier transform, z-transform, rational function descriptions of discrete-time LTI systems. Sampling, analog-to-digital conversion, and digital processing of analog signals. Techniques for the design of finite impulse response (FIR) and infinite impulse response (IIR) digital filters. Hardware implementation of digital filters and finite-register effects. The Discrete Fourier Transform and the Fast Fourier Transform (FFT) algorithm. Prerequisite: ESE 351. | |||

ESE | 488 | Signals and Systems Laboratory | 3 |

A laboratory course designed to complement the traditional EE course offerings in signal processing, communication theory, and automatic control. Signals and systems fundamentals: continuous-time and discrete-time linear time-invariant systems, impulse and step response, frequency response, A/D and D/A conversion. Digital signal processing: FIR and IIR digital filter design, implementation and application of the Fast Fourier Transform. Communication theory: baseband, digital communication, amplitude modulation, frequency modulation,bandpass digital communication. Automatic control: system modeling, feedback control systems, closed-loop transient and frequency response. Laboratory experiments involve analog and digital electronics, and mechanical systems. Computer workstations and modern computational software used extensively for system simulation, real-time signal processing, and discrete-time automatic control. Prerequisite: ESE 351. | |||

ESE | 497 | Undergraduate Research | 3 |

Undergraduate research under the supervision of a faculty member. The scope and depth of the research must be approved by the faculty member prior to enrollment. A written final report and a Web page describing the research are required. | |||

ESE | 497B | Undergraduate Research | 3 |

Undergraduate research in the summer under the supervision of Dr. Arye Nehorai. Prerequisite: Undergraduate standing | |||

ESE | 499 | Capstone Design Project | 3 |

Term design project supervised by a faculty course adviser. The project must require use of the theory, techniques, engineering, and concepts of the student's major: electrical engineering or systems science & engineering. The project must have a client, typically either an engineer or supervisor from local industry or a professor or researcher in university laboratories. Namely a self-directed project is not allowed. The solution of a real technological or societal problem is carried through completely, starting from the stage of initial specification, proceeding with the application of engineering methods, and terminating with an actual solution. Required documents are a written proposal, a final report, and a Web page on the project. An oral presentation of the project also is required. Prerequisite: ESE senior standing and instructor's consent. |