Electrical and Computer Engineering (2023)

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[graduation course|graduate program|Faculty]

All courses, faculty listings, and curriculum and graduation requirements described in this document are subject to change or withdrawal without notice.


For course descriptions not found inUC San Diego General Catalog 2022–23, contact the department for more information.

The department will endeavor to offer the courses described below; however, unforeseen circumstances sometimes require a change in scheduled offers. Students are strongly encouraged to consult the class or department schedule before relying on the schedule below. For the names of the instructors who will teach the course, see the Quarterly Class Schedule. the department's websitehttp://ece.ucsd.eduincludes the current best estimate of school hours for the entire school year.

lower division

ECE 5. Introduction to Electrical and Computer Engineering (4)

Introduction to Electrical and Computer Engineering. Topics include circuit theory, assembly and testing, embedded systems programming and debugging, transducer mechanisms and interfacing transducers, systems and signal theory, digital signal processing, and modular design techniques.

ECE 15. Computer Engineering (4)

Students learn the C programming language with an emphasis on high performance numerical computing. The similarity between control structures, data structures, and I/O programming languages ​​is also discussed. Techniques are developed for using MATLAB to graph the results of C computations.previous requirements:Familiarity with basic mathematics, such as trigonometric functions and graphs, is expected, but this course does not assume any prior knowledge of programming.

ECE 16. Rapid design of hardware and software to interact with the world (4)

Students are introduced to integrated systems concepts with the structured development of a computer controller based on electromyogram (EMG) signals through four lab assignments during the quarter. Key concepts include sampling, signal processing, communication, and real-time control. Students will apply their prior knowledge of C (since ECE15) to program microcontrollers and engage in data analysis using the Python programming language.previous requirements:MAE 8 or CSE 8B or CSE 11 or ECE 15.

ECE 17. Object Oriented Programming: Design and Development with C++ (4)

This course combines the fundamentals of object-oriented design in C++ with the programming, debugging, and testing practices used by modern software developers. Emphasizes the use of object-oriented techniques to model and reason about system design and the use of modern C++ idioms, design patterns, and the Standard Template Library (STL) to develop solutions to system engineering challenges that are more reliable, robust, scalable and secure.previous requirements:CSE 8B o CSE 11 o ECE 15.

ECE 25. Introduction to Digital Design (4)

This course emphasizes digital electronics. The principles introduced in the lectures are used in laboratory work, which also serves to introduce experimental and design methods. Topics include Boolean algebra, combination and sequential logic, gates, and their implementation in digital circuits. (Course materials and/or program fees may apply.)previous requirements:none.

ECE 30. Introduction to Computer Engineering (4)

The fundamentals of hardware and software in a computer system. Topics include information representation, computer organization and design, assembly and microprogramming, current technology in logic design.previous requirements:ECE 15 and 25 with grades of C– or better.

ECE 35. Introduction to analog design (4)

Fundamentals of circuit theory, Kirchhoff's current and voltage laws, Thevenin's and Norton's theorems, loop and node analysis, time-varying signals, first-order transient circuits, steady-state sinusoidal response. It is highly recommended to take MATH 20C and PHYS 2B before or during the same trimester. Program or material fees may apply.previous requirements: MATH 18, 20A–B and PHYSICS 2A.

ECE 45. Circuits and Systems (4)

Steady state circuit analysis, first and second order systems, Fourier Series and Transforms, time domain analysis, convolution, transient response, Laplace Transform and filter design.previous requirements:CEPE 35.

ECE 65. Components and Circuits Laboratory (4)

Introduction to linear and nonlinear components and circuits. Topics will include two-terminal devices, bipolar and field-effect transistors, and small- and large-signal analysis of diode and transistor circuits. (Program or materials fees may apply.)previous requirements:CEPE 35.

ECE 85. iTunes 101: An Information Technology Survey (4)

Topics include how devices such as iPods and iPhones generate, transmit, receive, and process information (music, images, video, etc.), the relationship between technology and issues such as privacy and "net neutrality," and topics related to information technology. .previous requirements: none.

ECE 87. Freshman Seminar (1)

The Freshman Seminar Program is designed to give new students the opportunity to explore an intellectual topic with a faculty member in a small seminar. Freshman seminars are offered in all campus departments and undergraduate colleges, and topics vary from quarter to quarter. Enrollment is limited to fifteen or twenty students, with preference given to first-year students.previous requirements:none.

ECE 90. Graduation Seminar (1)

This seminar class will provide a broad review of current research topics in electrical engineering and computer engineering. Typical subject areas are signal processing, VLSI design, electronic materials and devices, radio astronomy, communications, and optical computing.previous requirements:none.

upper division

ECE 100. Linear Electronic Systems (4)

Design of linear active systems and circuits. Topics include frequency response; use of Laplace transforms; design and stability of filters using operational amplifiers. The integrated lab and lecture involve analysis, design, simulation, and testing of circuits and systems. Program or material fees may apply. previous requirements: ECE 45 and ECE 65. ECE 65 can be taken simultaneously.

ECE 101. Fundamentals of Linear Systems (4)

Complex variables. Singularities and residuals. Analysis of signals and systems in continuous and discrete time. Fourier series and transforms. Laplace and z transforms. Linear systems invariant in time. Impulse response, frequency response and transfer functions. poles and zeros. Stability. Convolution. Sampling. Alias.previous requirements:CEPE 45.

ECE 102. Introduction to the design of active circuits (4)

Design of nonlinear active circuits. Nonlinear device models for diodes, bipolar and field effect transistors. Linearization of device models and small signal equivalent circuits. Circuit designs will be computer simulated and lab tested.previous requirements:ECE 65 and ECE 100. ECE 100 can be taken simultaneously.

ECE 103. Fundamentals of Devices and Materials (4)

Introduction to semiconductor materials and devices. Semiconductor crystal structure, energy bands, doping, carrier statistics, drift and diffusion, p-n junctions, metal-semiconductor junctions. Bipolar Junction Transistors: Current Flow, Amplification, Switching, Non-Ideal Behavior. Metal oxide semiconductor structures, MOSFETs, device sizing.previous requirements:ECE 65 y PHYS 2D o PHYS 4D y 4E.

ECE 107. Electromagnetism (4)

electrostatic and magnetostatic; electrodynamics; Maxwell's equations; plane waves; effect on the skin. Electromagnetism of transmission lines: reflection and transmission in discontinuities, Smith chart, pulse propagation, dispersion. Rectangular waveguides. Dielectric and magnetic properties of materials. Circuit electromagnetism.previous requirements: PHYS 2A–CO ou 4A–C y ECE 45.

ECE 108. Digital circuits (4)

A transistor level view of digital integrated circuits. CMOS combinational logic, proportional logic, noise margins, rise and fall delays, power dissipation, transmission ports. Short channel MOS model, scale effects. Sequential circuits, memory and matrix logic circuits. Three hours of lecture, one hour of discussion, three hours of laboratory.previous requirements: ECE 25 o CSE 140, 45 y 65 y ECE 30 o CSE 30.

ECE 109. Engineering Statistics and Probability (4)

Axioms of probability, conditional probability, total probability theorem, random variables, densities, expected values, characteristic functions, transformation of random variables, central limit theorem. Random number generation, engineering reliability, estimation items, random sampling, sampling distributions, hypothesis testing. One unit of credit is awarded if achieved after MAE 108, MATH 180A, MATH 180B, MATH 183, MATH 186, or ECON 120A.previous requirements:MATH 20A-B, MATH 20D, MATH 20C o MATH 31BH y MATH 31AH o MATH 18.

ECE 111. Advanced Digital Design Project (4)

Advanced topics in digital circuits and systems. Use of computers and project automation tools. Hazard removal, synchronous/asynchronous FSM synthesis, synchronization and arbitration, pipeline and timing problems. Sets of problems and design exercises. A large-scale design project. Simulation and/or rapid prototyping.previous requirements:ECE 25 o CSE 140.

ECE 115. Rapid Prototyping (4)

Laboratory based course. Students will learn how to prototype a mechatronic solution. Topics include cheap/affordable parts and materials; providers; rapid prototyping techniques; useful electronic sketches and system integration shortcuts. Students will learn to materialize their electromechanical ideas and make design decisions to minimize costs, improve functionality/robustness. The labs will culminate in a fully functional robot prototype for demonstration.previous requirements:ECE 16 or instructor consent.

ECE 118. Computer interface (4)

Interface of computers and embedded controllers with the real world: buses, interrupts, DMA, memory mapping, concurrency, digital I/O, standards for serial and parallel communication, A/D, D/A, sensors, signal conditioning, video and closed circuit control. Students design and build an interface project. (Course materials and/or program fees may apply.)previous requirements:ECE 30 o CSE 30 y ECE 35, 45, 65.

ECE 120. Physics of the Solar System (4)

General introduction to planetary bodies, the general structure of the solar system, and the physics of space plasma. The emphasis of the course will be on the solar atmosphere, how the solar wind is produced, and its interaction with magnetized and non-magnetized planets (and comets).previous requirements:PHYS 2A–C o 4A–D, MATH 20A–B, 20C con notas de C– o mejor.

ECE 121A. Analysis and Fundamentals of Energy Systems (4)

This course introduces concepts to analyze large-scale power systems: generation, distribution, steady-state analysis, and economic operation of electric power. It provides the foundation for advanced courses and engineering practice in electric power systems, smart grids, and electric economics. The course requires the implementation of some of the computational techniques in simulation software.previous requirements:CEPE 35.

ECE 121B. Power conversion (4)

Principles of electromechanical energy conversion, balanced three-phase systems, fundamental concepts of magnetic circuits, single-phase transformers, and the steady-state performance of DC and induction machines. Students cannot receive credit for ECE 121B and ECE 121. previous requirements: ONION 121A.

ECE 123. Antenna Systems Engineering (4)

Electromagnetic engineering and radio antenna systems for terrestrial wireless and satellite communications. Antenna impedance, beam pattern, gain and polarization. Dipoles, monopoles, paraboloids, phased arrays. Power and noise budgets for communication links. Atmospheric and multipath propagation.previous requirements:ECE 107 grade C– or higher.

ECE 124. Motor drives (4)

Topics include steady state operation of DC motors and induction machines and speed control of DC and induction motors in an energy efficient manner through the use of power electronics. Control techniques such as vector control and direct torque control (DTC) of induction machines. Different control methods for DC motors using different types of power converters such as DC-DC and AC-DC converters. Design the torque, speed, and position controller for the DC motor.previous requirements:ECE 121B y ECE 125A.

ECE 125A. Introduction to Power Electronics I (4)

Power generation, system and electronics. Topics include Power Semiconductor Devices and Characteristics, Single and Three Phase Semiconductors and Fully Controlled AC to DC Rectifiers, Non-Isolated/Isolated DC-DC Converters, Power Loss Calculation and Thermal Considerations, Snubber Circuits.previous requirements: ONION 121A.

ECE 125B. Introduction to Power Electronics II (4)

Design and control of DC-DC converters, PWM rectifiers, single-phase and three-phase inverters, energy management and power electronics applications in renewable energy systems, motion control and lighting.previous requirements:CEPE 125A.

ECE 128A. Real World Power Grid Operation (4)

It provides practical information on the operation of the electrical network. It covers the same topics as the real power system operator certification course. It systematically describes the vital functions of network operators and the processes required to operate the system. It uses real case histories and real examples of best-in-class approaches from around the country and the world. It presents the problems encountered by the operators and the workable solutions to remedy them.previous requirements:first division position.

ECE 128B. Modernization of the electrical network (4)

Detailed coverage of future energy networks. It covers the practical aspects of the technologies, their design and system implementation. Topics include the changing nature of the grid with renewable resources, smart meters, synchrophasors (PMUs), microgrids, distributed energy resources, and associated information and communication infrastructure. Presents real examples and best practices. Students will have various tools.

ECE 128C. Grid resilience to adverse effects (4)

This course offers unique insight and practical answers through examples of how power systems can be affected by weather and what/how countermeasures can be applied to mitigate them and make the system more resilient. Detailed explanations of the impacts of extreme weather and applicable industry standards and initiatives. Proven practices for successful power grid restoration, increased system resiliency, and resilience after extreme weather conditions, providing real-world examples from around the world.

ECE 129. Renewable resources and energy storage (4)

It provides a solid foundation for renewable energy resources such as hydroelectric, solar, wind, geothermal, wave and tidal power. Energy grid storage systems. Presents lessons learned from current systems and the study of renewable energy resources and energy storage systems. It presents the lessons learned from current systems and the results of detailed studies on the applications of renewable energy resources and energy storage systems. Industry tools will be provided for hands-on experience with such features and systems.previous requirements:first division position.

ECE 134. Electronic Materials Science of Integrated Circuits (4)

Electronic materials science with an emphasis on topics related to microelectronics and VLSI technology. The concept of the course is to use components in integrated circuits to discuss the structure, thermodynamics, reaction kinetics, and electrical properties of materials.previous requirements:PHYS 2C–D with grades of C– or better.

ECE 135A. Semiconductor Physics (4)

Crystal structure and quantum theory of solids; electronic band structure; carrier statistics review, drift and diffusion, p-n junctions; non-equilibrium carriers, imrefs, traps, recombination, etc.; Metal-semiconductor junctions and heterojunctions.previous requirements:ECE 103 grade C– or higher.

ECE 135B. Electronic devices (4)

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Structure and function of bipolar junction transistors, junction field effect transistors, metal oxide semiconductor diodes, and transistors. Analysis of DC and AC characteristics. Dynamic behavior load control model.previous requirements:ECE 135A grade C– or higher.

ECE 136L. Microelectronics Laboratory (4)

Laboratory fabrication of field effect diodes and transistors covering photolithography, oxidation, diffusion, thin film deposition, corrosion, and device evaluation. (Course materials and/or program fees may apply.)previous requirements:CEPE 135B.

UNECE 138L. Microstructuring Processing Technology Laboratory (4)

A laboratory course that covers the concept and practice of microstructuring science and technology in the fabrication of devices relevant to sensors, laboratory chips, and related devices. (Course materials and/or program fees may apply.)previous requirements:first division ranking for science and engineering students.

ECE 139. Design and modeling of semiconductor devices (4)

Device physics of modern field-effect transistors and bipolar transistors, including the behavior of submicron structures. Relationship between structure and models of transistor circuits. CMOS and BiCMOS technologies. Emphasis on computational simulation of transistor operation and application in integrated circuits.previous requirements: ECE 135A-B with grades of C– or better.

UNECE 140A. The art of product engineering I (4)

Building on a solid foundation of electrical and computer engineering skills, this course strives to broaden the student's skills in software, comprehensive engineering, and a concrete understanding of the methods involved in developing realistic commercial products. Students will research, design, and develop an IOT device to serve an emerging market.previous requirements: CSE 8B o CSE 11 o ECE 15.

ECE 140B. The art of product engineering II (4)

Building on a solid foundation of electrical and computer engineering skills, this course strives to broaden the student's skills in software, comprehensive engineering, and a concrete understanding of the methods involved in developing realistic commercial products. Students will research, design, and develop an IOT device to serve an emerging market. previous requirements:ONION 140A.

CEPE 141A. Software Fundamentals I (4)

Software analysis, design and development. Data structures, algorithms and design and development idioms in C++. Students will gain extensive experience using object-oriented methods and design patterns. Through increasingly difficult challenges, students will gain valuable real-world experience in creating, testing, and debugging software and will develop a strong mental model of modern software architecture and design.previous requirements:ECE 17 y CSE 30 o ECE 30.

CEPE 141B. Software Fundamentals II (4)

ECE 141B builds on the strong C++ foundation of ECE 141A. Students will model and build a functional database administration solution in C++. Topics include STL, design patterns, analysis, search and classification, algorithmic thinking, and design partitioning. The course will continue to explore best practices in software development, debugging, and testing.previous requirements: CEPE 141A.

ECE 143. Programming for data analysis (4)

This course covers the fundamentals of effectively using the Python language for data analysis. Students learn the underlying mechanics and implementation details of Python and how to effectively use the many built-in data structures and algorithms. The course introduces the main modules for data analysis, such as Numpy, Pandas, and Matplotlib. Participants learn to tap into and navigate the vast Python ecosystem to find code and communities of individual interest.previous requirements:CEPE 16.

ECE 144. Programming in LabVIEW: Design and Applications (4)

Develop, debug, and test LabVIEW VIs, solve problems using LabVIEW, use data acquisition, and perform signal processing and instrument control in LabVIEW applications. Student groups will build an elevator system from 3D printed and laser cut parts; integrate sensors, motors and servos; and program using state machine architecture in LabVIEW. Students will have the opportunity to take the National Instruments Certified LabVIEW Associate Developer (CLAD) exam at the end of the quarter. Program or material fees may apply.previous requirements:CSE 11 o CSE 8B o ECE 15.

ECE 145AL-BL-CL. Acoustics Laboratory (4-4-4)

Automated laboratory based on instruments controlled by H-P GPIB. Software controlled data collection and analysis. Vibrations and waves in strings and bars of electromechanical systems and transducers. Transmissions, reflection and dispersion of sound waves in air and water. Auditory and visual detection.previous requirements:ECE 107 grade C– or better or instructor consent.

ECE 148. Introduction to Autonomous Vehicles (4)

This course introduces students to the fundamentals of autonomous vehicles using a fast-paced and engaging engineering curriculum, harnessing the educational benefits of robotic “cooperation” (cooperative competition). Students work in small teams building scale cars using best engineering practices. Skills to be learned include rapid prototyping, project management, traditional programming and computer vision using the Robot Operating System (ROS) and artificial intelligence deep learning. Cross-list with MAE 148. Students may not receive credit for ECE 148 and MAE 148. Program or materials fees may apply.previous requirements:ECE 15 or ECE 35 or MAE 2 or MAE 3, and consent of the instructor.

ECE 150. Entrepreneurship for Engineers (4)

A basic course that teaches you the basics of starting and running a successful new business. Students learn to think like entrepreneurs, direct their ideas to meet customer needs, and assess financial, market, and timeline feasibility. The ultimate goal is an investor pitch and a business plan. It offers experiential education, encouragement, and training ("E3CE") that prepares students for successful careers in startups and large companies. Students cannot receive credit for ECE 150 and CSE 175. Counts only toward a career elective.previous requirements:students must apply to assess their prior experience and interest in entrepreneurship. Instructor consent is required.

ECE 153. Probability and random processes for engineers (4)

Random processes. Stationary processes: correlation, power spectral density. Gaussian processes and linear transformation of Gaussian processes. occasional processes. Random noise in linear systems.previous requirements:ECE 109 grade C– or higher.

ECE 155. Theory of Digital Communications (4)

Design and performance analysis of digital modulation techniques, including probability of error results for PSK, DPSK, and FSK. Introduction to intersymbol interference and fading effects. Detection theory and estimation, including optimal receiver design and maximum likelihood parameter estimation. Renumbered from ECE 154B. Students cannot receive credit for ECE 155 and ECE 154B.previous requirements:ECE 101 o BENG 122A, ECE 109 o ECON 120A o MAE 108 o MATH 180A o MATH 180B o MATH 183 o MATH 186 y ECE 153.

ECE 156. Sensor networks (4)

Characteristics of chemical, biological, seismic and other physical sensors; signal processing techniques that support distributed detection of highlight events; wireless communication and network protocols that support the formation of robust sensor fabrics; Current experience with low-cost, low-power sensor implementations. Undergraduate students must take a final exam; Graduate students are required to write a capstone paper or complete a capstone project. Cross list with MAE 149 and SIO 238.previous requirements:position in the upper division and consent of the instructor or graduate student in science and engineering.

ECE 157A. Communications Systems Laboratory I (4)

Experiments on the modulation and demodulation of baseband and passband signals. Statistical characterization of signs and deficiencies. (Course materials and/or program fees may apply.)previous requirements:ECE 109 o ECON 120A o MAE 108 o MATH 180A o MATH 180B o MATH 183 o MATH 186 y ECE 161A.

CEPE 157B. Communication Systems Laboratory II (4)

Advanced projects in communication systems. Students will plan and implement design projects in the lab, updating progress weekly and making adjustments to the plan/design based on feedback.previous requirements:ECE 157A o ECE 161A.

CEPE 158A. Data Networks I (4)

Layered network architectures, data link control protocols and multiple access systems, performance analysis. flow control; prevention of deadlocks and performance degradation. Routing, centralized and decentralized schemes, static dynamic algorithms. Shortest path and minimum average delay algorithms. comparisonsprevious requirements:ECE 109 grade C– or higher.

CEPE 158B. Data Networks II (4)

Layered network architectures, data link control protocols and multiple access systems, performance analysis. flow control; prevention of deadlocks and performance degradation. Routing, centralized and decentralized schemes, static dynamic algorithms. Shortest path and minimum average delay algorithms. comparisonsprevious requirements:ECE 158A grade C– or higher.

ECE 159. Introduction to Data Processing and Information Theory (4)

Introduction to information theory and coding, including entropy, mutual information averaging, channel capacity, block codes, and convolutional codes. Renumbered from ECE 154C. Students cannot receive credit for ECE 159 and ECE 154C.previous requirements:CEPE 153.

ECE 161A. Introduction to digital signal processing (4)

Review of discrete-time systems and signals, Discrete-Time Fourier Transform and its properties, Fast Fourier Transform, Design of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, implementation of digital filters.previous requirements:CEPE 101.

ECE 161B. Digital signal processing I (4)

Sampling and quantization of baseband signals; A/D and D/A conversion, quantization noise, oversampling and noise shaping. Bandpass signal sampling, downconversion subsampling, and Hilbert transforms. Quantization of coefficients, rounding noise, limit cycles and overflow oscillations. Structures of insensitive filters, digital network and wave filters. The systems will be designed and tested with MATLAB, implemented with DSP processors and tested in the laboratory.previous requirements:ECE 161A grade C– or better.

ECE 161C. Digital signal processing applications (4)

This course discusses various DSP applications. Topics covered will include speech analysis and coding; image and video compression and processing. Class design is required, algorithms simulated by MATLAB.previous requirements:ONION 161A.

ECE 163. Electronic Circuits and Systems (4)

Analysis and design of circuits and analog systems. Feedback systems with applications in operational amplifier circuits. Stability, sensitivity, bandwidth, compensation. Design of active filters. Switched capacitor circuits. Phase locked loops. Analog to digital and digital to analog conversion. (Course materials and/or program fees may apply.)previous requirements:ECE 101 and 102 with grades of C– or better.

ECE 164. Design of analog integrated circuits (4)

Design of linear and non-linear analog integrated circuits, including operational amplifiers, voltage regulators, drivers, power stages, oscillators and multipliers. Use of noise feedback and performance evaluation. Parasitic effects of integrated circuit technology. Laboratory simulation and circuit testing.previous requirements:ECE 102 grade C– or higher. ECE 163 is recommended.

ECE 165. Design of Digital Integrated Circuits (4)

VLSI digital systems. Circuit characterization, performance estimation and optimization. Circuits for alternative logic styles and clock schemes. Subsystems include ALU, memory, processor sets, and PLA. Techniques for door arrangements, standard cells and custom design. Design and simulation using CAD tools.previous requirements:CEPE 102.

ECE 166. Microwave systems and circuits (4)

Wave methods, distributed circuits and dispersion matrices. Passive microwave elements. Impedance. Detection and frequency conversion by microwave diodes. Design of transistor amplifiers including noise performance. Circuit designs will be computer simulated and lab tested. (Course materials and/or program fees may apply.)previous requirements:ECE 102 and 107 with grades of C– or better.

CEPE 171A. Linear Control System Theory (4)

Stability of linear time-invariant single-input/single-output and discrete-time control systems emphasized by frequency-domain methods. Transient and stationary behavior. Root locus stability analysis, Bode, Nyquist and Nichols plots. Compensator design.previous requirements:ECE 45 or MAE 140.

ECE 171B. Linear Control System Theory (4)

Time domain and state variable formulation of the control problem for linear systems in discrete and continuous time. Realizations of the state space from the description of the system of transfer functions. Internal and input-output stability, controllability/observability, minimal realizations, and full-state feedback pole placement.previous requirements:ECE 171A grade C– or better.

ECE 172A. Introduction to Intelligent Systems: Robotics and Machine Intelligence (4)

This course will introduce basic concepts in the perception of machines. Topics covered will include edge detection, segmentation, texture analysis, image registration, and compression.previous requirements:ECE 101 grade C– or higher. ECE 109 is recommended.

ECE 174. Introduction to linear and nonlinear optimization with applications (4)

The linear least squares problem, including constrained and unrestrained quadratic optimization and the relationship to the geometry of linear transformations. Introduction to nonlinear optimization. Applications for signal processing, system identification, robotics, and circuit design. Recommended preparation: ECE 143 (for Python) or equivalent competency in MATLAB programming.previous requirements:MATH 18 o MATH 31AH y ECE 15.

ECE 175A. Artificial intelligence elements: pattern recognition and machine learning (4)

Introduction to pattern recognition and machine learning. Decision functions. Statistical pattern classifiers. generative vs. discriminants for pattern classification. Selection of characteristics. Regression. Unsupervised learning. Grouping. machine learning applications.previous requirements:ECE 109 to ECE 174.

ECE 175B. Artificial intelligence elements: probabilistic reasoning and graphical models (4)

Bayes' rule as a motor of probabilistic reasoning; graphic models as knowledge encoders; conditional independence and D-Separation; random Markov fields; inference in graphic models; sampling methods and Markov Monte Carlo chain (MCMC); sequential data and the Viterbi and BCJR algorithms; The Baum-Welsh algorithm for the estimation of Markov Chain parameters.previous requirements: CEPE 175A.

ECE 176. Introduction to Deep Learning and Applications (4)

This course covers the fundamentals of deep learning, the basics of deep neural networks, including different network architectures (eg, ConvNet, RNN), and the optimization algorithms for training these networks. We will have hands-on implementation courses in PyTorch. This course will also introduce deep learning applications in computer vision, robotics, and sequence modeling in natural language processing. previous requirements:MATH 18 or MATH 31AH and ECE 143, or instructor consent.

ECE 180. Topics in Electrical and Computer Engineering (4)

Topics of special interest in electrical engineering and computer science. The theme will not be repeated, so it can be taken for credit more than once.previous requirements:consent of the instructor; department stamp.

ECE 181. Physical Optics and Fourier Optics (4)

Ray optics, wave optics, beam optics, Fourier optics and electromagnetic optics. Ray transfer array, cascaded optical arrays, scaled and graded index fiber numerical apertures. Fresnel and Fraunhofer diffractions, wave interference. Gaussian and Bessel beams, the ABCD law for transmissions through arbitrary optical systems. Spatial frequency, impulse response and transfer function of optical systems, Fourier transform and imaging properties of lenses, holography. Wave propagation in various media (non-homogeneous, dispersive, anisotropic or non-linear). (Course materials and/or program fees may apply.)previous requirements:ECE 103 and 107 degrees C– or higher.

ECE 182. Electromagnetic Optics, Guided Wave and Optical Fiber (4)

Polarization optics: glass optics, birefringence. Guided wave optics: modes, losses, dispersion, coupling, switching. Optical fiber: scaled and graduated index, single mode and multimode operation, attenuation, dispersion, fiber optic communications. resonator optics. (Course materials and/or program fees may apply.)previous requirements:ECE 103 and 107 degrees C– or higher.

ECE 183. Optical electronics (4)

Quantum electronics, light and matter interaction in atomic systems, semiconductors. Laser amplifiers and laser systems. Photodetection. Electro-optic and acousto-optic, photonic commutation. Fiber optic communication systems. Laboratories: semiconductor lasers, semiconductor photodetectors. (Course materials and/or program fees may apply.)previous requirements:ECE 103 and 107 degrees C– or higher.

ECE 184. Optical Information Processing and Holography (4)

(Together with ECE 241AL) Laboratories: optical holography, photorefractive effect, spatial filtering, computer generated holography. Students enrolled in ECE 184 will receive four units of credit; students enrolled in ECE 241AL will receive two units of credit. (Course materials and/or program fees may apply.)previous requirements:ECE 182 grade C– or higher.

ECE 185. Lasers and Modulators (4)

(Together with ECE 241BL) Laboratories: CO2 laser, HeNe laser, electro-optic modulation, acousto-optic modulation, spatial light modulators. Students enrolled in ECE 185 will receive four units of credit; students enrolled in ECE 241BL will receive two units of credit. (Course materials and/or program fees may apply.)previous requirements:ECE 183 grade C– or better.

ECE 187. Introduction to Biomedical Imaging and Sensing (4)

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Fundamentals of Image Processing: Image Theory, Image Processing, Pattern Recognition; digital radiography, computed tomography, nuclear medicine imaging, nuclear magnetic resonance imaging, ultrasound imaging, microscopy imaging.previous requirements:MATH 20A-B-F, 20C or 21C, 20D or 21D, PHYS 2A–D, ECE 101 (can be done concurrently) with grades of C– or better.

ECE 188. Electrical and Computer Engineering Topics with Laboratory (4)

Topics of special interest in electrical engineering and laboratory computing. The article will not be repeated and may be credited up to three times.previous requirements:first division position.

ECE 189. Technical Oratory (2)

Basics of technical speaking, including speech organization, body language (eye contact, hand gestures, etc.), volume and rhythm, and technical slide design. Students will practice technical speaking, including PowerPoint slide shows and speaker presentations, as well as impromptu presentations. Students cannot receive credit for ECE 189 and ENG 100E.previous requirements: first division position.

ECE 190. Engineering Project (4)

Students complete a project comprising at least 50% or more of engineering design to satisfy the following abilities: student creativity, open-ended problem statement/specification, consideration of alternative solutions/realistic constraints. Final written report is required.previous requirements:Students enrolling in this course must have completed all breadth courses and one depth course. A department seal is required to apply for ECE 190. (Specifications and application forms are available in the Graduate Office.)

ECE 191. Engineering Group Design Project (4)

Groups of students work to design, build, demonstrate, and document an engineering project. All students provide weekly progress reports on their assignments and contribute a section to the final project report.previous requirements:completion of all breadth courses and one depth course.

CEPE 193H. Honors project (4–8)

An advanced reading or research project completed under the direction of an ECE faculty member. It must contain enough design to satisfy the ECE program's four-unit design requirement. It must be done by letter note. It may span two quarters with a grade assigned at the end of both quarters.previous requirements:admission to the department's ECE honors program.

ECE 194. Viacar Design Project (4)

Students design, build, and drive an autonomous car using the principles of electrical engineering and computer science: circuit design, control theory, digital signal processing, embedded systems, microcontrollers, electromagnetism, and programming.previous requirements:none.

ECE 195. Teaching (2 or 4)

Teaching activities and tutorials associated with courses and seminars. No more than four units of ECE 195 may be used to satisfy graduation requirements. (Grades P/NP only).previous requirements:consent of the department head.

ECE 196. Practical Engineering Group Project (4)

Student groups work to build and demonstrate at least three engineering projects at the beginning, intermediate, and advanced levels. The final project consists of a new project created by the student team or an extension of an existing project. Student teams also prepare a manual as part of the final project documentation. It can be taken for credit twice.previous requirements:BENG 1 is CENG 4 is CSE 11 is CSE 8B is ECE 5 is MAE 3 is NANO 4 is SE 1.

ECE 197. ECE Internship (2, 4, 6, 8, 10 or 12)

An enrichment program that provides work experience with public/private sector employers. Subject to space availability, students will work at a local company under the supervision of a local professor and supervisor. (Grades P/NP only).previous requirements:UC San Diego minimum 2.5 GPA. Consent of the instructor and seal of the department.

ECE 198. Directed study group (1, 2, 3 or 4)

Electrical engineering and computer science topics whose study involves reading and discussion by a small group of students under the direction of a faculty member. (Grades P/NP only).previous requirements:consent of the instructor.

ECE 199. Undergraduate Independent Study (2 or 4)

Independent reading or research by special arrangement with a faculty member. (Grades P/NP only).previous requirements:consent of the instructor.


ECE 200. Research Lecture (2)

Group discussion of research activities and progress of group members. (Instructor consent is strongly recommended.) (S/U ratings only).previous requirements:graduate of pie.

ECE 201. Introduction to Biophysics (4)

The class will cover the fundamental physical principles of biological processes at the molecular, cellular, tissue, and organ levels related to human physiology and disease. Topics include the energy and dynamics of biological systems, physical factors in the environment, and the kinetics of biological systems.previous requirements:post-graduate or graduate level position.

ECE 202. Medical devices and interfaces (4)

This course will cover basic cellular and electrochemical processes, membrane potentials, ionic currents, nerve cell conductance, extracellular and intracellular stimulation, neural probe technology materials and devices, diagnostic and drug delivery devices. , physiological/material considerations, biosensors, microfluidics, optical and magnetic sensors. and electrical shielding.previous requirements:post-graduate or graduate level position.

ECE 203. Integrated Biomedical Circuits and Systems (4)

Analysis and design of integrated circuits for medical devices. Introduction to subthreshold conduction in MOS transistors and its similarities with biomolecular transport. Design of instrumentation amplifiers, sensors and electrical stimulation interfaces. Transcutaneous wireless energy transfer and electromagnetic effects on tissue. Recommended preparation: ECE 164 or BENG 186B or equivalent.previous requirements:post-graduate or graduate level position.

ECE 204. Statistical Learning in Bioinformatics (4)

A characteristic of bioinformatics is the computational analysis of complex data. The combination of statistics and algorithms produces statistical learning methods that automate the analysis of complex data. Such machine learning methods are widely used in systems biology and bioinformatics. This course provides an introduction to statistical learning and assumes familiarity with key statistical methods. Students cannot receive credit for BNFO 285 and ECE 204 and BENG 285. Crosslist with BNFO 285 and BENG 285.previous requirements:ECE 271A or ECE 271B or MATH 283; graduate of pie.

ECE 207A. Principles of medical imaging (4)

Fundamentals of Fourier transform and linear systems theory, including convolution, sampling, noise, filtering, reconstruction, and image display, with an emphasis on applications to biomedical imaging. Examples of optical, CT, MRI, ultrasound, nuclear, PET, and radiography images. Cross list with BENG 280A. Renumbered from ECE 207. Students may receive credit for one of the following: ECE 207A or ECE 207 or BENG 280A.previous requirements:graduate of pie.

ECE 208. Computational Evolutionary Biology (4)

Evolutionary biology (for example, the study of the tree of life) uses computational methods of statistics and machine learning. We cover methods widely used in many fields and apply them to biology, with a focus on scalability to big genomic data. Topics include dynamic programming, continuous-time Markov models, hidden Markov models, statistical inference of phylogenies, sequence alignment, uncertainty (eg, bootstrap), and heterogeneity (eg, phylogenetic admixture models).previous requirements: graduate of pie.

ECE 209. Statistical learning for biosignal processing (4)

Medical device systems are increasingly measuring biosignals from multiple sensors, requiring computational analysis of complex, time-varying multivariate data. The combination of statistics and algorithms produces statistical learning methods that automate the analysis of complex data. Applications within the domain of neural engineering using supervised and unsupervised generative statistical modeling techniques are explored. This course assumes familiarity with the main statistical methods.previous requirements:ECE 271A-B; graduate of pie.

UNECE 212AN. Principles of Nanoscience and Nanotechnology (4)

Introduction and rigorous treatment of the electronic, photonic, magnetic and mechanical properties of materials at the nanoscale. Concepts of mathematical physics, quantum mechanics, quantum optics, and electromagnetic theory will be introduced as appropriate. Students cannot receive credit for ECE 212A and ECE 212AN.previous requirements:graduate of pie.

CEPE 212BN. Nanoelectronics (4)

Quantum states and quantum transport of electrons; single electron devices; nanoelectronic devices and systems concepts; Introduction to molecular and organic electronics. Students cannot receive credit for ECE 212BN and ECE 212C.previous requirements:CEPE 212AN; graduate of pie.

CEPE 212CN. Nanophotonics (4)

Photonic properties of artificially engineered nanoscale inhomogeneous composite materials incorporating dielectrics, semiconductors and/or metals. Near field location effects and applications. Device and component applications. Students cannot receive credit for ECE 212CN and 212B.previous requirements:CEPE 212BN; graduate of pie.

ECE 221. Magnetic materials: principles and applications (4)

The basis of magnetism: views from classical and quantum mechanics. Different types of magnetic materials. Magnetic phenomena including anisotropy, magnetostriction, domains, and magnetization dynamics. Current frontiers of nanomagnetic research, including thin films and particles. Optical engineering, data storage and biomedical applications of soft and hard magnetic materials. previous requirements:graduate of pie.

ECE 222A. Antennas and their system applications (4)

Antennas, waves, polarization. Friis transmission and radar equations, dipoles, loops, slots, ground planes, traveling wave antennas, array theory, phased arrays, impedance, frequency independent antennas, microstrip antennas, cell phone antennas, level implications system such as MIMO, multibeam and phase array systems.Recommended preparation:ECE 107 or an equivalent college course in electromagnetism.previous requirements:graduate of pie.

ECE 222B. Applied Electromagnetic Theory - Electromagnetic (4)

Introductory course to electromagnetic theory with applications at the postgraduate level. Topics covered include Maxwell's equations, plane waves in free space and in the presence of interfaces, polarization, fields in metallic and dielectric waveguides, including surface waves; fields in metallic cavities, Green's functions, radiation and dispersion of electromagnetic fields.previous requirements:CEPE 222A; graduate of pie.

ECE 222C. Applied Electromagnetic Theory - Computational Methods for Electromagnetism (4)

Computational techniques for the numerical analysis of electromagnetic fields, including the finite difference time domain (FDTD) method, the finite difference frequency domain (FDFD) method, the method of moments (MOM), and the finite element method (FEM). Practice writing number codes. Review of commercial electromagnetic simulators.previous requirements:CEPE 222B; graduate of pie.

ECE 222D. Advanced Antenna Design (4)

Revision of 222A–B. Fourier transform, waveguide antennas. Mutual coupling, active impedance, Floquet modes in arrays. Microstrip antennas, surface waves. Analysis of reflectors and lenses: methods of conicity, overflow, aperture and physical optics. Impedance surfaces. Advanced concepts: Wavelength propagation, etc. (chosen by instructor). Recommended preparation: CE 222A, ECE 222B or equivalent.previous requirements:CEPE 222C; graduate of pie.

ECE 225A. Probability and Statistics for Data Science (4)

The course reinforces students' intuitive, theoretical, and computational understanding of probability and statistics and builds on these foundations to introduce more advanced concepts useful in both data science research and practice. The following topics will be covered: basic notions, convergence, estimation and hypothesis testing. Python programs, examples, and visualizations will be used throughout the course.previous requirements:graduate of pie.

ECE 225B. Universal probability and its applications in data science (4)

In many data science problems, there is only limited information about the statistical properties of the data. This course develops the concept of universal probability that can be used as a proxy for the unknown distribution of data and provides a unified framework for various data science problems, including compression, portfolio selection, prediction, and classification.previous requirements:ECE 225A or ECE 250; graduate of pie.

ECE 226. Deep Learning Optimization and Acceleration on Multiple Hardware Platforms (4)

This course aims to present the mathematical and computational challenges for holistic content/algorithm/hardware co-design of an efficient deep learning framework. Attendees will discuss select topics including DNN, CNN, and RNN in supervised and unsupervised settings. Special emphasis will be placed on optimizing the physical performance of DL on different hardware platforms. Hardware platforms include CPU-CPU and CPU-GPU architectures.previous requirements:ECE 250 or ECE 269 or ECE 271A; graduate of pie.

ECE 227. Large network data (4)

A network science course driven by data analysis. The class will focus on theoretical and empirical analysis performed on real data, including technological networks, social networks, information networks, biological networks, economic networks, and financial networks. Students will be exposed to several state-of-the-art software libraries for analyzing and visualizing network data through the Python notebook environment. Previous experience in Python programming is recommended.previous requirements:graduate of pie.

ECE 228. Machine Learning for Physical Applications (4)

Machine learning has received enormous interest. To learn from data, we use probability theory, which has been a mainstay of statistics and engineering for centuries. The class will focus on implementations for physics problems. Topics: Gaussian probabilities, linear models for regression, linear models for classification, neural networks, kernel methods, support vector machines, graphical models, mixture models, sequential sampling and estimation methods.previous requirements:graduate of pie.

ECE 229. Computational Data Analysis and Product Development (4)

Students learn to create statistical models and use computation and simulations to develop insights and deliver value to the end user. Randomly assigned teams will learn how to develop and implement a data science product, write and document code in an ongoing process, produce corresponding user documentation and communicate the value of the product verbally and in writing, and finally implement and maintain products in a cloud platform. Recommended preparation: ECE 143. previous requirements:ECE 225A or ECE 269, graduated.

UNECE 230A. Solid State Electronics I (4)

This course is designed to provide a general foundation in solid-state electronic devices and materials. Course content emphasizes fundamental and current topics in semiconductor physics related to ECE solid-state electronic sequences.Recommended preparation:ECE 135A-B or equivalent.previous requirements:graduate of pie.

ECE 230B. Solid State Electronics II (4)

Physics of solid state electronic devices, including p-n diodes, Schottky diodes, field effect transistors, bipolar transistors, pnpn structures. Computer simulation of devices, scaling characteristics, high-frequency performance, and circuit models.previous requirements:CEPE 230A; graduate of pie.

CEPE 230C. Solid State Electronics III (4)

This course is designed to provide a treatise on semiconductor devices based on solid-state phenomena. Emphasis will be placed on the dispersion and recombination processes of the band structure carriers and their influence on the transport properties.previous requirements:CEPE 230A; graduate of pie.

ECE 235. Nanoscale VLSI Devices (4)

This course covers modern research topics on sub-100nm scale, next-generation silicon VLSI devices. Starting with the fundamentals of scaling CMOS to nanometer dimensions, various advanced device and circuit concepts, including RF CMOS, Low Power CMOS, Silicon Memory, Silicon on Insulator, Bipolar SiGe, Silicon Voltage MOSFETs, etc. The physics of quasi-ballistic transport in a large-scale 10 nm MOSFET will be discussed in light of the recently developed scattering theory.previous requirements:graduate of pie.

ECE 236A. Fundamentals of Heterostructure Materials and Devices (4)

This course comprehensively covers heterostructured materials and devices. Topics include band alignments in semiconductor heterostructures and their measurement techniques, crystal growth, thermodynamics and kinetics, crystal defects and mismatches, lattice mismatches, strain energy and coherence limits, influence of strain on edges of bands, energy, quantum wells, 2DEG and superlattices, lateral and vertical transport in heterostructures, tunnel diodes, nitrides, polarization effects, HEMTs. Recommended Preparation: Completion of ECE 230B and ECE 230C.previous requirements:CEPE 230A; graduate of pie.

ECE 236B. Optical processes in semiconductors (4)

Radiation absorption and emission in semiconductors. Radiative transition and non-radiative recombination. Laser devices, modulators, and photodetectors will be discussed.Recommended preparation:ECE 230A, Solid State Electronics I and ECE 230C, Solid State Electronics III or equivalent.previous requirements:graduate of pie.

ECE 236C. Heterojunction Field Effect Transistors (4)

The physical and circuit applications of heterojunction field-effect transistors (HFETs) and heterojunction bipolar transistors (HBTs). The operating principles of FETs and BJTs are reviewed, and opportunities to improve their performance with proper material selection and bandgap engineering are highlighted. SiGe and III-V HBTs, III-V FETs, and current research areas are covered. Microwave characteristics, models and applications of representative circuits. Students who have already completed ECE 236C and/or D should not enroll in this course. Recommended preparation: ECE 230B or equivalent course with an emphasis in physics of solid-state electronic devices.previous requirements:CEPE 236B; graduate of pie.

CEPE 238A. Thermodynamics of Solids (4)

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Thermodynamics and statistical mechanics of solids. Basic concepts, equilibrium properties of alloy systems, thermodynamic information from phase diagrams, surfaces and interfaces, crystal defects. Crosslisted with Materials Science 201A and MAE 271A.previous requirements:consent of the instructor.

ECE 238B. Solid state diffusion and reaction kinetics (4)

Thermally activated processes. Boltzman factor, homogeneous and heterogeneous reactions, solid state diffusion, Fick's law, diffusion mechanisms, Kirkendall effects, Boltzmann-Manato analysis, high diffusivity pathways. Crosslisted with Materials Science 201B and MAE 271B.previous requirements:CEPE 238A.

UNECE 240A. Lasers and Optics (4)

Fresnel and Fraunhofer diffraction theory. Optical resonators, interferometry. Propagation and transformation of Gaussian beams. Laser oscillation and amplification, Q switching and mode lock of lasers, some specific laser systems.Recommended preparation:ECE 107 and ECE 182 or equivalent, Introduction to Quantum Mechanics or ECE 183.previous requirements:graduate of pie.

ECE 240B. Optical information processing (4)

Spatial bandwidth product, super resolution, space variable optics, partial coherence, coherent and incoherent light imaging, feedback processing, real-time light modulators for hybrid processing, non-linear processing. Optical computing and other applications.Recommended preparation:ECE 182 or equivalent.previous requirements:CEPE 240A; graduate of pie.

UNECE 240C. Optical Modulation and Detection (4)

Propagation of waves and rays in anisotropic media. Electro-optic switching and modulation. Deflection and acousto-optic modulation. Detection theory. Heterodyne detection, incoherent and coherent detection.Recommended preparation:ECE 181, ECE 183 or equivalent.previous requirements:CEPE 240B; graduate of pie.

CEPE 241A. Non-linear optics (4)

Second harmonic generation (color conversion), parametric amplification and oscillation, photorefractive effects and four-wave mixing, optical bistability; forms Recommended preparation: ECE 240A, C.previous requirements:graduate of pie.

CEPE 241B. Integrated Photonics (4)

Integrated photonic devices and components made with silicon, compound semiconductors, thin-film crystals, and dielectric materials. Design, analysis, and application of components (eg, waveguides, microresonators, couplers, modulators, lasers, and detectors) for use in communications, sensing, metrology, and other areas. previous requirements:CEPE 241A; graduate of pie.

CEPE 241C. Holographic optical elements (4)

Fresnel, Fraunhofer and Fourier holography. Analysis of thin and volume holograms, reflection and transmission holograms, color and polarization holograms. Computer generated, optically recorded holography. Applications for information storage, optical interconnections, 2D and 3D visualization, pattern recognition, and image processing.Recommended preparation:ECE 182 or equivalent.previous requirements:CEPE 241B; graduate of pie.

CEPE 243B. Optical Fiber Communication (4)

Optical fibers, waveguides, laser communication system. Modulation and demodulation; detection processes and communication receptors.Recommended preparation:ECE 240A-B-C or equivalent.previous requirements:CEPE 243A; graduate of pie.

ECE 244A. Statistical Optics (4)

Introduction to statistical phenomena in optics, including first-order properties of light waves generated from various sources. Optical wave coherence, high order coherence. Partial coherence and its effects on imaging systems. Image in the presence of a randomly inhomogeneous medium. Limits in the photoelectric detection of light.Recommended preparation:ECE 240A-B.previous requirements:graduate of pie.

CEPE 247A. Advanced Biophotonics (4)

Basic physics and chemistry for the interaction of photons with matter, including biological and synthetic materials; use of photon radiation pressure to manipulate objects and materials; advanced optoelectronic detection systems, devices, and methods, including time-resolved fluorescent and chemiluminescent methods, fluorescent energy transfer (FRET) techniques, quantum dots, and near-field optical techniques; underlying mechanisms of light-sensitive biological systems, including chloroplasts for photosynthetic energy conversion and the basis of vision processes. Cross list with BENG 247A and NANO 247A.previous requirements:graduate of pie.

CEPE 247B. Bioelectronics (4)

Topics covered will include photolithographic techniques for producing high-density DNA microarrays, incorporating CMOS control into electronic DNA microarrays, direct electronic detection technology used in microarrays and biosensing devices, and a focus on issues related to device fabrication. highly integrated. (lab-on-a-chip, in vivo biosensors, etc.) from heterogeneous materials and components. Cross list with BENG 247B and NANO 247B.previous requirements:graduate of pie.

CEPE 247C. BioNanotechnology (4)

Topics include nanosensors and nanodevices for clinical diagnosis and detection of biological warfare agents (bioterrorists); nanostructures for drug delivery; nanoarrays and nanodevices; use of nanoanalytical devices and systems; methods and techniques for modification or functionalization of nanoparticles and nanostructures with biological molecules; nanostructural aspects of fuel cells and biofuel cells; potential use of DNA and other biomolecules for ultra-high-density computing and data storage. Cross list with BENG 247C and NANO 247C.previous requirements:graduate of pie.

ECE 250. Random processes (4)

Random variables, probability distributions and densities, characteristic functions. Convergence in probability and mean square, stochastic processes, stationarity. Processes with orthogonal and independent increments. Power spectrum and power spectral density. Integrals and stochastic derivatives. Spectral representation of stationary processes in the broad sense, harmonious processes, representations of moving averages.Recommended preparation:CEPE 153.previous requirements:graduate of pie.

ECE 251A. Digital signal processing I (4)

discrete random signals; conventional spectral estimation (based on FFT). Coherence estimation and transfer function; model-based spectral estimation; linear prediction and AR modelling. Levinson-Durbin algorithm and network filters, minimum variance spectrum estimation. Cross list with SIOC 207B. SIOC 207A is intended for graduate students who have not earned a bachelor's degree in DSP. Recommended preparation: ECE 153, Probability, or ECE 250, Random Processes; ECE 161A, DSP; ECE 251C, Filter Banks and Wavelets; ECE 269, Linear Algebra and Application, or equivalent and SIOC 207A, Fundamentals of DSP, or equivalent.previous requirements:graduate of pie.

ECE 251B. Digital Signal Processing II (4)

Theory of adaptive filters, estimation errors for gradient algorithms and recursive least squares, convergence analysis and tracking of LMD, RLS and Kalman filter algorithms, comparative performance of Weiner and adaptive filters, implementations of traversal and network filters, analysis of performance for equalization, noise cancellation and linear prediction applications. Cross list with SIO 207C.previous requirements:graduate position; ECE 251A (for ECE 251B); SIO 207B (for SIO 207C).

ECE 251C. Filter banks and wavelets (4)

Fundamentals of multi-rate systems (principal identities, polyphase representations), fully decimated filter banks (QMF 2-channel filters, M-channel perfect reconstruction systems), paraunit perfect reconstruction filter banks, waveform transforms (multi-resolution, discrete waveform transform, filter banks, and waveforms) .previous requirements:graduate of pie.

UNECE 251D. Matrix processing (4)

The coherent processing of data collected from spatially distributed sensors for the purpose of signal enhancement and noise rejection or estimation of wavefield directionality. Conventional and adaptive beamforming. Corresponding field processing. Sparse matrix design and processing techniques. Applications in acoustics, geophysics and electromagnetism. Students will not receive credit for ECE 251D and SIOC 207D. Cross list with SIOC 207D.previous requirements:graduate position; ECE 251A or SIOC 207B.

ECE 252A. Voice compression (4)

Voice signals, production and perception, compression theory, high-speed compression using waveform coding (PCM, DPCM, ADPCM, . . .), DSP tools for low-speed coding, LPC voice coders, voice coding sinusoidal transform, multiband coding, average rate coding using excitation code linear prediction (CELP). Recommended preparation: ECE 161A.previous requirements:graduate of pie.

ECE 252B. voice recognition (4)

Signal analysis methods for recognition, dynamic time warping, single word recognition, hidden Markov models, connected words, and continuous speech recognition.previous requirements:CEPE 252A; graduate of pie.

ECE 253. Fundamentals of digital image processing (4)

Image quantification and sampling, image transformations, image enhancement, image compression. Recommended preparation: ECE 109, 153, ECE 161, ECE 161A.

ECE 254. Detection Theory (4)

Hypothesis testing, signal detection in white and colored Gaussian noise; signal parameter estimation, maximum probability detection; signal resolution; detection and estimation of stochastic signals; applications for radar, sonar and communications.Recommended preparation:CEPE 153.previous requirements:graduate of pie.

ECE 255A. Information Theory (4)

Introduction to basic concepts, source coding theorems, capacity, noisy channel coding theorem. Recommended preparation: ECE 154A-B-C.previous requirements:graduate of pie.

ECE 255B. source code (4)

Lossy source coding theory and practice, vector quantization, predictive and differential coding, universal coding, source channel coding, asymptotic theory, voice and image applications. Students who achieved 255BN cannot receive 255B as credit. Recommended preparation: ECE 250 and 259A or 259AN.previous requirements:CEPE 255A; graduate of pie.

UNECE 255C. Network information theory (4)

The course aims to provide a broad coverage of key results, techniques, and open problems in the information theory of networks. Topics include background (typical data and sequence measurements, point-to-point communication) and single-hop networks (multiple access channels, degraded transmission channels, interference channels, stateful channels, general transmission channels, Gaussian vector channels , lossless distributed source coding , source code with secondary information).previous requirements:CEPE 250; CEPE 255B; graduate of pie.

ECE 256. Fundamentals of image and video compression (4)

This course provides the theoretical foundations of image and video compression. Topics cover basic coding tools such as entropy coding, transformation, and quantization, as well as advanced coding methods: motion estimation and compensation, error-resistant coding, and scalable coding. Recommended preparation: Programming in MATLAB.previous requirements:graduate of pie.

CEPE 257A. Modern communication networks (4)

This course focuses on modern local area networks (Wi-Fi, Ethernet, etc.) and wide area networks (LTE, 5G, etc.). Topics to be covered include end-to-end network architecture, physical layer packet processing, medium access control protocols, mobile IP and mobility management, TCP over wireless mobile applications (e.g. mobile web, streaming video in real time and telephony). Recommended preparation: ECE 158A.previous requirements:license or consent of the instructor.

CEPE 257B. Fundamentals of the wireless network (4)

This course will focus on the principles, architectures, and analytical methodologies for the design of multi-user wireless networks. Topics to be covered include cellular approaches, call processing, digital modulation, MIMO technology, broadband networks, ad-hoc networks, and packet wireless access.Recommended preparation:ECE 159A and 154B, or equivalent.previous requirements:graduate of pie.

ECE 257C. Stochastic models of wireless networks (4)

Elements of spatial punctual processes. Stochastic spatial models of wireless networks. Topological structure, interference, stochastic dependencies. Elements of network information theory/physical statistical models of information flow. Role of signal propagation/random fading models. Decentralized operation, route discovery, architectural principles. Power limitations/random failures. Recommended preparation: Previous exposure to stochastic processes and information theory.previous requirements: CEPE 257B; graduate of pie.

ECE 258A. Digital Communications I (4)

Digital communication theory, including performance of various modulation techniques, intersymbol interference effects, adaptive equalization, spread spectrum communication. Recommended preparation: ECE 155.previous requirements: CEPE 250; graduate of pie.

ECE 258B. Digital Communications II (4)

Digital communication theory, including the performance of various modulation techniques, intersymbol interference effects, adaptive equalization, and spread spectrum communication.previous requirements:CEPE 258A; graduate of pie.

ECE 259A. Algebraic coding (4)

Fundamentals of block codes, introduction to groups, rings and finite fields, non-binary codes, cyclic codes such as BCH and RS codes, decoding algorithms, applications. Students who have taken ECE 259AN may not receive credit for ECE 259A.previous requirements:graduate of pie.

ECE 259B. Probabilistic Coding (4)

Convolutional codes, maximum likelihood (ML) decoding, maximum posterior decoding (MAP), parallel and serial concatenation architectures, turbo codes, repetition accumulated (RA) codes, turbo principle, turbo decoding, graph-based codes , message decoding step, low density parity check codes, threshold analysis, applications. Students who have taken ECE 259BN may not receive credit for ECE 259B.Recommended preparation:ECE 154A-B-C.previous requirements:CEPE 259A; graduate of pie.

ECE 259C. Advanced Topics in Coding (4)

Advanced topics in coding theory. Course content varies by instructor. Examples of course topics: Coded modulation for bandwidth-efficient data transmission; advanced algebraic and combinatorial coding theory; space-time coding for wireless communications; strict encoding for digital recording. Students who have taken ECE 259CN may not receive credit for ECE 259C.previous requirements:ECE 259A-B; graduate of pie.

UNECE 260A. Algorithms and architectures of digital VLSI systems (4)

Custom and semi-custom VLSI design, both from the perspective of the circuit and system designer. MOS transistor theory, circuit characterization and performance estimation. CMOS logic design will be emphasized. Computer Aided Design (CAD) tools will be introduced for transistor level simulation, design, and verification. Includes two hours of lab time per week. Recommended preparation: Graduate level semiconductor electronics and digital systems design, ECE 165 or equivalent.previous requirements:graduate of pie.

ECE 260B. Design of Systems and Integrated Circuits VLSI (4)

VLSI implementation methodology at block, circuit and design abstraction levels. Circuit building blocks, including onboard memory and clock layout. Computer Aided Design (synthesis, placement and routing, verification) and performance analysis and small group block implementation designs ranging from RTL to recording using state of the art EDA tools. Cross list with CSE 241A. Recommended preparation: ECE 165.previous requirements:CEPE 260A; graduate of pie.

CEPE 260C. Advanced VLSI Themes (4)

Advanced topics in design practices and methodologies for modern system-on-chip design. Different design alternatives are presented and analyzed. Advanced design tools are used to design a hardware and software system. Class discussions, participation in projects and presentations, and special topic assignments are emphasized.previous requirements:CEPE 260B; graduate of pie.

CEPE 264A. Circuits and Analog Integrated Systems CMOS I (4)

Basic frequency response of the CMOS gain stage and current mirror settings. Advanced feedback and stability analysis; compensation techniques. High performance CMOS op amp topologies. Noise and distortion analysis.Recommended preparation:ECE 164 and ECE 153, or equivalent courses.previous requirements:graduate of pie.

CEPE 264B. Systems and analogue integrated circuits CMOS II (4)

Non-ideal effects and their mitigation in high performance operational amplifiers. Switched Capacitor Circuit Techniques: CMOS Circuit Topologies, Analysis and Mitigation of Non-Ideal Effects, and Filter Synthesis. Overview of CMOS samplers, data converters, and PLLs.Recommended preparation:ECE 161A, Introduction to Digital Signal Processing and ECE 251A, Digital Signal Processing I.previous requirements:ECE 250 or ECE 264A; graduate of pie.

CEPE 264C. Systems and analogue integrated circuits CMOS III (4)

Integrated CMOS analog/digital systems: analog-to-digital and digital-to-analog converters, Nyquist vs. oversampling, linearity, jitter, scrambling, calibration, speed vs. resolution, pipeline, mirroring, interpolation, averaging. Recommended preparation: ECE 163 and 164.previous requirements:CEPE 264B; graduate of pie.

ECE 264D. CMOS IV Analog Integrated Circuits and Systems (4)

PLL: phase noise effect, VCO, phase detector, charge pump, integer/fractional N frequency synthesizer, clock and data recovery, decision feedback. Filter: Continuous Time Filter, Complex I-Q Filter, Raised Cosine, Gaussian, Delay, Zero EQs. Recommended preparation: ECE 251A. Digital signal processing I.previous requirements:ECE 250 or ECE 264A; graduate of pie.

ECE 265A. Communication Circuit I Project (4)

(Video) Why I chose Electrical Engineering over Computer Engineering

Introduction to the concepts of noise and linearity. Budget the system for optimal dynamic range. Compensation of the frequency plan. Linearity analysis techniques. Downconversion and upconversion techniques. Modulation and demodulation. Design of RF microwave and communications systems. Current research topics in the area.previous requirements:ECE 166 or instructor consent; standing graduate.

ECE 265B. Communication Circuit Project II (4)

RF ICs: Low Noise Amplifiers, AGCs, Mixers, Filters, Voltage Controlled Oscillators. BJT and CMOS technologies for radio frequency and microwave applications. Device modeling for radio frequency applications. Design and device trade-offs for linearity, noise, power dissipation, and dynamic range. Current research topics in the area.previous requirements:ECE 166 and ECE 265A or instructor consent; standing graduate.

ECE 265C. Power amplifiers for wireless communications (4)

Design of power amplifiers for mobile terminals and base stations, with emphasis on high linearity and efficiency. After a discussion of classical designs (Class A, AB, B, C, D, E, F, and S), linearization procedures are introduced, and composite architectures (envelope tracking, EER, and Doherty) are discussed. Familiarity with basic microwave design and communication system architecture is assumed.Recommended preparation:CEPE 166.previous requirements:ECE 265A-B; consent of the instructor; standing graduate.

CEPE 265D. Communication Circuits III (4)

IF upconversion and forward conversion mixers, harmonic and spurious emissions, I/Q mismatch, LO leakage, GPS/receive band noise, harmonic suppression, and 4fmod. Driver amplifiers, load line, OIP3/ACPR, P1dB, Psat, PAE, in-band noise and distortion, out-of-band noise and emissions. VCO design, in-band and out-of-band phase noise. N-path filters. Diversity, MIMO, carrier aggregation, and beamforming transmitter and receiver architectures.previous requirements:ECE 265A-B; graduate of pie.

ECE 266. CMOS Circuits Laboratory (4)

Physical design of CMOS circuits through the life cycle of tapeout and measurement. Design techniques covering process variation, parasitic, ESD, and snub ring assembly. Students will learn the entire flow of the Tapeout tool, including DRC, LVS, and RCX. Discussion of PCB and package design, as well as how to measure and characterize the performance of CMOS circuits.previous requirements:ECE 264A-B or ECE 265A-B; graduate of pie.

ECE 267. Algorithms and Network Analysis/Graphics (4)

Modern network theory from an algorithmic perspective with an emphasis on fundamentals in terms of performance analysis and design. Topics include algorithmic problems that arise in the context of scheduling, routing, and congestion control in communication networks, including wired, wireless, sensor, and social networks.previous requirements:graduate of pie.

ECE 268. Security of Embedded Hardware Systems (4)

The course provides an overview of the areas of security and protection of modern hardware, embedded systems, and IoT. It covers essential cryptographic methodologies and basic components needed to build a secure system. Topics include low-cost security, physical and side-channel attacks, physical security primitives, physical security and proof of presence, secure execution of hardware-based programs, scalable deployment of secure features, emerging technologies, and growing threats. Recommended preparation: Programming in a standard programming language. Graduate level knowledge of integrated circuit design flow and digital designs.previous requirements:graduate of pie.

ECE 269. Linear Algebra and Application (4)

This course will build the mathematical foundations of linear algebraic techniques and justify their use in signal processing, communication, and machine learning. Topics include geometry of vector and Hilbert spaces, orthogonal projection, systems of linear equations and the role of scattering, self-analysis, Hermitian matrices and variational characterization, positive semidefinite matrices, singular value decomposition, and principal component analysis.previous requirements:graduate of pie.

ECE 271A. Statistical Learning I (4)

Bayesian decision theory; parameter estimation; maximum likelihood; bias-variance compensation; Bayesian estimation; predictive distribution; conjugated and non-informative priors; dimensionality and dimensionality reduction; principal component analysis; Fisher's linear discriminant analysis; density estimation; parametric versus kernel-based methods; expectation-maximization; formsRecommended preparation:CEPE 109.previous requirements:graduate of pie.

ECE 271B. Statistical Learning II (4)

linear discriminants; the perceptron; margin and high margin classifiers; learning theory; empirical risk minimization vs. structural; the CV size; kernel functions; reproduce Hilbert spaces from the kernel; regularization theory; Lagrangian optimization; duality theory; the support vector machine; boost; Gaussian processes; formsRecommended preparation:CEPE 109.previous requirements:CEPE 271A; graduate of pie.

ECE 271C. Deep learning and applications (4)

Fundamentals of deep learning. Deep learning architectures and learning algorithms. Feedforward, convolutional and recurrent networks. Regularization. Applications for vision, speech or word processing.previous requirements: ECE 271A-B; graduate of pie.

CEPE 272A. Stochastic Processes in Dynamic Systems I (4)

Diffusion equations, estimation and linear and non-linear detection, random fields, optimization of stochastic dynamic systems, applications of stochastic optimization to problems. Recommended preparation: ECE 250.previous requirements:CEPE 269; graduate of pie.

CEPE 272B. Stochastic Processes in Dynamic Systems II (4)

Continuous and discrete random processes, Markov models and hidden Markov models, Martingales, linear and non-linear estimation. Applications in finance mathematics and real options.previous requirements:CEPE 272A; graduate of pie.

ECE 273. Convex Optimization and Applications (4)

This course covers some convex optimization theories and algorithms. It will focus mainly on the recognition and formulation of convex problems, duality and applications in various fields (system design, pattern recognition, combinatorial optimization, financial engineering, etc.).previous requirements:CEPE 269; graduate of pie.

CEPE 275A. Parameter Estimation I (4)

Linear least squares (batch, recursive, total, sparse, pseudoinverse, QR, SVD); Statistical figures of merit (bias, consistency, Cramer-Rao lower limit, efficiency); maximum likelihood estimation (MLE); enough statistics; Algorithms for calculating the MLE, including the Expectation Maximation (EM) algorithm. The problem of the lack of information; the problem of outliers.Recommended preparation:ECE 109 to ECE 153.previous requirements:graduate of pie.

CEPE 275B. Estimation of Parameters II (4)

the Bayesian Statistical Framework; Estimation of parameters and states of Hidden Markov Models, including Kalman Filtering and Viterbi and Baum-Welsh algorithms. It provides a solid foundation for follow-on courses in Bayesian machine learning theory. Recommended preparation: ECE 153.previous requirements:graduate of pie.

ECE 276A. Detection and estimation in robotics (4)

This course covers the mathematical foundations of Bayesian filtering and its application to sensing and estimation in mobile robotics. Topics include maximum likelihood estimation (MLE), expectation maximization (EM), Gaussian and particle filters, simultaneous localization and mapping (SLAM), visual and optical flow, and hidden Markov models (HMM). Recommended Preparation: Students should have knowledge equivalent to the following ECE courses: ECE 101 or ECE 171 and ECE 153 and ECE 174.previous requirements: graduate of pie.

ECE 276B. Planning and Learning in Robotics (4)

This course covers the fundamentals of optimal control and reinforcement learning and its application to planning and decision making in mobile robotics. Topics include Markov Decision Processes (MDPs), Pontryagin's Maximum Principle, Linear Quadratic Regulation (LQR), Deterministic Planning, Value and Policy Iteration, and Policy Gradient Methods.previous requirements:CEPE 276A; graduate of pie.

ECE 276C. Robot Manipulation and Control (4)

This course follows ECE 276A-B and covers techniques relevant to robot manipulation, as well as open problems involving new forms of machine learning (i.e. reinforcement learning). Topics will review kinematics, dynamics, low-level control and motion planning, and reinforcement learning approaches. A substantial student-led project demonstrates the collected knowledge of robotics from ECE 276A-B-C.previous requirements:CEPE 276B; graduate of pie.

ECE 277. GPU Programming (4)

This course is high-level GPU programming for parallel data processing. Topics cover parallel CUDA programming on the GPU, including efficient memory access, threading models, multi-stream and multi-GPU programming. Focusing on practical applications such as big data processing, visualization, and artificial intelligence through the real-time GPU system. Recommended preparation: High level C/C++ programming skills, ECE 15 or equivalent, CSE 240A or equivalent.previous requirements: graduate of pie.

ECE 278. Mathematics Topics for the Comprehensive Master's Exam (4)

Mathematical topics covered on the comprehensive ECE mastery exam, including calculus, linear algebra and linear systems, statistics, and probability theory. Additional topics include vector calculus, partial differential equations, linear transformations, and probability.previous requirements:graduate of pie.

ECE 279. Special seminar (2)

Seminar course in which topics of special interest to students of electrical engineering and computer science will be exposed. S/U grades only. It can be taken for credit three times. previous requirements: graduate of pie.

ECE 280. Special Topics in Electronic Materials and Devices/Applied Physics (4)

A course to be taught at the discretion of the faculty in which topics of interest in electronic devices and materials or applied physics will be presented by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 281. Special Topics in Nanoscience/Nanotechnology (4)

A course to be taught at the discretion of the faculty in which topics of interest in nanoscience and nanotechnology will be presented by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 282. Special Topics in Photonics/Applied Optics (4)

Course to be dictated at the discretion of the faculty in which topics of interest in photonics, optoelectronic materials, devices, systems and applications will be exposed by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 283. Special Topics in Electronic Circuits and Systems (4)

Course to be dictated at the discretion of the faculty in which topics of interest in electronic circuits and systems will be exposed by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 284. Special Topics in Computer Engineering (4)

Course to be dictated at the discretion of the faculty in which topics of interest in computer engineering will be presented by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 285. Special Topics in Signal and Image Processing/Robotics and Control Systems (4)

Course to be dictated at the discretion of the faculty in which topics of interest in signal and image processing or robotics and control systems will be exposed by visiting or resident professors. The theme will not be repeated, it can be taken for credit more than once.previous requirements:graduate of pie.

ECE 286. Latest Topics in Computational Statistics and Machine Learning (4)

The class discusses fundamental and cutting-edge research topics in computational statistics and machine learning. Topics vary based on current research and have included non-parametric Bayesian models; sampling methods for inference in graphical models; Markov chain Monte Carlo methods (MCMC).previous requirements:graduate of pie.

ECE 287. Special Topics in Communication Theory and Systems (4)

A course to be taught at the discretion of the faculty in which topics of interest in data sciences will be presented by visiting or resident professors. It will not be repeated, so it can be credited more than once.previous requirements:graduate of pie.

ECE 289. Special Topics in Electrical Engineering and Computer Science (4)

A course to be taught at the discretion of the faculty in which general topics of interest in electrical engineering and computer science will be presented by visiting or resident professors. It may be taken for credit six times, as long as each course is a different topic.previous requirements:graduate of pie.

ECE 290. Postgraduate Seminar in Current ECE Research (2)

Weekly discussion of current research conducted in the Department of Electrical and Computer Engineering by faculty members involved in research projects. (S/U degrees only.)previous requirements: graduate of pie.

ECE 291. Industry Sponsored Engineering Design Project (4)

Design, build and demonstrate an engineering project in groups. All students make weekly progress reports on homework and write a final report, with individual exams and presentations. The projects/sponsorships are born from the needs of the local industry.Recommended preparation:ECE 230 or ECE 240 or ECE 251 or ECE 253 or ECE 258 or equivalent.previous requirements:graduate of pie.

ECE 293. Postgraduate Seminar in Communication Theory and Systems (2)

Weekly discussion of current research topics in communication theory and systems. (S/U degrees only.)previous requirements: graduate of pie.

ECE 294. Graduate Seminar on Electronic Devices and Materials/Applied Physics (2)

Weekly discussion of current research topics in electronic devices and materials or applied solid state physics and quantum electronics. (S/U degrees only.)previous requirements: graduate of pie.

ECE 295. Postgraduate Seminar in Signal and Image Processing/Robotics and Control Systems (2)

Weekly discussion of research topics in control systems and signal and image processing in robotics. (S/U degrees only.)previous requirements: graduate of pie.

ECE 296. Graduate Seminar in Photonics/Applied Optics (2)

Weekly discussion of current research topics in photonics and applied optics, including imaging, photonic communications, sensing, energy, and signal processing. (S/U degrees only.)previous requirements: graduate of pie.

ECE 297. Postgraduate Seminar in Nanoscience/Nanotechnology (2)

Weekly discussion of current research topics in nanoscience and nanotechnology. (S/U degrees only.)previous requirements:graduate of pie.

ECE 298. Independent Study (1–16)

Open to suitably qualified graduate students who wish to pursue a subject through advanced study under the direction of a member of staff. (S/U degrees only.)previous requirements:consent of the instructor.

ECE 299. Research (1–16)

(S/U degrees only.)

ECE 501. Teaching (1–4)

Teaching activities and tutorials associated with courses and seminars. The number of units of credit depends on the number of hours spent in class or section attendance. (S/U degrees only.)previous requirements:consent of the department head.


Is Electrical and Computer Engineering difficult? ›

Someone studying electrical engineering should be highly interested in learning about the physics and mathematics of electronics, electricity, and electromagnetism. This can be a difficult subject to master even for those talented in this area.

Can I do both Electrical and Computer Engineering? ›

It's common for engineers in both fields to have a bachelor's degree in electrical and computer engineering (ECE), providing the fundamental knowledge and skills necessary for roles in either industry.

Is electronics and Computer Engineering easy? ›

Yes, electronics and computer engineering is a bit tough compared to computer science engineering as in the ECE branch, two separate engineering fields of engineering are integrated.

Is an Electrical and Computer Engineering degree worth it? ›

The answer is Yes. Being a computer engineer is the dream of millions of graduates. Whether you're aiming to earn high incomes every year, enhance job satisfaction, or retain a sustainable future, this desirable field hits the spot.

What is the hardest engineering degree? ›

What Is the Hardest Engineering Major?
Top 3 Hardest Engineering MajorsTop 3 Easiest Engineering Majors
1. Chemical engineering (19.66 hours)1. Industrial engineering (15.68 hours)
2. Aero and astronautical engineering (19.24 hours)2. Computer engineering and technology (16.46 hours)
1 more row

What is the toughest engineering branch to study? ›

Electrical engineering is considered to be one of the toughest Engineering courses by students, mostly because of the abstract thinking involved. An electrical engineer's job involves a lot of creative and abstract thinking because the elements of the machine or system they are working on are not visible to their eyes.

Is there coding in electrical engineering? ›

Electrical engineers work closely with electric light and power systems, and use a variety of programming languages to develop and automate devices.

Does computer engineering require coding? ›

Computer engineers need technical skills such as programming, coding, and network architecture. Computer engineers also use analytical, problem-solving, and communication skills in their work.

Is there coding in electronics and computer engineering? ›

Computer Engineering is the study of programming and computing. A degree in computer engineering gives you the foundational skills needed to pursue a career in coding or computer programming. It gives you a good understanding of the theory behind the processes involved in creating computer programs and applications.

Which engineering has highest salary? ›

Top 10 Highest Paying Engineering Jobs of 2022
  • Systems Engineer. ...
  • Electrical Engineer. ...
  • Chemical Engineer. ...
  • Big Data Engineer. ...
  • Nuclear Engineer. ...
  • Aerospace Engineer. ...
  • Computer Hardware Engineer. ...
  • Petroleum Engineer.
Mar 24, 2023

Is ECM better than EEE? ›

Well both the fields EEE and ECM will give you good knowledge of Electronics but EEE is more hardware oriented in which you will deal with machines whereas ECM is more software oriented. Both the fields are good for Jobs as there is a wide application of both fields in real time.

Is computer engineering the hardest major? ›

Students worldwide study engineering each year. It's no secret that some majors are harder than others. We've done the research and concluded that chemical, aerospace, biomedical, electrical, and computer engineering are the top 5 hardest engineering majors you can enroll in.

Why EE is better than CS? ›

The greatest difference between a career in electrical engineering and a career in computer science, however, is that the engineer builds solutions by working with electrical components, while computer scientists develop theoretical solutions with logic and computation.

Why is electrical and computer engineering a good major? ›

Few disciplines have had an impact on society greater than that of Electrical or Computer Engineering. ECE majors create and work directly with a wide array of innovative technologies: Computers, Electronics, Communications, Automation, Robotics, Sensors and Electronic Devices.

What does an electrical and computer engineer do? ›

Electrical and computer engineers design and implement devices, circuits, and systems for communication, computing, power, control, medical diagnostics, and transportation.

What type of engineer is Elon Musk? ›

Elon Musk has no formal engineering degree, but he clearly is industrial engineer by profession. His approach to business and problem solving is typical industrial engineering approach. His actual degrees (BS in both economics and physics) have good overlap with undergraduate curriculum in IE.

What is the lowest paid engineer? ›

The Lowest Paying: Environmental, Geological, Civil, and Biological Engineering.

What is a good GPA for engineering? ›

A 3.5 and above is considered a great GPA for engineering students. It shows that you get mostly A's in all of your classes. Occasionally, companies will require a 3.5 GPA or above for internships.

Is electrical engineering a difficult major? ›

Electrical engineering is a common engineering major for college students, but it is still ranked as one of the hardest. There are so many small systems to learn within electrical engineering so it can be difficult to see what you're physically doing.

Is it better to be an electrical engineer or computer engineer? ›

If your interest lies in computing and software, computer engineering is likely the answer. If you have more of a general fascination with electronic devices and their function, you may prefer electrical engineering.

Which engineering is the easiest? ›

Environmental Engineering

It's considered one of the easier engineering majors that you can study though, because it's not as focused on advanced math and physics as other engineering majors.

Is computer engineering a difficult major? ›

Because computer engineering is a part of the STEM, which consists of some of the most difficult majors college students can choose from, majoring in it can be quite challenging. There are simply lots of math and physics as well as logical thinking and problem-solving skills involved in computer engineering.

Is electrical engineering math heavy? ›

Engineering is a calculus-heavy program, regardless of whether it is Mechanical, electrical, or civil engineering focused. The first circuits class you'll take in this program require Calculus 2 as a Pre-req! Other math requirements of the degree are Calc. 3 , differential equations and Linear algebra.

How stressful is electrical engineering? ›

Stress. While working as an electrical engineer can be rewarding, it can also be challenging. Electrical engineers are responsible for troubleshooting and resolving issues to ensure each system, device and component they work on operates effectively.

Is there a lot of math in electrical engineering? ›

Calculus. As we move beyond resistor circuits and start to include capacitors and inductors, we need calculus to understand how they work. Think of calculus as a corequisite in parallel with electrical engineering. You don't need to have a complete calculus background to get started, but it is helpful before too long.


1. Electrical and Computer Engineering @ Cornell University
(Cornell ECE)
2. Electrical and Computer Engineering at the University of Michigan
(Michigan Engineering)
3. Electrical and Computer Engineering
(Department of Electrical & Computer Engineering)
4. What is Electrical and Computer Engineering?
(Department of Electrical and Computer Engineering, UMN)
5. Electrical and Computer Engineering at Georgia Tech
(Georgia Tech College of Engineering)
6. Electrical and Computer Engineering
(Texas A&M University College of Engineering)


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