Electrical and Computer Engineering

This course is an introductory course in programming and computing concepts for engineering students who have little/no experience in computing/programming and are interested in learning programming in the context of a robotic autonomous vehicle system. Intro to fundamental programming concepts: control flow, iteration, abstraction, sub-routines, functions, recursion, lists and arrays. Course is tightly integrated with a real robotic platform: an autonomous Unmanned Aerial Vehicle, which students will program and fly in lab, as they learn programming.

Signals that carry information, e.g. sound, images, sensors, radar, communication, robotic control, play a central role in technology and engineering. This course teaches mathematical tools to analyze, manipulate, and preserve information signals. We discuss how continuous signals can be perfectly represented through sampling, leading to digital signals. Major focus points are the Fourier transform, linear time-invariant systems, frequency domain, and filtering. We use MATLAB for laboratory exercises. Prerequisite: knowledge of elementary calculus.

Introduction to electronic circuits and systems. Methods of circuit analysis to create functions from devices, including resistors, capacitors, inductors, diodes, and transistors, in conjunction with op-amps. Quantitative focus on DC and higher-frequency signals using linear systems theory with major emphasis on intuition. Students pursue design (using op-amps and micro controllers), simulations (using SPICE), and analysis in labs.

Introduction of the basic concepts in logic design that form the basis of computation and communication circuits. This course will start from scratch and end with building a working computer on which we will run small programs.

This course explores broadly renewable energy systems and smart grids. Technical and operational principles of the modern electric grids will be introduced, followed by an overview of various energy sources from fossil-fuel generators to photovoltaic systems. The intermittency of renewable energy systems and its impact on the electric grid will be discussed together with its potential solutions: energy storage systems and demand response techniques. This course will also include a few experimental demo sessions in which students will gain hands-on experience in understanding the fundamental principles of power conversion.

Comprehensive laboratory-based course in electronic system design and analysis. Covers formal methods for the design and analysis of moderately complex real-world electronic systems. Course is centered around a semester-long design project involving a computer-controlled vehicle designed and constructed by teams of two students. Integrates microprocessors, communications, and control. Prerequisites: ECE 201 and 203.

This course provides a comprehensive and practical background for students interested in continuous mathematics for computer science. The goal is to prepare students for higher-level subjects in artificial intelligence, machine learning, computer vision, natural language processing, graphics, and other topics that require numerical computation. This course is intended students who wish to pursue these more advanced topics, but who have not taken (or do not feel comfortable) with university-level multivariable calculus (e.g., MAT 201/203) and probability (e.g., ORF 245 or ORF 309).

Intro to fundamentals and operations of semiconductor devices and sensors and micro/nano fabrication technologies used to make them. Devices include field-effect transistors, photodetectors and solar cells, light-emitting diodes and lasers. Applications include: computing and microchips, optical transmission of info (the internet backbone), displays and renewable energy. Students will fabricate their own devises in a clean room and test via microprobes. Special emphasis placed on the interplay between the material properties, fabrication capabilities, device performance and ultimate system performance. Prerequisites:MAT103-104 and PHY103-104.

Using applied microeconomic theory and case studies, this course examines the impact of digital technology on markets. In a connected market, information is freely and instantly available to all participants. We ask how these features affect the way markets function. Topics include the economics of platform markets and multisided markets, the impact of the internet on the news media, education, health care and new industries, such as big-data driven industries, social networks, technological innovation and intellectual property, internet security, privacy and other regulatory issues.

Fundamentals of quantum mechanics and statistical mechanics needed for understanding the principles of operation of modern solid state and optoelectronic devices and quantum computers. Topics covered include Schrödinger Equation, Operator and Matrix Methods, Quantum Statistics and Distribution Functions, and Approximation Methods, with examples from solid state and materials physics and quantum electronics. Prerequisites: (PHY 103 or PHY 105) and (PHY 104 or PHY 106) or EGR 151 and EGR 153. MAT 201 and MAT 202, or EGR 152 and EGR 154.

Robotics is a rapidly-growing field with applications including unmanned aerial vehicles, autonomous cars, and robotic manipulators. This course will provide an introduction to the basic theoretical and algorithmic principles behind robotic systems. The course will also allow students to get hands-on experience through project-based assignments. Topics include inverse kinematics, motion planning, localization, mapping, vision, and reinforcement learning. Prerequisites: MAT 201 or 203, MAT 202 or 204, COS 126. Recommended ORF 309 and MAE 305. A.B. students ST requirement; B.S.E. students 1st-year science requirement. Two 90-minute lectures.

This course provides the students with a broad and solid background in electromagnetism, including both statics and dynamics, as described by Maxwell's equations. Fundamental concepts of diffraction theory, Fourier optics, polarization of light, and geometrical optics will be discussed. Emphasis is on engineering principles, and applications will be discussed throughout. A laboratory component will acquaint students with modern simulation tools for modeling optical phenomena. Examples include cavities, waveguides, antennas, fiber optic communications, and imaging. Prerequisite: PHY 103 and PHY 104 or equivalent.

Machine learning for predictive data analytics; information-based learning; similarity-based learning; probability-based learning; error-based learning; deep learning; evaluation.

An introduction to computer architecture and organization. Instruction set design; basic processor implementation techniques; caches and virtual memory; CPUs, GPUs, storage systems, hardware-software APIs, and compilers. Goal: building understanding of the systems you design and program. Design trade-offs among cost, performance, complexity, and power dissipation. Prerequisites: COS 217.

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing. Prerequisite: Linear algebra at the level of MAT 202, 204, 217, or the equivalent.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical and computer engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical and computer engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Principles and design of solar energy conversion systems. Quantity and availability of solar energy. Physics and chemistry of solar energy conversion: solar optics, optical excitation, capture of excited energy, and transport of excitations or electronic charge. Conversion methods: thermal, wind, photoelectric, photoelectrochemical, photosynthetic, biomass. Solar energy systems: low and high temperature conversion, photovoltaics. Storage of solar energy. Conversion efficiency, systems cost, and lifecycle considerations.

Security issues in computing, communications, and electronic commerce. Goals and vulnerabilities; legal and ethical issues; basic cryptology; private and authenticated communication; electronic commerce; software security; viruses and other malicious code; operating system protection; trusted systems design; network security; firewalls; policy, administration and procedures; auditing; physical security; disaster recovery; reliability; content protection; privacy. Prerequisites: 217 and 226. Two lectures.

The course is an introduction to the theoretical foundations of machine learning. A variety of classical and recent results in machine learning and statistical analysis including: Bayesian classification, regression, regularization, sparse regression, support vector machines, kernels, neural networks, convolutional networks, and reinforcement learning.

An introduction to the properties of solids. Theory of free electrons--classical and quantum. Crystal structure and methods of determination. Electron energy levels in a crystal: weak potential and tight-binding limits. Classification of solids--metals, semiconductors, and insulators. Types of bonding and cohesion in crystals. Lattice dynamics, phonon spectra, and thermal properties of harmonic crystals. Prerequisite: 342, or PHY 208 and 305, or permission of instructor.

Electronic structure of solids. Electron dynamics and transport. Semiconductors and impurity states. Surfaces and interfaces. Dielectric properties of insulators. Electron-electron, electron-phonon, and phonon-phonon interactions. Anharmonic effects in crystals. Magnetism. Superconductivity. Alloys. Three hours of lectures. Prerequisites: 441 or equivalent.

Fundmentals of light-matter interactions, waveguides and resonators, nonlinear optics and lasers.

This class will teach students about optical and photonic sensing technologies and their applications to environmental monitoring. The course will contain elements of atmospheric science and Earth observation, fundamentals of optics, photonics and laser physics, as well as a survey of modern optical and spectroscopic sensing applications.

This course provides an introduction to the state-of-the-art in photonic technology and systems, focusing on high performance fiber-optic telecommunication systems of silicon photonics. The basic physical principles and performance characteristics of optical fibers, lasers, detectors, optical amplifiers and dispersion management will be discussed. The design and performance analysis of photonic systems will be presented.

Analysis and design of digital integrated circuits using deep sub-micron CMOS technologies as well as emerging and post-CMOS technologies (Si finFETs, III-V, carbon). Emphasis on design, including synthesis, simulation, layout and post-layout verification. Analysis of energy, power, performance, area of logic-gates, interconnect and signaling structures.

This course studies computer networks and the services built on top of them. Topics covered include the internet protocol, internet routing, routers, packet switching, network management, network monitoring, congestion control, reliable transport, network security, and applications of ML on networking. Through programming assignments, students will gain practical experience building network components and operating an Internet-like network infrastructure. Two lectures, one preceptorial. Prerequisite: 217.

An in-depth study of the fundamentals of modern computer processor and system architecture. Students will develop a strong theoretical and practical understanding of modern, cutting-edge computer architectures and implementations. Studied topics include: Instruction-set architecture and high-performance processor organization including pipelining, out-of-order execution, as well as data and instruction parallelism. Cache, memory, and storage architectures. Multiprocessors and multicore processors. Coherent caches. Interconnection and network infrastructures. Prerequisite: ECE 375/COS 375 and ECE 206/COS 306 (or familiarity with Verilog).

The lectures will cover: (1) Basic principles of digital signal processing. (2) Design of digital filters. (3) Fourier analysis and the fast Fourier transform. (4) Roundoff errors in digital signal processing. (5) Applications of digital signal processing.

An introduction to lossless data compression algorithms, modulation/demodulation of digital data, error correcting codes, channel capacity, lossy compression of analog and digital sources. Three hours of lectures. Prerequisites: 301, ORF 309.

We cover the world of digital imaging, from how digital cameras form images to how special effects are used in Hollywood movies and how the Mars Rover sends photographs across millions of miles of space. The course starts by looking at how the human visual system works and then teaches the engineering, mathematics, and CS that makes digital images work. We will learn algorithms used for adjusting images, explore JPEG and MPEG standards for encoding and compressing video images, and go on to learn about image segmentation, noise removal and filtering. We will end with image processing techniques used in medicine and special projects.

This hands-on course introduces students to analysis and actions required to launch and commercialize a tech company, through the use of Harvard Business School cases, visits from entrepreneurs, and two "field assignments". You will learn conceptual frameworks and analytical techniques for evaluating technologies, markets, and commercialization strategies. Additionally, you will learn how to attract and motivate the resources needed to start a company (e.g. people, corporate partners and venture capital), prepare business plans, structure relationships, refine product-market fit, and create and grow enterprise value.

The senior thesis (498-499) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member in science, engineering, or a technical field. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.

The senior thesis (498-499) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member in science, engineering, or a technical field. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.