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51 - 60 of 65 results for: EE

EE 355: Imaging Radar and Applications (GEOPHYS 265)

Radar remote sensing, radar image characteristics, viewing geometry, range coding, synthetic aperture processing, correlation, range migration, range/Doppler algorithms, wave domain algorithms, polar algorithm, polarimetric processing, interferometric measurements. Applications: surfafe deformation, polarimetry and target discrimination, topographic mapping surface displacements, velocities of ice fields. Prerequisites: EE261. Recommended: EE254, EE278, EE279.
Terms: Win | Units: 3

EE 359: Wireless Communications

This course will cover advanced topics in wireless communications as well as current wireless system design. Topics include: an overview of current and future wireless systems; wireless channel models including path loss, shadowing, and statistical multipath channel models; fundamental capacity limits of wireless channels; digital modulation and its performance in fading and under intersymbol interference; techniques to combat fading including adaptive modulation and diversity; multiple antenna (MIMO) techniques to increase capacity and diversity, intersymbol interference including equalization, multicarrier modulation (OFDM), and spread spectrum; and multiuser system design, including multiple access techniques. Course is 3 units but can be taken for 4 units with an optional term project. Prerequisite: 279 or instructor consent.
Terms: Win | Units: 3-4
Instructors: Poon, A. (PI)

EE 364A: Convex Optimization I (CME 364A)

Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as EE263, basic probability.
Terms: Win, Sum | Units: 3

EE 364M: Mathematics of Convexity

This course covers the elegant mathematical underpinnings of convex optimization, with a focus on those analytic techniques central to the successes of the field. Topics include, but are not limited to, convex sets and functions, separation theorems, duality, set-valued analysis, and the mathematical insights central to the development of modern optimization methods. Pre- or co-requisite: EE364A, and mathematical analysis at the level of MATH171.
Terms: Win | Units: 1
Instructors: Duchi, J. (PI)

EE 367: Computational Imaging (CS 448I)

Digital photography and basic image processing, convolutional neural networks for image processing, denoising, deconvolution, single pixel imaging, inverse problems in imaging, proximal gradient methods, introduction to wave optics, time-of-flight imaging, end-to-end optimization of optics and imaging processing. Emphasis is on applied image processing and solving inverse problems using classic algorithms, formal optimization, and modern artificial intelligence techniques. Students learn to apply material by implementing and investigating image processing algorithms in Python. Term project. Recommended: EE261, EE263, EE278.
Terms: Win | Units: 3

EE 369A: Medical Imaging Systems I (BMP 269A)

Imaging internal structures within the body using high-energy radiation and ultrasound, studied from a systems viewpoint. Modalities covered: x-ray, computed tomography, nuclear medicine, and ultrasound. Review of linear signals and systems, Fourier transforms, random variables, and noise. Analysis of existing and proposed systems in terms of resolution, frequency response, detection sensitivity, noise, and potential for improved diagnosis. This course covers Fourier transform basics and serves as an alternative prerequisite to EE 261 for EE 369B. Prerequisite: EE 102A (undergraduate-level signals and systems) or similar.
Terms: Win | Units: 3

EE 370: Reinforcement Learning: Behaviors and Applications (MS&E 237B)

This course treats reinforcement learning, which addresses the design of agents to operate in environments where actions induce delayed consequences. Concepts generalize those arising in bandit learning, which is covered in EE277/MS&E 237A. The course covers principled and scalable approaches to realizing a range of intelligent learning behaviors. Topics include planning, credit assignment, and learning of models, value functions, and policies. Motivating examples will be drawn from generative artificial intelligence, web services, control, and finance. Prerequisites: EE277.
Terms: Win | Units: 3

EE 379A: Data Transmission Design

Data Transmission Design is the first of a two-quarter sequence (leading to EE379B) in MSEE communications depth sequence. Intended students are those interested in research or design of data transmission systems' lower layers. The course includes methods for transmission designs with and without coding and includes basic examples as well as their relationship to modern current/next-generation wireless and wireline transmission systems. The course also develops and uses information measures as generalizations of signal processing and minimum-mean-square-error estimation, developing design intution. Basic phase-locking and synchronization methods also appear. EE379B progresses to multidimensional modulation methods and their use in modern and next-generation multiuser MIMO networks, along with network-design strategies. Prerequisites: EE102B and EE278 (or equivalents). EE279 is helpful but not required.
Terms: Win | Units: 3
Instructors: Cioffi, J. (PI)

EE 380: Colloquium on Computer Systems

Live presentations of current research in the design, implementation, analysis, and applications of computer systems. Topics range over a wide range and are different every quarter. Topics may include fundamental science, mathematics, cryptography, device physics, integrated circuits, computer architecture, programming, programming languages, optimization, applications, simulation, graphics, social implications, venture capital, patent and copyright law, networks, computer security, and other topics of related to computer systems. May be repeated for credit.
Terms: Win, Sum | Units: 1 | Repeatable for credit

EE 382C: Interconnection Networks

The architecture and design of interconnection networks used to communicate from processor to memory, from processor to processor, and in switches and routers. Topics: network topology, routing methods, flow control, router microarchitecture, and performance analysis. Enrollment limited to 30. Prerequisite: 282.
Terms: Win | Units: 3
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