BIOE 313: Neuromorphics: Brains in Silicon (EE 207)
(Formerly
EE 304) Neuromorphic systems run perceptual, cognitive and motor tasks in realtime on a network of highly interconnected nonlinear units. To maximize density and minimize energy, these unitslike the brain's neuronsare heterogeneous and stochastic. The first half of the course covers learning algorithms that automatically synthesize network configurations to perform a desired computation on a given heterogeneous neural substrate. The second half of the course surveys systemonachip architectures that efficiently realize highly interconnected networks and mixed analogdigital circuit designs that implement area and energyefficient nonlinear units. Prerequisites: EE102A is required.
Terms: not given this year, last offered Spring 2018

Units: 3

Grading: Letter (ABCD/NP)
EE 101B: Circuits II
Continuation of
EE101A. Introduction to circuit design for modern electronic systems. Modeling and analysis of analog gain stages, frequency response, feedback. Filtering and analog¿to¿digital conversion. Fundamentals of circuit simulation. Prerequisites:
EE101A,
EE102A. Recommended:
CME102.
Terms: Spr

Units: 4

UG Reqs: GER:DBEngrAppSci, WAYSMA

Grading: Letter or Credit/No Credit
Instructors:
Murmann, B. (PI)
;
Wong, S. (PI)
EE 102A: Signal Processing and Linear Systems I
Concepts and tools for continuous and discretetime signal and system analysis with applications in signal processing, communications, and control. Mathematical representation of signals and systems. Linearity and time invariance. System impulse and step responses. System frequency response. Frequencydomain representations: Fourier series and Fourier transforms. Filtering and signal distortion. Time/frequency sampling and interpolation. Continuousdiscretetime signal conversion and quantization. Discretetime signal processing. Prerequisite:
MATH 53 or
CME 102.
Terms: Win, Sum

Units: 4

UG Reqs: GER:DBEngrAppSci, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Kahn, J. (PI)
EE 155: Green Electronics (EE 255)
Many green technologies including hybrid cars, photovoltaic energy systems, efficient power supplies, and energyconserving control systems have at their heart intelligent, highpower electronics. This course examines this technology and uses greentech examples to teach the engineering principles of modeling, optimization, analysis, simulation, and design. Topics include power converter topologies, periodic steadystate analysis, control, motors and drives, photovoltaic systems, and design of magnetic components. The course involves a handson laboratory and a substantial final project. Formerly
EE 152. Required:
EE101B,
EE102A,
EE108. Recommended: ENGR40 or
EE122A.
Terms: not given this year

Units: 4

Grading: Letter or Credit/No Credit
EE 169: Introduction to Bioimaging
Bioimaging is important for both clinical medicine, and medical research. This course will provide a introduction to several of the major imaging modalities, using a signal processing perspective. The course will start with an introduction to multidimensional Fourier transforms, and image quality metrics. It will then study projection imaging systems (projection XRay), backprojection based systems (CT, PET, and SPECT), systems that use beam forming (ultrasound), and systems that use Fourier encoding (MRI). Prerequisites:
EE102A,
EE102B
Terms: not given this year, last offered Autumn 2017

Units: 3

Grading: Letter or Credit/No Credit
EE 207: Neuromorphics: Brains in Silicon (BIOE 313)
(Formerly
EE 304) Neuromorphic systems run perceptual, cognitive and motor tasks in realtime on a network of highly interconnected nonlinear units. To maximize density and minimize energy, these unitslike the brain's neuronsare heterogeneous and stochastic. The first half of the course covers learning algorithms that automatically synthesize network configurations to perform a desired computation on a given heterogeneous neural substrate. The second half of the course surveys systemonachip architectures that efficiently realize highly interconnected networks and mixed analogdigital circuit designs that implement area and energyefficient nonlinear units. Prerequisites: EE102A is required.
Terms: not given this year, last offered Spring 2018

Units: 3

Grading: Letter (ABCD/NP)
EE 255: Green Electronics (EE 155)
Many green technologies including hybrid cars, photovoltaic energy systems, efficient power supplies, and energyconserving control systems have at their heart intelligent, highpower electronics. This course examines this technology and uses greentech examples to teach the engineering principles of modeling, optimization, analysis, simulation, and design. Topics include power converter topologies, periodic steadystate analysis, control, motors and drives, photovoltaic systems, and design of magnetic components. The course involves a handson laboratory and a substantial final project. Formerly
EE 152. Required:
EE101B,
EE102A,
EE108. Recommended: ENGR40 or
EE122A.
Terms: not given this year

Units: 4

Grading: Letter or Credit/No Credit
EE 278: Introduction to Statistical Signal Processing
Review of basic probability and random variables. Random vectors and processes; convergence and limit theorems; IID, independent increment, Markov, and Gaussian random processes; stationary random processes; autocorrelation and power spectral density; mean square error estimation, detection, and linear estimation. Formerly
EE 278B. Prerequisites: EE178 and linear systems and Fourier transforms at the level of
EE102A,B or
EE261.
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Prabhakar, B. (PI)
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