BIOE 313: Neuromorphics: Brains in Silicon (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 and EE108 are required; EE114 is recommended.
Terms: Spr

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Boahen, K. (PI)
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)
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:
Pauly, J. (PI)
;
Aryan, O. (TA)
;
Delgado, D. (TA)
;
Gibbons, E. (TA)
...
more instructors for EE 102A »
Instructors:
Pauly, J. (PI)
;
Aryan, O. (TA)
;
Delgado, D. (TA)
;
Gibbons, E. (TA)
;
Guignard, L. (TA)
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: Aut

Units: 4

Grading: Letter (ABCD/NP)
Instructors:
Dally, B. (PI)
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: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Nishimura, D. (PI)
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: Aut

Units: 4

Grading: Letter (ABCD/NP)
Instructors:
Dally, B. (PI)
EE 264: Digital Signal Processing
This is a course on digital signal processing techniques and their applications. Topics include: review of DSP fundamentals; discretetime random signals; sampling and multirate systems; oversampling and quantization in AtoD conversion; properties of LTI systems; quantization in fixedpoint implementations of filters; digital filter design; discrete Fourier Transform and FFT; spectrum analysis using the DFT; and parametric signal modeling. The course will also discuss applications of DSP in areas such as speech and audio processing, autonomous vehicles, and software radio. An optional (1 extra credit hour) lab will provide a handson opportunity to explore the application of DSP theory to practical realtime applications. For more information, see the course web page at
ee264.stanford.edu. Prerequisite: EE102A and EE102B or equivalent.
Terms: Win, Sum

Units: 34

Grading: Letter or Credit/No Credit
Instructors:
Mujica, F. (PI)
;
Schafer, R. (PI)
;
Gonzalez, A. (TA)
;
Kong, T. (TA)
...
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Instructors:
Mujica, F. (PI)
;
Schafer, R. (PI)
;
Gonzalez, A. (TA)
;
Kong, T. (TA)
;
Sharma, P. (TA)
;
Verma, P. (TA)
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, Spr, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
El Gamal, A. (PI)
;
Prabhakar, B. (PI)
EE 304: Neuromorphics: Brains in Silicon (BIOE 313)
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 and EE108 are required; EE114 is recommended.
Terms: Spr

Units: 3

Grading: Letter (ABCD/NP)
Instructors:
Boahen, K. (PI)
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