printer friendly pageEE 264: Digital Signal Processing
Digital signal processing (DSP) techniques and design of DSP applications. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; and spectrum analysis using the DFT. In the design part of the course, students develop basic DSP applications on an embedded processing platform. The 4-unit version of the course, which meets the EE design requirement, adds a final 4-week project and report. See
ee264.stanford.edu for more information. Prerequisite:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win, Sum
| Units: 3-4
Instructors:
Krause Perin, J. (PI)
;
Mujica, F. (PI)
;
Schafer, R. (PI)
...
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Instructors:
Krause Perin, J. (PI)
;
Mujica, F. (PI)
;
Schafer, R. (PI)
;
Patel, A. (TA)
;
Suri, A. (TA)
EE 264W: Digital Signal Processing (WIM)
Writing in the Major (WIM) version of the 4-unit
EE 264 theory + lab/project course. This course also meets the EE design requirement. Digital signal processing (DSP) techniques and design of DSP applications. Topics include: discrete-time random signals; sampling and multi-rate systems; oversampling and quantization in A-to-D conversion; properties of LTI systems; quantization in fixed-point implementations of filters; digital filter design; discrete Fourier Transform and FFT; and spectrum analysis using the DFT. In the design part of the course, students complete basic labs and a 4 week final project and report. See
ee264.stanford.edu for more information. Prerequisite:
EE 102A and
EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Win
| Units: 5
EE 373A: Adaptive Signal Processing
Learning algorithms for adaptive digital filters. Self-optimization. Wiener filter theory. Quadratic performance functions, their eigenvectors and eigenvalues. Speed of convergence. Asymptotic performance versus convergence rate. Applications of adaptive filters to statistical prediction, process modeling, adaptive noise canceling, adaptive antenna arrays, adaptive inverse control, and equalization and echo canceling in modems. Artificial neural networks. Cognitive memory/human and machine. Natural and artificial synapses. Hebbian learning. The Hebbian-LMS algorithm. Theoretical and experimental research projects in adaptive filter theory, communications, audio systems, and neural networks. Biomedical research projects, supervised jointly by EE and Medical School faculty. Recommended:
EE263,
EE264,
EE278.
Terms: Win
| Units: 3
Instructors:
Widrow, B. (PI)
;
Krause Perin, J. (TA)
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