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1 - 3 of 3 results for: EE264

EE 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; spectrum analysis using the DFT; and LMS adaptive filtering. The course also covers applications of DSP in areas such as speech, audio and communication systems. The optional (1 extra credit hour) lab provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See ee264.stanford.edu for more information. The optional lab is not available to remote SCPD students. Prerequisites: EE 102A and EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Spr, Sum | Units: 3-4

EE 264W: Digital Signal Processing (WIM)

EE 264W: Digital Signal Processing (WIM)nWriting in the Major (WIM) version of the 4-unit EE 264 theory + lab/project course. This course also meets the EE design requirement. 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; spectrum analysis using the DFT; and LMS adaptive filtering. The course also covers applications of DSP in areas such as speech, audio and communication systems. The optional (1 extra credit hour) lab provides a hands-on opportunity to explore the application of DSP theory to practical real-time applications in an embedded processing platform. See ee264.stanford.edu for more information. The optional lab is not available to remote SCPD students. Prerequisite: EE 102A and EE 102B or equivalent, basic programming skills (Matlab and C++)
Terms: Spr | 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: Spr | Units: 3
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