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Noise-shaping Quantization for Compressed Sensing
Thang Huynh
UCSD
Abstract:
Compressed sensing or compressive sampling (CS) is a signal processing technique for efficiently acquiring and reconstructing sparse signals by solving underdetermined linear systems. In practice, CS needs to be accompanied by a quantization process. That is, after sampling the signals, we represent the measurements using discrete data, e.g. 0s and 1s, and recover the signals from the quantized measurements. In this talk, I will discuss how to extend the noise-shaping quantization methods beyond the case of Gaussian measurements to structured random measurements, including random partial Fourier and random partial Circulant measurements. This is joint work with Rayan Saab.
Tuesday, May 23, 2017
11:00AM AP&M 2402
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Thang Huynh
UCSD
Abstract:
Compressed sensing or compressive sampling (CS) is a signal processing technique for efficiently acquiring and reconstructing sparse signals by solving underdetermined linear systems. In practice, CS needs to be accompanied by a quantization process. That is, after sampling the signals, we represent the measurements using discrete data, e.g. 0s and 1s, and recover the signals from the quantized measurements. In this talk, I will discuss how to extend the noise-shaping quantization methods beyond the case of Gaussian measurements to structured random measurements, including random partial Fourier and random partial Circulant measurements. This is joint work with Rayan Saab.
Tuesday, May 23, 2017
11:00AM AP&M 2402