Results 211 to 220 of about 18,005 (249)

Sub-Nyquist Sampling

IEEE Signal Processing Magazine, 2011
In this article, we review sampling strategies that target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 1950s through recent publications from the past several years. The prime focus is bridging theory and practice, that is, to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the ...
Yonina Eldar, Moshe Mishali
exaly   +2 more sources

Sub-Nyquist Sampling of Multiple Sinusoids

IEEE Signal Processing Letters, 2018
In this letter, we propose new sub-Nyquist sampling schemes for multiple sinusoids, which require fewer number of samples than previous works. Since it is impossible to resolve the frequency ambiguity using a single sub-Nyquist sample sequence, an additional sampling channel is used to determine the correct frequencies. First, a time-staggered sampling
Xiaodong Wang, Ning Fu, Xiaodong Wang
exaly   +2 more sources

Sub-Nyquist sampling with independent measurements

Signal Processing, 2020
Abstract This paper presents an efficient sub-Nyquist pulse-Doppler radar system via quadrature compressive sampling (QuadCS) in fast-time domain. QuadCS is a bandpass variant of random demodulation and inserts randomness into the measurement matrix by employing a random chipping signal.
Zhong Liu, Huizhang Yang, Feng Xi
exaly   +2 more sources

Compressive sensing and sub-Nyquist sampling

IEEE Instrumentation and Measurement Magazine, 2020
The Nyquist-Shannon sampling theorem states that if all that you know about a signal is that its highest frequency is no greater than B, then if you want to sample the signal without any loss of information, you must sample it at a rate that is greater than 2B.
Shlomo Engelberg
exaly   +2 more sources

Sub-NYQUIST Multichannel Blind Deconvolution

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
We consider a continuous-time sparse multichannel blind deconvolution problem. The signal at each channel is expressed as the convolution of a common source signal and its impulse response given as a sparse filter. The objective is to identify these sparse filters from sub-Nyquist samples of channel outputs by leveraging the correlation across channels.
Satish Mulleti   +2 more
openaire   +1 more source

Sub-Nyquist Interferometry

Workshop on Optical Fabrication and Testing, 1986
The primary limitation of conventional phase-shifting or heterodyne interferometry (PSI) is its inability to measure the profile of surfaces or wavefronts with large departures from a best-fit reference sphere. The surface must have limited asphericity.
openaire   +2 more sources

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