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Sub-NYQUIST Multichannel Blind Deconvolution
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021We 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.
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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 ...
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Compressive sensing and sub-Nyquist sampling
IEEE Instrumentation & Measurement Magazine, 2020The 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.
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Sub-Nyquist sampling with independent measurements
Signal Processing, 2020Abstract 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.
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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.
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Data-assisted sub-Nyquist spectrum sensing
2016 IEEE International Conference on Communication Systems (ICCS), 2016Spectrum sensing is the first step to realize dynamic spectrum sharing. Geo-location database can enable white space devices (WSDs) to make use of the vacant spectrum without harmfully interfering with incumbent services. Dynamic changes of the wireless environment pose significant challenges to the database approach.
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