On acoustically modulated jet shear layers and the Nyquist–Shannon sampling theorem by Nicholls, CJ, Chakravarthy, K, Tang, BMT, Williams, BAO, Bacic, M

“…The main idea is that the vortices that roll-up in the jet shear layer are similar to the discrete samples of a digital control system, and, hence, that the Nyquist–Shannon sampling theorem should apply. We further hypothesize that the strength of a vortex is determined by the mean amplitude of the excitation waveform during its creation. …”

Compressive techniques for wideband frequency hopping signals by Lim, Bing Xian

“…Compressive Sensing (CS) is an emerging theory that has a lower rate of signal acquisition as compared to the renowned Nyquist-Shannon sampling theorem. “CS theory asserts that one can recover from far fewer samples or measurements than traditional method uses” [1] With a lower amount of measurements taken as compared to the Nyquist rate, the complete signal is then subsequently reconstructed. …”
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Study of sub-nyquist sampling and noise folding effect by Sugunthan, T.

“…Sub-Nyquist sampling, sampling below Nyquist rate, was introduced to reduce the computational load and storage space caused when adhering to the Nyquist-Shannon sampling theorem. Studies over the years resulted in different schemes to achieve sub-Nyquist sampling. …”
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A hardware platform to test analog-to-information conversion and non-uniform sampling by Perez, Miguel E., M. Eng. Massachusetts Institute of Technology

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Learning nonlocal sparse and low-rank models for image compressive sensing: nonlocal sparse and low-rank modeling by Zha, Zhiyuan, Wen, Bihan, Yuan, Xin, Ravishankar, Saiprasad, Zhou, Jiantao, Zhu, Ce

“…The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. …”
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