Communication in the Presence of Noise

By Claude E. Shannon. 1949

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In Information Theory

Citations in the corpus

Rethinking Evaluation in ASR: Are Our Models Robust Enough? (2020, background): “For example, as SB+FSH has the lowest sample rate of 8kHz, filterbanks can span up to 4kHz and any spectrogram features beyond 4kHz can’t be determined accurately [20], as shown in Figure 1.”
Alias-Free Generative Adversarial Networks (2021, methodology): “According to the Nyquist–Shannon sampling theorem [49], a regularly sampled signal can represent any continuous signal containing frequencies between zero and half of the sampling rate.”
BigVGAN: A Universal Neural Vocoder with Large-Scale Training (2022, methodology): “2×) that approximates the continuous representation inspired by the Nyquist-Shannon sampling theorem [49].”
Spatio-temporal video autoencoder with differentiable memory (2015, neutral): “…could shed some light into the causes of some geometric optical illusions; e.g. when storing in memory an overly-downsampled version of the visual input, this could affect the capacity of correctly perceiving the visual stimuli, in accordance with Shannon-Nyquist sampling theorem Shannon (1949).”
Unsupervised Learning using Sequential Verification for Action Recognition (2016, background): “While theoretically, three frames are not sufficient to resolve cyclical ambiguity [67], we found that combining this with smart sampling (Section 3.”
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation (2021, methodology): “If spectral features of up to half of the sampling rate[20] are used as the input, full-band acoustic information can be provided to the model for the reconstruction of high-fidelity waveforms.”
A chronology of interpolation: from ancient astronomy to modern signal and image processing (2002, background): “…the now well-known sampling theorem , which he first published [110] without proof in 1948 and the subsequent year with full proof in a paper [111] apparently written already in 1940: “If a function contains no frequencies higher than cps [cycles per second], it is completely determined by…”
Mip-Splatting: Alias-Free 3D Gaussian Splatting (2023, methodology): “The Sampling Theorem, also known as the Nyquist-Shannon Sampling Theorem [33, 45], is a fundamental concept in signal processing and digital communication that describes the conditions under which a continuous signal can be accurately represented or reconstructed from its discrete samples.”
Quantitative Phase Imaging (2012, methodology): “According to the Nyquist sampling theorem (or Nyquist– Shannon theorem), the sampling frequency has to be at least twice the frequency of the signal of interest (Nyquist, 1928; Shannon, 1949).”
Survey on Free Space Optical Communication: A Communication Theory Perspective (2014, background): “The Shannon-Hartley theorem determines the (theoretical) maximum data rate that can be transmitted with an arbitrarily small BER over a channel for a given average signal power [166].”
Image Compressed Sensing Using Convolutional Neural Network (2020, methodology): “THE traditional image acquisition system usually first acquires a dense set of samples based on the Nyquist-Shannon sampling theorem [2], of which the sampling ratio is no less than twice the bandwidth of the signal, then compresses the signal to remove redundancy by a computationally complex compre”
Changing coasts: marine aliens and artificial structures (2012, background): “higher than the average probability of spotting a targeted organism in the FOV 467 (Nyquist-Shannon frequency; Shannon 1949).”
Human-Automation Interaction (2005, background): “This can result in what Woods has called automation surprises (Sarter, Woods, & Billlings, 1997) and lead the human to ask E. L. Wiener’s (1982) familiar questions: What is the automation doing now? Why is it doing that? What will it do next? Woods implies that increasing automation necessarily incr”
Feedback stabilization over signal-to-noise ratio constrained channels (2004, background): “Namely, for stabilization to be feasible, it is necessary and sufficient that the data rate R (bits per interval) satisfies the condition”
Super resolution fluorescence microscopy (2009, background): “The effect of labeling density on the effective resolution can be quantified by the Nyquist criterion, which states that structural features smaller than twice that of the fluorophore-tofluorophore distance cannot be reliably discerned (65,70).”