Theory of communication. Part 1: The analysis of information

By D. Gabor. 1946

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Citations in the corpus

Deep learning in neural networks: An overview (2014, background): “, 1996), such a s off-center-on-surround -like structures, as well as orientation sensitive edge detectors and Gabor filte rs (Gabor, 1946).”
Deep Networks with Internal Selective Attention through Feedback Connections (2014, background): “Low-level stages tend to learn biologically plausible feature detectors, such as Gabor filters [14].”
Sketch-a-Net: A Deep Neural Network that Beats Humans (2016, unknown): “8(right)) (Gabor, 1946).”
HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity (2013, methodology): “First, from the real-valued signals x(t) and y(t) , we obtain the corresponding analytic signals x an (t) and y an (t) (Gabor 1946), as: where x H (t) and y H (t) are the Hilbert transforms of x(t) and y(t) , respectively.”
Wavelet Networks: Scale Equivariant Learning From Raw Waveforms (2020, background): “The prime time-frequency transform is the Fourier transformF , in which f is described by a time-frequency dictionary of complex sinusoidal waves D = {eiωt}ω∈R: F [f ](ω) = f̂(ω) = 〈f, eiωt〉 = ∫ ∞ −∞ f(t) e−iωt dt. (2) Motivated by quantum mechanics, Gabor [22] proposed to decompose signals over dic”
Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning? (2013, background): “It is well known that the equality sign in the uncertainty principle above is achieved by Gabor functions[41] of the form The uncertainty principle leads to the concept of “optimal localization” instead of exact localization.”
A Survey on Multicarrier Communications: Prototype Filters, Lattice Structures, and Implementation Aspects (2012, background): “…the symbols may be important for the perfect reconstruction of the symbols since 3 its properties may lead one-to-one mapping from message space to the signal space [29], as in signaling over WeylHeisenberg frames, faster-than-Nyquist signaling, or partialresponse signaling [23], [24], [30], [31].”
Least-Squares Wavelet Analysis of Unequally Spaced and Non-stationary Time Series and Its Applications (2017, methodology): “In order to measure the frequency variation of sounds, Gabor (1946) introduced the short-time Fourier transform (STFT) to analyze piece-wise stationary and equally spaced time series.”
Texture Feature Extraction Methods: A Survey (2019, methodology): “To extract the Gabor features, the multi-channel Gabor filtering technique is used [145].”
Machine learning and computer vision approaches for phenotypic profiling (2017, background): “Texture features quantify spatial statistics of pixel intensities, including measurements from different channels (e.g., mean, sum, standard deviation) and low-level patterns such as edges and blobs (Gabor, 1946; Haralick, 1979).”
Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review (2019, methodology): “For time-frequency analysis, Gabor [4] introduced the STFT, also called a windowing technique, which maps the signal into a two-dimensional function of time and frequency.”
Quadratic Shrinkage for Large Covariance Matrices (2020, background): “This is a basic concept in signal processing that goes back to Gabor (1946), but we review all the necessary details in the main body of the paper.”