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First Digits’ Shannon Entropy [PDF]
Entropy, 2022 Related to the letters of an alphabet, entropy means the average number of binary digits required for the transmission of one character. Checking tables of statistical data, one finds that, in the first position of the numbers, the digits 1 to 9 occur ...
Welf Alfred Kreiner
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On Shannon entropy and its applications
Kuwait Journal of Science, 2023 A brief and intuitive introduction to Shannon entropy is presented, including some of its properties. The application of this measure is exemplified in two different contexts from what was in its genesis: biological diversity and an original study on ...
Paulo Saraiva
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Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location
Entropy, 2015 In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection.
Jianlin Wang
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Perceptual Complexity as Normalized Shannon Entropy [PDF]
EntropyComplexity is one of the most important variables in how the brain performs decision making based on esthetic values. Multiple definitions of perceptual complexity have been proposed, with one of the most fruitful being the Normalized Shannon Entropy one.
Norberto M. Grzywacz
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Shannon Entropy Loss in Mixed-Radix Conversions [PDF]
Entropy, 2021 This paper models a translation for base-2 pseudorandom number generators (PRNGs) to mixed-radix uses such as card shuffling. In particular, we explore a shuffler algorithm that relies on a sequence of uniformly distributed random inputs from a mixed ...
Amy Vennos, Alan Michaels
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Perceived Complexity as Normalized, Integrated, Localized Shannon Entropy [PDF]
EntropyPerceived complexity is a key component of sensory brain function as it indicates the number of resources necessary to process incoming information. A recently proposed measure of perceived complexity defined it as normalized Shannon entropy.
Sébastien Berquet, Norberto M. Grzywacz
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Application of Positional Entropy to Fast Shannon Entropy Estimation for Samples of Digital Signals
Entropy, 2020 This paper introduces a new method of estimating Shannon entropy. The proposed method can be successfully used for large data samples and enables fast computations to rank the data samples according to their Shannon entropy.
Bartłomiej Płaczek
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Spatial distribution of the Shannon entropy for mass spectrometry imaging [PDF]
PLoS ONE, 2023 Mass spectrometry imaging (MSI) allows us to visualize the spatial distribution of molecular components in a sample. A large amount of mass spectrometry data comprehensively provides molecular distributions.
Lili Xu +13 more
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From Chaos to Ordering: New Studies in the Shannon Entropy of 2D Patterns [PDF]
Entropy, 2022 Properties of the Voronoi tessellations arising from random 2D distribution points are reported. We applied an iterative procedure to the Voronoi diagrams generated by a set of points randomly placed on the plane. The procedure implied dividing the edges
Irina Legchenkova +5 more
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Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties [PDF]
Journal of Cheminformatics, 2023 Accurate prediction of molecular properties is essential in the screening and development of drug molecules and other functional materials. Traditionally, property-specific molecular descriptors are used in machine learning models.
Rajarshi Guha, Darrell Velegol
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