Relevant bibliographies by topics / Fractional Fourier entropy

Academic literature on the topic 'Fractional Fourier entropy'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Fractional Fourier entropy"

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Li, Shu-Nan, and Bing-Yang Cao. "On Entropic Framework Based on Standard and Fractional Phonon Boltzmann Transport Equations." Entropy 21, no. 2 (February 21, 2019): 204. http://dx.doi.org/10.3390/e21020204.

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Generalized expressions of the entropy and related concepts in non-Fourier heat conduction have attracted increasing attention in recent years. Based on standard and fractional phonon Boltzmann transport equations (BTEs), we study entropic functionals including entropy density, entropy flux and entropy production rate. Using the relaxation time approximation and power series expansion, macroscopic approximations are derived for these entropic concepts. For the standard BTE, our results can recover the entropic frameworks of classical irreversible thermodynamics (CIT) and extended irreversible thermodynamics (EIT) as if there exists a well-defined effective thermal conductivity. For the fractional BTEs corresponding to the generalized Cattaneo equation (GCE) class, the entropy flux and entropy production rate will deviate from the forms in CIT and EIT. In these cases, the entropy flux and entropy production rate will contain fractional-order operators, which reflect memory effects.
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Tao, Ran, Xudong Zhao, Wei Li, Heng-Chao Li, and Qian Du. "Hyperspectral Anomaly Detection by Fractional Fourier Entropy." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 12, no. 12 (December 2019): 4920–29. http://dx.doi.org/10.1109/jstars.2019.2940278.

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Yan, Yan. "Gingivitis detection by fractional Fourier entropy with optimization of hidden neurons." International Journal of Cognitive Computing in Engineering 1 (June 2020): 36–44. http://dx.doi.org/10.1016/j.ijcce.2020.09.003.

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Wang, Shuihua, Yudong Zhang, Xiaojun Yang, Ping Sun, Zhengchao Dong, Aijun Liu, and Ti-Fei Yuan. "Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy." Entropy 17, no. 12 (December 17, 2015): 8278–96. http://dx.doi.org/10.3390/e17127877.

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Zhang, Lili, Jiachen Ma, Baozhi Cheng, and Fang Lin. "Fractional Fourier Transform-Based Tensor RX for Hyperspectral Anomaly Detection." Remote Sensing 14, no. 3 (February 8, 2022): 797. http://dx.doi.org/10.3390/rs14030797.

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Anomaly targets in a hyperspectral image (HSI) are often multi-pixel, rather than single-pixel, objects. Therefore, algorithms using a test point vector may ignore the spatial characteristics of the test point. In addition, hyperspectral anomaly detection (AD) algorithms usually use original spectral signatures. In a fractional Fourier transform (FrFT), the signals in the fractional Fourier domain (FrFD) possess complementary characteristics of both the original reflectance spectrum and its Fourier transform. In this paper, a tensor RX (TRX) algorithm based on FrFT (FrFT-TRX) is proposed for hyperspectral AD. First, the fractional order of FrFT is selected by fractional Fourier entropy (FrFE) maximization. Then, the HSI is transformed into the FrFD by FrFT. Next, TRX is employed in the FrFD. Finally, according to the optimal spatial dimensions of the target and background tensors, the optimal AD result is achieved by adjusting the fractional order. TRX employs a test point tensor, making better use of the spatial characteristics of the test point. TRX in the FrFD exploits the complementary advantages of the intermediate domain to increase discrimination between the target and background. Six existing algorithms are used for comparison in order to verify the AD performance of the proposed FrFT-TRX over five real HSIs. The experimental results demonstrate the superiority of the proposed algorithm.
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NAGHSH, Mohammad Mahdi, and Mahmood MODARRES-HASHEMI. "ISAR Image Formation Based on Minimum Entropy Criterion and Fractional Fourier Transform." IEICE Transactions on Communications E92-B, no. 8 (2009): 2714–22. http://dx.doi.org/10.1587/transcom.e92.b.2714.

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Zhang, Yudong, Xiaojun Yang, Carlo Cattani, Ravipudi Rao, Shuihua Wang, and Preetha Phillips. "Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm." Entropy 18, no. 3 (February 27, 2016): 77. http://dx.doi.org/10.3390/e18030077.

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Lopes, António M., and J. A. Tenreiro Machado. "Application of Fractional Techniques in the Analysis of Forest Fires." International Journal of Nonlinear Sciences and Numerical Simulation 17, no. 7-8 (December 1, 2016): 381–90. http://dx.doi.org/10.1515/ijnsns-2016-0026.

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Abstract:In this paper we study the global behavior of forest fires (FFs) in the Continental United States for the period 1984–2013. The data are obtained from a public domain catalog maintained by the Monitoring Trends in Burn Severity project. First we adopt clustering analysis to reduce the information dimensionality. Then we adopt mathematical tools commonly used in the analysis of dynamical systems, namely fractal dimension, entropy and fractional Fourier transform. The fractional techniques unveil FF patterns embedded in the data.
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Wang, Shui-Hua, Hong Cheng, Preetha Phillips, and Yu-Dong Zhang. "Multiple Sclerosis Identification Based on Fractional Fourier Entropy and a Modified Jaya Algorithm." Entropy 20, no. 4 (April 5, 2018): 254. http://dx.doi.org/10.3390/e20040254.

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Guo, ZhiPeng, Yi Xin, and YiZhang Zhao. "Cancer classification using entropy analysis in fractional Fourier domain of gene expression profile." Biotechnology & Biotechnological Equipment 32, no. 4 (December 19, 2017): 1042–46. http://dx.doi.org/10.1080/13102818.2017.1413596.

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Book chapters on the topic "Fractional Fourier entropy"

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Yan, Yan, and Elijah Nguyen. "Gingivitis Detection by Fractional Fourier Entropy and Standard Genetic Algorithm." In Intelligent Computing Theories and Application, 585–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60799-9_53.

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Lu, Siyuan, and Liam O’Donnell. "Teeth Category Classification by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 267–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51103-6_23.

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Wang, Liying, and Zhiqiang Xu. "Hearing Loss Identification via Fractional Fourier Entropy and Direct Acyclic Graph Support Vector Machine." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 277–89. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51103-6_24.

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Conference papers on the topic "Fractional Fourier entropy"

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Vourdas, A. "Fractional Fourier transform for periodic functions." In The twentieth international workshop on bayesian inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381904.

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Tan, Yang, Zhihua Wang, Kun Qian, Bin Hu, Shiliang Zhao, Bjorn W. Schuller, and Yoshiharu Yamamoto. "Heart Sound Classification based on Fractional Fourier Transformation Entropy." In 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2022. http://dx.doi.org/10.1109/lifetech53646.2022.9754781.

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Chountasis, S. "Fractional Fourier transform on the phase-space plane." In The twentieth international workshop on bayesian inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381909.

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Yan, Yan. "Gingivitis detection by Fractional Fourier Entropy and Biogeography-based Optimization." In 2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC). IEEE, 2020. http://dx.doi.org/10.1109/ucc48980.2020.00051.

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Lu, Zhihai, Siyuan Lu, Shuihua Wang, Yujie Li, Yudong Zhang, and Huimin Lu. "A Fruit Sensing and Classification System by Fractional Fourier Entropy and Improved Hybrid Genetic Algorithm." In International Conference on Industrial Application Engineering 2017. The Institute of Industrial Applications Engineers, 2017. http://dx.doi.org/10.12792/iciae2017.053.

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