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Journal articles on the topic 'Optimal thresholding'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Liu, Haoyang, and Rina Foygel Barber. "Between hard and soft thresholding: optimal iterative thresholding algorithms." Information and Inference: A Journal of the IMA 9, no. 4 (2019): 899–933. http://dx.doi.org/10.1093/imaiai/iaz027.

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Abstract Iterative thresholding algorithms seek to optimize a differentiable objective function over a sparsity or rank constraint by alternating between gradient steps that reduce the objective and thresholding steps that enforce the constraint. This work examines the choice of the thresholding operator and asks whether it is possible to achieve stronger guarantees than what is possible with hard thresholding. We develop the notion of relative concavity of a thresholding operator, a quantity that characterizes the worst-case convergence performance of any thresholding operator on the target o

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2

Moradi, Hassan, Hazem H. Refai, and Peter G. LoPresti. "Thresholding-Based Optimal Detection of Wireless Optical Signals." Journal of Optical Communications and Networking 2, no. 9 (2010): 689. http://dx.doi.org/10.1364/jocn.2.000689.

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3

Belkasim, S., A. Ghazal, and O. A. Basir. "Phase-based optimal image thresholding." Digital Signal Processing 13, no. 4 (2003): 636–55. http://dx.doi.org/10.1016/s1051-2004(02)00032-5.

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4

Snyder, Wesley, Griff Bilbro, Ambalavaner Logenthiran, and Sarah Rajala. "Optimal thresholding—A new approach." Pattern Recognition Letters 11, no. 12 (1990): 803–9. http://dx.doi.org/10.1016/0167-8655(90)90034-y.

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5

Yu, Yuncai, Xinsheng Liu, Ling Liu, and Weisi Liu. "On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise." Mathematica Slovaca 69, no. 6 (2019): 1485–500. http://dx.doi.org/10.1515/ms-2017-0324.

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Abstract This paper considers the nonparametric regression model with negatively super-additive dependent (NSD) noise and investigates the convergence rates of thresholding estimators. It is shown that the term-by-term thresholding estimator achieves nearly optimal and the block thresholding estimator attains optimal (or nearly optimal) convergence rates over Besov spaces. Additionally, some numerical simulations are implemented to substantiate the validity and adaptivity of the thresholding estimators with the presence of NSD noise.

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6

Lin, Zhengchun, Zhiyan Wang, and Yanqing Zhang. "Optimal Evolution Algorithm for Image Thresholding." Journal of Computer-Aided Design & Computer Graphics 22, no. 7 (2010): 1201–6. http://dx.doi.org/10.3724/sp.j.1089.2010.10907.

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7

Wu, Tao, Rui Hou, and Yixiang Chen. "Cloud Model-Based Method for Infrared Image Thresholding." Mathematical Problems in Engineering 2016 (2016): 1–18. http://dx.doi.org/10.1155/2016/1571795.

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Traditional statistical thresholding methods, directly constructing the optimal threshold criterion using the class variance, have certain versatility but lack the specificity of practical application in some cases. To select the optimal threshold for infrared image thresholding, a simple and efficient method based on cloud model is proposed. The method firstly generates the cloud models corresponding to image background and object, respectively, and defines a novel threshold dependence criterion related with the hyper-entropy of these cloud models and then determines the optimal grayscale thr

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8

Li, Kun, Na Yang, Jian Wang, Yan Han, Peng-Fei Nie, and Min Zhang. "Size Projection Algorithm: Optimal Thresholding Value Selection for Image Segmentation of Electrical Impedance Tomography." Mathematical Problems in Engineering 2019 (October 20, 2019): 1–11. http://dx.doi.org/10.1155/2019/1368010.

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Thresholding is an efficient step to extract quantitative information since the potential artefacts are often introduced by the point-spread effect of tomographic imaging. The thresholding value was previously selected only relying on engineering experience or histogram of tomographic image, which often presents a great challenge to determine an accurate thresholding value for various applications. As the tomographic image features often do not provide sufficient information to choose the best thresholding value, the information implicit in the measured boundary data is introduced into the thr

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9

Kamilov, Ulugbek S., and Hassan Mansour. "Learning Optimal Nonlinearities for Iterative Thresholding Algorithms." IEEE Signal Processing Letters 23, no. 5 (2016): 747–51. http://dx.doi.org/10.1109/lsp.2016.2548245.

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10

Hyun, Baro, Pierre Kabamba, and Anouck Girard. "Optimal Classification by Mixed-Initiative Nested Thresholding." IEEE Transactions on Cybernetics 45, no. 1 (2015): 29–39. http://dx.doi.org/10.1109/tcyb.2014.2317672.

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11

Eichmann, Marco. "Framework for efficient optimal multilevel image thresholding." Journal of Electronic Imaging 18, no. 1 (2009): 013004. http://dx.doi.org/10.1117/1.3073891.

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12

Peng, Hong, Jun Wang, and Mario J. Pérez-Jiménez. "Optimal multi-level thresholding with membrane computing." Digital Signal Processing 37 (February 2015): 53–64. http://dx.doi.org/10.1016/j.dsp.2014.10.006.

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13

Sathya, P. D., and R. Kayalvizhi. "Optimal multilevel thresholding using bacterial foraging algorithm." Expert Systems with Applications 38, no. 12 (2011): 15549–64. http://dx.doi.org/10.1016/j.eswa.2011.06.004.

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14

He, Lifang, and Songwei Huang. "Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem." Mathematical Problems in Engineering 2016 (2016): 1–24. http://dx.doi.org/10.1155/2016/3196958.

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The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO) algorithm has been presented to find the optimal multilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The propos

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15

Zahara, Erwie, Shu-Kai S. Fan, and Du-Ming Tsai. "Optimal multi-thresholding using a hybrid optimization approach." Pattern Recognition Letters 26, no. 8 (2005): 1082–95. http://dx.doi.org/10.1016/j.patrec.2004.10.003.

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16

Zhao, Yun-Bin. "Optimal $k$-Thresholding Algorithms for Sparse Optimization Problems." SIAM Journal on Optimization 30, no. 1 (2020): 31–55. http://dx.doi.org/10.1137/18m1219187.

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17

Shark, L. K., and C. Yu. "Denoising by optimal fuzzy thresholding in wavelet domain." Electronics Letters 36, no. 6 (2000): 581. http://dx.doi.org/10.1049/el:20000451.

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18

Ye, Zhi-Wei, Ming-Wei Wang, Wei Liu, and Shao-Bin Chen. "Fuzzy entropy based optimal thresholding using bat algorithm." Applied Soft Computing 31 (June 2015): 381–95. http://dx.doi.org/10.1016/j.asoc.2015.02.012.

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19

Phanendra Babu, G., and M. Narasimha Murty. "Optimal thresholding using multi-state stochastic connectionist approach." Pattern Recognition Letters 16, no. 1 (1995): 11–18. http://dx.doi.org/10.1016/0167-8655(94)00062-8.

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20

Zhao, Yun-Bin, and Zhi-Quan Luo. "Analysis of optimal thresholding algorithms for compressed sensing." Signal Processing 187 (October 2021): 108148. http://dx.doi.org/10.1016/j.sigpro.2021.108148.

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21

Wang, Xiangluo, Chunlei Yang, Guo-Sen Xie, and Zhonghua Liu. "Image Thresholding Segmentation on Quantum State Space." Entropy 20, no. 10 (2018): 728. http://dx.doi.org/10.3390/e20100728.

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Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searchi

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22

Wang, Yi, and Kangshun Li. "A Fuzzy Adaptive Firefly Algorithm for Multilevel Color Image Thresholding Based on Fuzzy Entropy." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (2021): 1–20. http://dx.doi.org/10.4018/ijcini.20211001.oa44.

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Multilevel thresholding image segmentation has always been a hot issue of research in last several years since it has a plenty of applications. Traditional exhaustive search method consumes a lot of time for searching the optimal multilevel thresholding, color images contain more information, solving multilevel thresholding will become worse. However, the meta-heuristic search algorithm has unique advantages in solving multilevel threshold values. In this paper, a fuzzy adaptive firefly algorithm (FaFA) is proposed to solve the optimal multilevel thresholding for color images, and the fuzzy Ka

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23

Yang, Guifeng, Jiulun Fan, and Dong Wang. "Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram." Mathematical Problems in Engineering 2021 (March 24, 2021): 1–13. http://dx.doi.org/10.1155/2021/6653031.

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Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria,

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24

KANATANI, KENICHI, and YASUSHI KANAZAWA. "AUTOMATIC THRESHOLDING FOR CORRESPONDENCE DETECTION." International Journal of Image and Graphics 04, no. 01 (2004): 21–33. http://dx.doi.org/10.1142/s0219467804001270.

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We study the problem of thresholding the residual of template matching as a preprocess for selecting the correct matches between feature points in two images. In order to determine the threshold dynamically, we introduce a statistical model of the residual and compute an optimal threshold according to that model. The model parameters are estimated from the histogram of the residuals of candidate matches. Using real images, we show that our method can substantially upgrade the quality of the initial matches by simply adjusting the threshold.

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25

Singh, V. P., Tapan Prakash, Natwar Singh Rathore, Dharma Pal Singh Chauhan, and Sugandh P. Singh. "Multilevel Thresholding with Membrane Computing Inspired TLBO." International Journal on Artificial Intelligence Tools 25, no. 06 (2016): 1650030. http://dx.doi.org/10.1142/s0218213016500305.

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The selection of optimal thresholds is still a challenging task for researchers in case of multilevel thresholding. Many swarm and evolutionary computation techniques have been applied for obtaining optimal values of thresholds. The performance of all these computation techniques is highly dependent on proper selection of algorithm-specific parameters. In this work, a new hybrid optimization technique, membrane computing inspired teacher-learner-based-optimization (MCTLBO), is proposed which is based on the structure of membrane computing (MC) and teacher-learner-based-optimization (TLBO) algo

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26

Yin, Jun, Yi Quan Wu, and Li Zhu. "Multi-Thresholding Based on Symmetric Tsallis-Cross Entropy and Particle Swarm Optimization." Advanced Materials Research 760-762 (September 2013): 1457–61. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1457.

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Multi-thresholding is an important step for automatic image analysis. In this paper, a multi-thresholding method based on symmetric Tsallis-cross entropy and uniform searching particle swarm optimization (UPSO) is proposed. The criterion function using symmetric Tsallis-cross entropy can make the grayscale within the background cluster and the object cluster uniform. Since the exhaustive multi-thresholding algorithm would be too time-consuming, UPSO algorithm is adopted to find the optimal thresholds quickly and accurately. A large number of experimental results show that, compared with relate

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27

U, Sesadri, Siva Sankar B, and Nagaraju C. "Fuzzy Entropy Based Optimal Thresholding Technique for Image Enhancement." International Journal on Soft Computing 6, no. 2 (2015): 17–26. http://dx.doi.org/10.5121/ijsc.2015.6202.

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28

Yin, Peng-Yeng. "A fast scheme for optimal thresholding using genetic algorithms." Signal Processing 72, no. 2 (1999): 85–95. http://dx.doi.org/10.1016/s0165-1684(98)00167-4.

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29

Rambeaux, F., F. Hamelin, and D. Sauter. "Optimal thresholding for robust fault detection of uncertain systems." International Journal of Robust and Nonlinear Control 10, no. 14 (2000): 1155–73. http://dx.doi.org/10.1002/1099-1239(20001215)10:14<1155::aid-rnc521>3.0.co;2-v.

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30

Liang, Yun-Chia, and Yueh-Chuan Yin. "Optimal multilevel thresholding using a hybrid ant colony system." Journal of the Chinese Institute of Industrial Engineers 28, no. 1 (2011): 20–33. http://dx.doi.org/10.1080/10170669.2010.531771.

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31

Fan, Chaodong, Honglin Ouyang, Yingjie Zhang, and Leyi Xiao. "Optimal multilevel thresholding using molecular kinetic theory optimization algorithm." Applied Mathematics and Computation 239 (July 2014): 391–408. http://dx.doi.org/10.1016/j.amc.2014.04.103.

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32

Mlakar, Uroš, Božidar Potočnik, and Janez Brest. "A hybrid differential evolution for optimal multilevel image thresholding." Expert Systems with Applications 65 (December 2016): 221–32. http://dx.doi.org/10.1016/j.eswa.2016.08.046.

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33

Sri Madhava Raja, N., V. Rajinikanth, and K. Latha. "Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm." Modelling and Simulation in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/794574.

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Histogram based multilevel thresholding approach is proposed using Brownian distribution (BD) guided firefly algorithm (FA). A bounded search technique is also presented to improve the optimization accuracy with lesser search iterations. Otsu’s between-class variance function is maximized to obtain optimal threshold level for gray scale images. The performances of the proposed algorithm are demonstrated by considering twelve benchmark images and are compared with the existing FA algorithms such as Lévy flight (LF) guided FA and random operator guided FA. The performance assessment comparison b

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34

bonga, Siya, and Shi ra. "Separation from Brain Magnetic Resonance images (MRI) using Multistage Thresholding Technique." International Journal of Pharmacy and Biomedical Engineering 2, no. 3 (2015): 9–12. http://dx.doi.org/10.14445/23942576/ijpbe-v2i3p103.

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Image separation is a significant task concerned in dissimilar areas from image dispensation to picture examination. One of the simplest methods for image segmentation is thresholding. Though, many thresholding methods are based on a bi-level thresholding process. These methods can be extensive to form multi-level thresholding, but they become computationally expensive since a large number of iterations would be necessary for computing the most select threshold values. In order to conquer this difficulty, a new process based on a Shrinking Search Space (3S) algorithm is proposed in this paper.

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35

Yimit, Adiljan, and Yoshihiro Hagihara. "2D Direction Histogram-Based Rényi Entropic Multilevel Thresholding." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 3 (2018): 369–79. http://dx.doi.org/10.20965/jaciii.2018.p0369.

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2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Rényi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Rényi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrat

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36

Qin, Jun, ChuTing Wang, and GuiHe Qin. "A Multilevel Image Thresholding Method Based on Subspace Elimination Optimization." Mathematical Problems in Engineering 2019 (June 25, 2019): 1–11. http://dx.doi.org/10.1155/2019/6706590.

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Multilevel thresholding is to find the thresholds to segment the image with grey levels. Usually, the thresholds are so determined that some indicator functions of the segmented image are optimized. To improve the computational efficiency, we presented an optimization method for multilevel thresholding. First, the solution space is divided into subspaces. Second, the subspaces are searched to obtain their current local optimal value. Third, the subspaces that are of worse current optimal value are eliminated. Then, the next round of elimination is exerted in the remainder. The elimination is r

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37

ZHANG, LEI, XIAOLIN WU, and PAUL BAO. "NOISY SIGNAL COMPRESSION BY WAVELET TRANSFORM WITH OPTIMAL DOWNSAMPLING." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (2003): 407–23. http://dx.doi.org/10.1142/s0219691303000268.

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This paper presents a wavelet transform (WT) based on simultaneous de-noising and compression scheme for noisy signal. Due to the downsampling in decomposition process, the orthogonal wavelet transform (OWT) is translation variant, which significantly hinders its performance in coding and denoising. In this paper the wavelet bintree decomposition (WBD), which is equivalent to a translation invariant WT, is first formed and an optimal downsampling route is then traversed among all the routes of the bintree. The WT with the optimal route would most effectively decorrelate and compactly represent

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38

Sysoev, Evgeny V., and Rodion V. Kulikov. "Microrelief Measurements for White-Light Interferometer with Adaptive Algorithm Interferogram Processing." Key Engineering Materials 437 (May 2010): 35–39. http://dx.doi.org/10.4028/www.scientific.net/kem.437.35.

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The problem of reducing measurement errors of surface microrelief with nonuniform scattering properties using white-light interferometer was discussed. Adaptive algorithm of thresholding which allows us to set optimal threshold in every point of measuring surface was proposed. Application of this algorithm of threshold choice allows increasing dynamical range of interference detection more than in 10 times. The dependences of scanning interferometer resolution on differentional interferogram thresholding have been discussed.

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39

Wonohadidjojo, Daniel Martomanggolo. "Performance Comparison of Firefly and Cuckoo Search Algorithms in Optimal Thresholding of Cancer Cell Images." ComTech: Computer, Mathematics and Engineering Applications 10, no. 1 (2019): 29. http://dx.doi.org/10.21512/comtech.v10i1.5632.

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This research presented a performance comparison of the two methods in cancer cells image processing. Each method consisted of two stages. The first stage was image enhancement using fuzzy sets. The second stage was optimal fuzzy entropy based image thresholding. In the thresholding stage, the first method used Firefly Algorithm (FA) and the second used Cuckoo Search (CS). In both methods, four performance metrics (Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structured Similarity Indexing Method (SSIM), and Feature Similarity Indexing Method (FSIM)) and variance and entropy of

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40

Zhu, Xunzhi. "Approximately Normalized Iterative Hard Thresholding for Nonlinear Compressive Sensing." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2594752.

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The nonlinear compressive sensing (NCS) is an extension of classical compressive sensing (CS) and the iterative hard thresholding (IHT) algorithm is a popular greedy-type method for solving CS. The normalized iterative hard thresholding (NIHT) is a modification of IHT and is more effective than IHT. In this paper, we propose an approximately normalized iterative hard thresholding (ANIHT) algorithm for NCS by using the approximate optimal stepsize combining with Armijo stepsize rule preiteration. Under the condition similar to restricted isometry property (RIP), we analyze the condition that ca

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41

Yi Liu, Caihong Mu, and Weidong Kou. "Optimal Multilevel Thresholding Using the Modified Adaptive Particle Swarm Optimization." International Journal of Digital Content Technology and its Applications 6, no. 15 (2012): 208–19. http://dx.doi.org/10.4156/jdcta.vol6.issue15.25.

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42

Agrawal, Sanjay, Rutuparna Panda, Sudipta Bhuyan, and B. K. Panigrahi. "Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm." Swarm and Evolutionary Computation 11 (August 2013): 16–30. http://dx.doi.org/10.1016/j.swevo.2013.02.001.

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43

Hai, Nguyen Cao Truong, Do-Yeon Kim, and Hyuk-Ro Park. "Obtaining Object by Using Optimal Threshold for Saliency Map Thresholding." Journal of the Korea Contents Association 11, no. 6 (2011): 18–25. http://dx.doi.org/10.5392/jkca.2011.11.6.018.

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44

Padmanabhan, Krishnan, William F. Eddy, and Justin C. Crowley. "A novel algorithm for optimal image thresholding of biological data." Journal of Neuroscience Methods 193, no. 2 (2010): 380–84. http://dx.doi.org/10.1016/j.jneumeth.2010.08.031.

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45

Rojas, Dario, Luis Rueda, Alioune Ngom, Homero Hurrutia, and Gerardo Carcamo. "Image segmentation of biofilm structures using optimal multi-level thresholding." International Journal of Data Mining and Bioinformatics 5, no. 3 (2011): 266. http://dx.doi.org/10.1504/ijdmb.2011.040384.

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Bhandari, Ashish Kumar, Neha Singh, and Swapnil Shubham. "An efficient optimal multilevel image thresholding with electromagnetism-like mechanism." Multimedia Tools and Applications 78, no. 24 (2019): 35733–88. http://dx.doi.org/10.1007/s11042-019-08195-8.

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47

Charansiriphaisan, Kanjana, Sirapat Chiewchanwattana, and Khamron Sunat. "A Global Multilevel Thresholding Using Differential Evolution Approach." Mathematical Problems in Engineering 2014 (2014): 1–23. http://dx.doi.org/10.1155/2014/974024.

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Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images a

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48

Khairuzzaman, Abdul Kayom Md, and Saurabh Chaudhury. "Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation." International Journal of Swarm Intelligence Research 11, no. 4 (2020): 123–39. http://dx.doi.org/10.4018/ijsir.2020100106.

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Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the propos

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49

Pickerling, Pickerling, Hendrawan Armanto, and Stefanus Kurniawan Bastari. "Multilevel Image Thresholding Memanfaatkan Firefly Algorithm, Improved Bat Algorithm, dan Symbiotic Organisms Search." Journal of Intelligent System and Computation 1, no. 1 (2019): 1–8. http://dx.doi.org/10.52985/insyst.v1i1.24.

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Multilevel image thresholding adalah teknik penting dalam pemrosesan gambar yang digunakan sebagai dasar image segmentation dan teknik pemrosesan tingkat tinggi lainnya. Akan tetapi, waktu yang dibutuhkan untuk pencarian bertambah secara eksponensial setara dengan banyaknya threshold yang diinginkan. Algoritma metaheuristic dikenal sebagai metode optimal untuk memecahkan masalah perhitungan yang rumit. Seiring dengan berkembangnya algoritma metaheuristic untuk memecahkan masalah perhitungan, penelitian ini menggunakan tiga algoritma metaheuristic, yaitu Firefly Algorithm (FA), Symbiotic Organi

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50

Phanindra Kumar N.S.R. and Prasad Reddy P.V.G.D. "Evolutionary Image Thresholding for Image Segmentation." International Journal of Computer Vision and Image Processing 9, no. 1 (2019): 17–34. http://dx.doi.org/10.4018/ijcvip.2019010102.

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Image segmentation is a method of segregating the image into required segments/regions. Image thresholding being a simple and effective technique, mostly used for image segmentation, these thresholds are optimized by optimization techniques by maximizing the Tsallis entropy. However, as the two level thresholding extends to multi-level thresholding, the computational complexity of the algorithm is further increased. So there is need of evolutionary and swarm optimization techniques. In this article, first time optimal thresholds are obtained by maximizing the Tsallis entropy by using novel hyb

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