Relevant bibliographies by topics / Entropy algorithms

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  2. Dissertations / Theses
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Academic literature on the topic 'Entropy algorithms'

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Published: 17 May 2025

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Journal articles on the topic "Entropy algorithms"

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Li, Yancang, and Wanqing Li. "Adaptive Ant Colony Optimization Algorithm Based on Information Entropy: Foundation and Application." Fundamenta Informaticae 77, no. 3 (January 2007): 229–42. https://doi.org/10.3233/fun-2007-77303.

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In order to solve the premature convergence problem of the basic Ant Colony Optimization algorithm, a promising modification based on the information entropy is proposed. The main idea is to evaluate stability of the current space of represented solutions using information entropy, which is then applied to turning of the algorithm's parameters. The path selection and evolutional strategy are controlled by the information entropy self-adaptively. Simulation study and performance comparison with other Ant Colony Optimization algorithms and other meta-heuristics on Traveling Salesman Problem show that the improved algorithm, with high efficiency and robustness, appears self -adaptive and can converge at the global optimum with a high probability. The work proposes a more general approach to evolutionary-adaptive algorithms related to the population's entropy and has significance in theory and practice for solving the combinatorial optimization problems.
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Turlykozhayeva, D. A. "ROUTING METRIC AND PROTOCOL FOR WIRELESS MESH NETWORK BASED ON INFORMATION ENTROPY THEORY." Eurasian Physical Technical Journal 20, no. 4 (46) (December 19, 2023): 90–98. http://dx.doi.org/10.31489/2023no4/90-98.

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In this work, the authors propose a routing algorithm based on information entropy theory for calculating the metric, considering theprobability of packet loss. Information entropy theory serves as a robust foundation for evaluating uncertainty and disorder in data transmission, facilitating the development of a more resilient and intelligent routing strategy. In contrast to existing algorithms, the proposed approach enables a more accurate assessment of data transmission quality within the network, optimizing the routing process for maximum efficiency. The experimental results demonstrate a significant enhancement in network service quality while maintaining high performance. To validate the algorithm's effectiveness, a series of experiments were conducted, evaluating key performance metrics such as throughput, delay, and packet loss. A comparative analysis with established routing algorithms was also carried out, allowing for the assessment of advantages and drawbacks in relation to well-known algorithms. The findings suggest that the proposed algorithm surpasses traditional routing methods in optimizing data transmission quality and overall network efficiency.
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Zhang, Chuang, Yue-Han Pei, Xiao-Xue Wang, Hong-Yu Hou, and Li-Hua Fu. "Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm." PLOS ONE 18, no. 6 (June 29, 2023): e0287573. http://dx.doi.org/10.1371/journal.pone.0287573.

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To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm’s ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.
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Manis, George, Md Aktaruzzaman, and Roberto Sassi. "Low Computational Cost for Sample Entropy." Entropy 20, no. 1 (January 13, 2018): 61. http://dx.doi.org/10.3390/e20010061.

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Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in m dimensional space. In this paper, we propose new algorithms or extend already proposed ones, aiming to compute Sample Entropy quickly. All algorithms return exactly the same value for Sample Entropy, and no approximation techniques are used. We compare and evaluate them using cardiac inter-beat (RR) time series. We investigate three algorithms. The first one is an extension of the k d -trees algorithm, customized for Sample Entropy. The second one is an extension of an algorithm initially proposed for Approximate Entropy, again customized for Sample Entropy, but also improved to present even faster results. The last one is a completely new algorithm, presenting the fastest execution times for specific values of m, r, time series length, and signal characteristics. These algorithms are compared with the straightforward implementation, directly resulting from the definition of Sample Entropy, in order to give a clear image of the speedups achieved. All algorithms assume the classical approach to the metric, in which the maximum norm is used. The key idea of the two last suggested algorithms is to avoid unnecessary comparisons by detecting them early. We use the term unnecessary to refer to those comparisons for which we know a priori that they will fail at the similarity check. The number of avoided comparisons is proved to be very large, resulting in an analogous large reduction of execution time, making them the fastest algorithms available today for the computation of Sample Entropy.
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Liu, Jing, Huibin Lu, Xiuru Zhang, Xiaoli Li, Lei Wang, Shimin Yin, and Dong Cui. "Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?" Entropy 25, no. 3 (February 21, 2023): 396. http://dx.doi.org/10.3390/e25030396.

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So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.
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Ji, Binghui, Xiaona Sun, Peimiao Chen, Siyu Wang, Shangfa Song, and Bo He. "An Integrated Navigation Algorithm for Underwater Vehicles Improved by a Variational Bayesian and Minimum Mixed Error Entropy Unscented Kalman Filter." Electronics 13, no. 23 (November 29, 2024): 4727. http://dx.doi.org/10.3390/electronics13234727.

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In complex marine environments, autonomous underwater vehicles (AUVs) rely on robust navigation and positioning. Traditional algorithms face challenges from sensor outliers and non-Gaussian noise, leading to significant prediction errors and filter divergence. Outliers in sensor observations also impact positioning accuracy. The original unscented Kalman filter (UKF) based on the minimum mean square error (MMSE) criterion suffers from performance degradation under these conditions. This paper enhances the minimum error entropy unscented Kalman filter algorithm using variational Bayesian (VB) methods and mixed entropy functions. By implementing minimum error entropy (MEE) and mixed kernel functions in the UKF, the algorithm’s robustness under complex underwater conditions is improved. The VB method adaptively fits the measurement noise covariance, enhancing adaptability to marine environments. Simulations and sea trials validate the proposed algorithm’s performance, showing significant improvements in navigation accuracy and root mean square error (RMSE). In environments with complex noise, our algorithm improves the overall navigation accuracy by at least 10% over other existing algorithms. This demonstrates the high accuracy and robustness of the algorithm.
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Du, Xinzhi. "A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy." Entropy 25, no. 3 (March 16, 2023): 510. http://dx.doi.org/10.3390/e25030510.

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Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enhances the efficiency of extracting the contribution of different features. The first entropy term helps to minimize the clusters’ dispersion and maximize the negative entropy to control the clustering process, which also promotes the association between the samples. The second entropy term helps to control the weights of features since different features have different weights in the clustering process. Experiments on real-world datasets indicate that the proposed algorithm gives better clustering results than other algorithms. The experiments demonstrate the proposed algorithm’s robustness by analyzing the parameters’ sensitivity and comparing the computational distance formulas. In summary, the improved algorithm improves classification performance under noisy interference and high-dimensional datasets, increases computational efficiency, performs well in real-world high-dimensional datasets, and encourages the development of robust noise-resistant high-dimensional fuzzy clustering algorithms.
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Morozov, Denys. "Polynomial Representation of Binary Trees of Entropy Binary Codes." Mohyla Mathematical Journal 4 (May 19, 2022): 20–23. http://dx.doi.org/10.18523/2617-70804202120-23.

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An important component of streaming large amounts of information are algorithms for compressing information flow. Which in turn are divided into lossless compression algorithms (entropic) - Shannon, Huffman, arithmetic coding, conditional compression - LZW, and otherinformation cone injections and lossy compression algorithms - such as mp3, jpeg and others. It is important to follow a formal strategy when building a lossy compression algorithm. It can be formulated as follows. After describing the set of objects that are atomic elements of exchange in the information flow, it is necessary to build an abstract scheme of this description, which will determine the boundary for abstract sections of this scheme, which begins the allowable losses. Approaches to the detection of an abstract scheme that generates compression algorithms with allowable losses can be obtained from the context of the subject area. For example, an audio stream compression algorithm can divide a signal into simple harmonics and leave among them those that are within a certain range of perception. Thus, the output signal is a certain abstraction of the input, which contains important information in accordance with the context of auditory perception of the audio stream and is represented by less information. A similar approach is used in the mp3 format, which is a compressed representation. Unlike lossy compression algorithms, entropic compression algorithms do not require contextanalysis, but can be built according to the frequency picture. Among the known algorithms for constructing such codes are the Shannon-Fano algorithm, the Huffman algorithm and arithmetic coding. Finding the information entropy for a given Shannon code is a trivial task. The inverse problem, namely finding the appropriate Shannon codes that have a predetermined entropy and with probabilities that are negative integer powers of two, is quite complex. It can be solved by direct search, but a significant disadvantage of this approach is its computational complexity. This article offers an alternative technique for finding such codes.
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Crysdian, Cahyo. "The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge." JOIV : International Journal on Informatics Visualization 7, no. 4 (December 31, 2023): 2481. http://dx.doi.org/10.62527/joiv.7.4.1727.

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This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.
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Crysdian, Cahyo. "The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge." JOIV : International Journal on Informatics Visualization 7, no. 4 (December 31, 2023): 2481. http://dx.doi.org/10.30630/joiv.7.4.01727.

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This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.
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Dissertations / Theses on the topic "Entropy algorithms"

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Höns, Robin. "Estimation of distribution algorithms and minimum relative entropy." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=980407877.

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Luo, Shen. "Interior-Point Algorithms Based on Primal-Dual Entropy." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1181.

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We propose a family of search directions based on primal-dual entropy in the context of interior point methods for linear programming. This new family contains previously proposed search directions in the context of primal-dual entropy. We analyze the new family of search directions by studying their primal-dual affine-scaling and constant-gap centering components. We then design primal-dual interior-point algorithms by utilizing our search directions in a homogeneous and self-dual framework. We present iteration complexity analysis of our algorithms and provide the results of computational experiments on NETLIB problems.
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Fellman, Laura Suzanne. "The Genetic Algorithm and Maximum Entropy Dice." PDXScholar, 1996. https://pdxscholar.library.pdx.edu/open_access_etds/5247.

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The Brandeis dice problem, originally introduced in 1962 by Jaynes as an illustration of the principle of maximum entropy, was solved using the genetic algorithm, and the resulting solution was compared with that obtained analytically. The effect of varying the genetic algorithm parameters was observed, and the optimum values for population size, mutation rate, and mutation interval were determined for this problem. The optimum genetic algorithm program was then compared to a completely random method of search and optimization. Finally, the genetic algorithm approach was extended to several variations of the original problem for which an analytical approach would be impractical.
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Meehan, Timothy J. "Joint demodulation of low-entropy narrow band cochannel signals." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FMeehan%5FPhD.pdf.

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Thesis (Ph.D. in Electrical Engineering)--Naval Postgraduate School, December 2006.
Dissertation supervisor(s): Frank E. Kragh. "December 2006." Includes bibliographical references (p. 167-177). Also available in print.
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Reimann, Axel. "Evolutionary algorithms and optimization." Doctoral thesis, [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=969093497.

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JIMMY, TJEN. "Entropy-Based Sensor Selection Algorithms for Damage Detection in SHM Systems." Doctoral thesis, Università degli Studi dell'Aquila, 2021. http://hdl.handle.net/11697/173561.

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It is often the case that small faults in a structure lead to irreparable damages that deliver a huge financial loss or even pose safety risks. Thus, an early fault detection is necessary, such that these unfortunate events can be avoided. In this thesis, the problem of structural damage detection is considered. In particular there are 3 main contributions: First, a novel sensors selection algorithm based on the concepts of entropy and information gain from information theory is developed, to reduce the number of sensors without affecting, or even improving, model accuracy; Second, a novel technique based on Kalman filtering and on a combination of Regression Trees theory from Machine Learning and Auto Regressive (AR) system identification from control theory is derived, to build models that can be used to detect structural damages. Finally, a new fault detection algorithm based on Poly-Exponential (PE) models and nonlinear Kalman filtering on the residual is introduced, which is able to enhance the sensitivity of the proposed fault detection algorithm and improve the data prediction quality for some accelerometers in a notably margin. The presented techniques are validated on three different experimental datasets, providing evidence that the proposed algorithms outperform some previous approaches, improving the prediction accuracy and the damage detection sensitivity while reducing the number of sensors.
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Kirsch, Matthew Robert. "Signal Processing Algorithms for Analysis of Categorical and Numerical Time Series: Application to Sleep Study Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1278606480.

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Molari, Marco. "Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/6160/.

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Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed. The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.
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Kotha, Aravind Eswar Ravi Raja, and Lakshmi Ratna Hima Rajitha Majety. "Performance Comparison of Image Enhancement Algorithms Evaluated on Poor Quality Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13880.

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Many applications require automatic image analysis for different quality of the input images. In many cases, the quality of acquired images is suitable for the purpose of the application. However, in some cases the quality of the acquired image has to be modified according to needs of a specific application. A higher quality of the image can be achieved by Image Enhancement (IE) algorithms. The choice of IE technique is challenging as this choice varies with the application purpose. The goal of this research is to investigate the possibility of the selective application for the IE algorithms. The values of entropy and Peak Signal to Noise Ratio (PSNR) of the acquired image are considered as parameters for selectivity. Three algorithms such as Retinex, Bilateral filter and Bilateral tone adjustment have been chosen as IE techniques for evaluation in this work. Entropy and PSNR are used for the performance evaluation of selected IE algorithms. In this study, we considered the images from three fingerprint image databases as input images to investigate the algorithms. The decision to enhance an image in these databases by the considered algorithms is based on the empirically evaluated entropy and PSNR thresholds. Automatic Fingerprint Identification System (AFIS) has been selected as the application of interest. The evaluation results show that the performance of the investigated IE algorithms affects significantly the performance of AFIS. The second conclusion is that entropy and PSNR might be considered as indicators for required IE of the input image for AFIS.
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Saraiva, Gustavo Francisco Rosalin. "Análise temporal da sinalização elétrica em plantas de soja submetidas a diferentes perturbações externas." Universidade do Oeste Paulista, 2017. http://bdtd.unoeste.br:8080/jspui/handle/jspui/1087.

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Submitted by Michele Mologni (mologni@unoeste.br) on 2018-07-27T17:57:40Z No. of bitstreams: 1 Gustavo Francisco Rosalin Saraiva.pdf: 5041218 bytes, checksum: 30127a7816b12d3bd7e57182e6229bc2 (MD5)
Made available in DSpace on 2018-07-27T17:57:40Z (GMT). No. of bitstreams: 1 Gustavo Francisco Rosalin Saraiva.pdf: 5041218 bytes, checksum: 30127a7816b12d3bd7e57182e6229bc2 (MD5) Previous issue date: 2017-03-31
Plants are complex organisms with dynamic processes that, due to their sessile way of life, are influenced by environmental conditions at all times. Plants can accurately perceive and respond to different environmental stimuli intelligently, but this requires a complex and efficient signaling system. Electrical signaling in plants has been known for a long time, but has recently gained prominence with the understanding of the physiological processes of plants. The objective of this thesis was to test the following hypotheses: temporal series of data obtained from electrical signaling of plants have non-random information, with dynamic and oscillatory pattern, such dynamics being affected by environmental stimuli and that there are specific patterns in responses to stimuli. In a controlled environment, stressful environmental stimuli were applied in soybean plants, and the electrical signaling data were collected before and after the application of the stimulus. The time series obtained were analyzed using statistical and computational tools to determine Frequency Spectrum (FFT), Autocorrelation of Values and Approximate Entropy (ApEn). In order to verify the existence of patterns in the series, classification algorithms from the area of machine learning were used. The analysis of the time series showed that the electrical signals collected from plants presented oscillatory dynamics with frequency distribution pattern in power law. The results allow to differentiate with great efficiency series collected before and after the application of the stimuli. The PSD and autocorrelation analyzes showed a great difference in the dynamics of the electric signals before and after the application of the stimuli. The ApEn analysis showed that there was a decrease in the signal complexity after the application of the stimuli. The classification algorithms reached significant values in the accuracy of pattern detection and classification of the time series, showing that there are mathematical patterns in the different electrical responses of the plants. It is concluded that the time series of bioelectrical signals of plants contain discriminant information. The signals have oscillatory dynamics, having their properties altered by environmental stimuli. There are still mathematical patterns built into plant responses to specific stimuli.
As plantas são organismos complexos com processos dinâmicos que, devido ao seu modo séssil de vida, sofrem influência das condições ambientais todo o tempo. Plantas podem percebem e responder com precisão a diferentes estímulos ambientais de forma inteligente, mas para isso se faz necessário um complexo e eficiente sistema de sinalização. A sinalização elétrica em plantas já é conhecida há muito tempo, mas vem ganhando destaque recentemente com seu entendimento em relação aos processos fisiológicos das plantas. O objetivo desta tese foi testar as seguintes hipóteses: séries temporais de dados obtidos da sinalização elétrica de plantas possuem informação não aleatória, com padrão dinâmico e oscilatório, sendo tal dinâmica afetada por estímulos ambientais e que há padrões específicos nas respostas a estímulos. Em ambiente controlado, foram aplicados estímulos ambientais estressantes em plantas de soja, e captados os dados de sinalização elétrica antes e após a aplicação dos mesmos. As séries temporais obtidas foram analisadas utilizando ferramentas estatísticas e computacionais para se determinar o Espectro de Frequências (FFT), Autocorrelação dos valores e Entropia Aproximada (ApEn). Para se verificar a existência de padrões nas séries, foram utilizados algoritmos de classificação da área de aprendizado de máquina. A análise das séries temporais mostrou que os sinais elétricos coletados de plantas apresentaram dinâmica oscilatória com padrão de distribuição de frequências em lei de potência. Os resultados permitem diferenciar com grande eficácia séries coletadas antes e após a aplicação dos estímulos. As análises de PSD e autocorrelação mostraram grande diferença na dinâmica dos sinais elétricos antes e após a aplicação dos estímulos. A análise de ApEn mostrou haver diminuição da complexidade do sinal após a aplicação dos estímulos. Os algoritmos de classificação alcançaram valores significativos na acurácia de detecção de padrões e classificação das séries temporais, mostrando haver padrões matemáticos nas diferentes respostas elétricas das plantas. Conclui-se que as séries temporais de sinais bioelétricos de plantas possuem informação discriminante. Os sinais possuem dinâmica oscilatória, tendo suas propriedades alteradas por estímulos ambientais. Há ainda padrões matemáticos embutidos nas respostas da planta a estímulos específicos.
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Books on the topic "Entropy algorithms"

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National Institute of Standards and Technology (U.S.), ed. Parallel algorithms for entropy-coding techniques. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1998.

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Santos, Cícero Nogueira dos. Entropy guided transformation learning: Algorithms and applications. London: Springer, 2012.

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dos Santos, Cícero Nogueira, and Ruy Luiz Milidiú. Entropy Guided Transformation Learning: Algorithms and Applications. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2978-3.

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Luiz, Milidiu Ruy, and SpringerLink (Online service), eds. Entropy Guided Transformation Learning: Algorithms and Applications. London: Springer London, 2012.

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Príncipe, J. C. Information theoretic learning: Renyi's entropy and kernel perspectives. New York: Springer, 2010.

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M, Le Thinh, Lian Yong, and SpringerLink (Online service), eds. Entropy Coders of the H.264/AVC Standard: Algorithms and VLSI Architectures. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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1954-, Mayer-Kress G., and International Workshop on "Dimensions and Entropies in Chaotic Systems" (1985 : Pecos River Ranch), eds. Dimensions and entropies in chaotic systems: Quantification of complex behavior : proceedings of an international workshop at the Pecos River Ranch, New Mexico, September 11-16, 1985. Berlin: Springer-Verlag, 1986.

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Sbert, Mateu. Information theory tools for computer graphics. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool Publishers, 2009.

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United States. National Aeronautics and Space Administration., ed. An adaptive numeric predictor-corrector guidance algorithm for atmospheric entry vehicles. Cambridge, Mass: The Charles Stark Draper Laboratories, Inc., 1987.

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United States. National Aeronautics and Space Administration., ed. Entry vehicle performance analysis and atmospheric guidance algorithm for precision landing on Mars. Cambridge, Mass: The Charles Stark Draper Laboratory, Inc., 1990.

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Book chapters on the topic "Entropy algorithms"

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Pham, Tuan D. "Entropy Algorithms." In Fuzzy Recurrence Plots and Networks with Applications in Biomedicine, 81–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37530-0_6.

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Cardinal, Jean, Samuel Fiorini, and Gwenaël Joret. "Minimum Entropy Coloring." In Algorithms and Computation, 819–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11602613_82.

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Masters, Timothy. "Information and Entropy." In Data Mining Algorithms in C++, 1–73. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3315-3_1.

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Li, C. H., C. K. Lee, and P. K. S. Tam. "Entropic Thresholding Algorithms and their Optimizations." In Entropy Measures, Maximum Entropy Principle and Emerging Applications, 199–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-36212-8_10.

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Cordova, Joshimar, and Gonzalo Navarro. "Practical Dynamic Entropy-Compressed Bitvectors with Applications." In Experimental Algorithms, 105–17. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38851-9_8.

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Farzan, Arash, Travis Gagie, and Gonzalo Navarro. "Entropy-Bounded Representation of Point Grids." In Algorithms and Computation, 327–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17514-5_28.

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Ferragina, Paolo. "Data Structures: Time, I/Os, Entropy, Joules!" In Algorithms – ESA 2010, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15781-3_1.

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dos Santos, Cícero Nogueira, and Ruy Luiz Milidiú. "Entropy Guided Transformation Learning." In Entropy Guided Transformation Learning: Algorithms and Applications, 9–21. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2978-3_2.

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Cooke, D. E., V. Kreinovich, and L. Longpré. "Which Algorithms are Feasible? Maxent Approach." In Maximum Entropy and Bayesian Methods, 25–33. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5028-6_3.

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Shapiro, Jonathan L., Magnus Rattray, and Adam PrüGel-Bennett. "Maximum Entropy Analysis of Genetic Algorithms." In Maximum Entropy and Bayesian Methods, 303–10. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-5430-7_36.

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Conference papers on the topic "Entropy algorithms"

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Feng, Xilong, Guosheng Hao, Yi Zhu, and Shijin Ren. "An Entropy Feedback Based Evolutionary Algorithms and its Application." In 2024 6th International Conference on Data-driven Optimization of Complex Systems (DOCS), 685–95. IEEE, 2024. http://dx.doi.org/10.1109/docs63458.2024.10704514.

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Kurt, İlke, Sezer Ulukaya, Oğuzhan Erdem, Sibel Güler, and Cem Uzun. "Shannon Wavelet Entropy-based Machine Learning Applications in Parkinson’s Disease Diagnosis with Videonystagmography." In 2024 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 127–31. IEEE, 2024. http://dx.doi.org/10.23919/spa61993.2024.10715608.

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Antoneac, Andrada-Livia, Gheorghiţă Mutu, and Dragoş-Teodor Gavriluţ. "Entropy-Driven Visualization in GView: Unveiling the Unknown in Binary File Formats." In 2024 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 74–81. IEEE, 2024. https://doi.org/10.1109/synasc65383.2024.00025.

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Lan, Rui, Liang Gao, and Chao Liu. "Research on the evaluation model of human job matching based on improved entropy method." In Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), edited by Qinghua Lu and Weishan Zhang, 3. SPIE, 2024. http://dx.doi.org/10.1117/12.3049482.

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Harvey, Nicholas J. A., Jelani Nelson, and Krzysztof Onak. "Streaming algorithms for estimating entropy." In 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578656.

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Suciu, Alin, Kinga Marton, and Zoltan Antal. "Data Flow Entropy Collector." In 2008 10th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2008. http://dx.doi.org/10.1109/synasc.2008.25.

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Nadar, Mariappan S., Philip J. Sementilli, and Bobby R. Hunt. "A Projection-Onto-Convex-Sets Interpretation of Cross-Entropy Based Image Super-Resolution Algorithms." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc3.

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Signal recovery problems are generally posed in the form of rigid constraints (constraint sets), flexible constraints (optimization functional) or a combination thereof. Minimum cross-entropy methods1,2 belong to this third category due to an implicit rigid non-negativity constraint. An elegant approach to solving problems of the first category for convex constraint sets is the Projection Onto Convex Sets (POCS)3 technique. POCS has been limited primarily to least-squares projections, although other distance measures have been proposed.4 In this paper, minimum cross-entropy methods are interpreted as parallel cross-entropic POCS algorithms. This interpretation provides a theoretical basis for including rigid constraints in iterative super-resolution algorithms.
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Rothvoß, Thomas. "The Entropy Rounding Method in Approximation Algorithms." In Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2012. http://dx.doi.org/10.1137/1.9781611973099.32.

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Chang-Yong Lee. "Genetic algorithms with entropy-Boltzmann samplings." In Proceedings of the 2001 Congress on Evolutionary Computation. IEEE, 2001. http://dx.doi.org/10.1109/cec.2001.934432.

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Wentao Ma, Hua Qu, Jihong Zhao, Badong Chen, and Jose C. Principe. "Sparsity aware minimum error entropy algorithms." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178357.

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Reports on the topic "Entropy algorithms"

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Youssef, Abdou. Parallel algorithms for entropy-coding techniques. Gaithersburg, MD: National Institute of Standards and Technology, 1998. http://dx.doi.org/10.6028/nist.ir.6113.

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Dolotii, Marharyta H., and Pavlo V. Merzlykin. Using the random number generator with a hardware entropy source for symmetric cryptography problems. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2883.

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The aim of the research is to test the possibility of using the developed random number generator [1], which utilizes the sound card noise as an entropy source, in the symmetric cryptography algorithms.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, December 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Allende López, Marcos, Diego López, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo, et al. Quantum-Resistance in Blockchain Networks. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003313.

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This paper describes the work carried out by the Inter-American Development Bank, the IDB Lab, LACChain, Cambridge Quantum Computing (CQC), and Tecnológico de Monterrey to identify and eliminate quantum threats in blockchain networks. The advent of quantum computing threatens internet protocols and blockchain networks because they utilize non-quantum resistant cryptographic algorithms. When quantum computers become robust enough to run Shor's algorithm on a large scale, the most used asymmetric algorithms, utilized for digital signatures and message encryption, such as RSA, (EC)DSA, and (EC)DH, will be no longer secure. Quantum computers will be able to break them within a short period of time. Similarly, Grover's algorithm concedes a quadratic advantage for mining blocks in certain consensus protocols such as proof of work. Today, there are hundreds of billions of dollars denominated in cryptocurrencies that rely on blockchain ledgers as well as the thousands of blockchain-based applications storing value in blockchain networks. Cryptocurrencies and blockchain-based applications require solutions that guarantee quantum resistance in order to preserve the integrity of data and assets in their public and immutable ledgers. We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network. In our implementation, quantum entropy was provided via the IronBridge Platform from CQC and we used LACChain Besu as the blockchain network.
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Liu, Ju, Hector Gomez, John A. Evans, Thomas J. Hughes, and Chad M. Landis. Functional Entropy Variables: A New Methodology for Deriving Thermodynamically Consistent Algorithms for Complex Fluids, with Particular Reference to the Isothermal Navier-Stokes-Korteweg Equations. Fort Belvoir, VA: Defense Technical Information Center, November 2012. http://dx.doi.org/10.21236/ada572015.

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Fellman, Laura. The Genetic Algorithm and Maximum Entropy Dice. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7120.

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Soloviev, Vladimir, Andrii Bielinskyi, and Viktoria Solovieva. Entropy Analysis of Crisis Phenomena for DJIA Index. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3179.

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The Dow Jones Industrial Average (DJIA) index for the 125-year-old (since 1896) history has experienced many crises of different nature and, reflecting the dynamics of the world stock market, is an ideal model object for the study of quantitative indicators and precursors of crisis phenomena. In this paper, the classification and periodization of crisis events for the DJIA index have been carried out; crashes and critical events have been highlighted. Based on the modern paradigm of the theory of complexity, a spectrum of entropy indicators and precursors of crisis phenomena have been proposed. The entropy of a complex system is not only a measure of uncertainty (like Shannon's entropy) but also a measure of complexity (like the permutation and Tsallis entropy). The complexity of the system in a crisis changes significantly. This fact can be used as an indicator, and in the case of a proactive change as a precursor of a crisis. Complex systems also have the property of scale invariance, which can be taken into account by calculating the Multiscale entropy. The calculations were carried out within the framework of the sliding window algorithm with the subsequent comparison of the entropy measures of complexity with the dynamics of the DJIA index itself. It is shown that Shannon's entropy is an indicator, and the permutation and Tsallis entropy are the precursors of crisis phenomena to the same extent for both crashes and critical events.
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Berney, Ernest, Naveen Ganesh, Andrew Ward, J. Newman, and John Rushing. Methodology for remote assessment of pavement distresses from point cloud analysis. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40401.

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The ability to remotely assess road and airfield pavement condition is critical to dynamic basing, contingency deployment, convoy entry and sustainment, and post-attack reconnaissance. Current Army processes to evaluate surface condition are time-consuming and require Soldier presence. Recent developments in the area of photogrammetry and light detection and ranging (LiDAR) enable rapid generation of three-dimensional point cloud models of the pavement surface. Point clouds were generated from data collected on a series of asphalt, concrete, and unsurfaced pavements using ground- and aerial-based sensors. ERDC-developed algorithms automatically discretize the pavement surface into cross- and grid-based sections to identify physical surface distresses such as depressions, ruts, and cracks. Depressions can be sized from the point-to-point distances bounding each depression, and surface roughness is determined based on the point heights along a given cross section. Noted distresses are exported to a distress map file containing only the distress points and their locations for later visualization and quality control along with classification and quantification. Further research and automation into point cloud analysis is ongoing with the goal of enabling Soldiers with limited training the capability to rapidly assess pavement surface condition from a remote platform.
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