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Статті в журналах з теми "Dynamic time warping algorithm"
Jeong, Seung-Do. "Speaker Identification Using Dynamic Time Warping Algorithm." Journal of the Korea Academia-Industrial cooperation Society 12, no. 5 (May 31, 2011): 2402–9. http://dx.doi.org/10.5762/kais.2011.12.5.2402.
Повний текст джерелаStübinger, Johannes, and Dominik Walter. "Using Multi-Dimensional Dynamic Time Warping to Identify Time-Varying Lead-Lag Relationships." Sensors 22, no. 18 (September 12, 2022): 6884. http://dx.doi.org/10.3390/s22186884.
Повний текст джерелаGao, Cuifang, Junjie Li, Wanqiang Shen, and Ping Yin. "Two-dimensional dynamic time warping algorithm for matrices similarity." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 859–71. http://dx.doi.org/10.3233/ida-215908.
Повний текст джерелаAli, Aya Hamdy, Ayman Atia, and Mostafa-Sami M. Mostafa. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors." International Journal of Ambient Computing and Intelligence 8, no. 3 (July 2017): 22–37. http://dx.doi.org/10.4018/ijaci.2017070102.
Повний текст джерелаHagen, Cedric J., Brendan T. Reilly, Joseph S. Stoner, and Jessica R. Creveling. "Dynamic time warping of palaeomagnetic secular variation data." Geophysical Journal International 221, no. 1 (January 9, 2020): 706–21. http://dx.doi.org/10.1093/gji/ggaa004.
Повний текст джерелаHale, Dave. "Dynamic warping of seismic images." GEOPHYSICS 78, no. 2 (March 1, 2013): S105—S115. http://dx.doi.org/10.1190/geo2012-0327.1.
Повний текст джерелаLiu, Zhen Wu, Zhi Wu Shang, Ya Feng Li, and Tai Yong Wang. "A Fault Diagnosis System Based on Bistable Stochastic Resonance and Dynamic Time Warping." Key Engineering Materials 693 (May 2016): 1294–99. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1294.
Повний текст джерелаWang, Hairong, and Qiufang Zheng. "Improvement and Application of Hale’s Dynamic Time Warping Algorithm." Symmetry 16, no. 6 (May 23, 2024): 645. http://dx.doi.org/10.3390/sym16060645.
Повний текст джерелаPielmus, Alexandru-Gabriel, Michael Klum, Timo Tigges, Reinhold Orglmeister, and Mike Urban. "Progressive Dynamic Time Warping for Noninvasive Blood Pressure Estimation." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 579–82. http://dx.doi.org/10.1515/cdbme-2020-3148.
Повний текст джерелаC.Bhokse, Bhushan, and Bhushan S. Thakare. "Devnagari Handwriting Recognition System using Dynamic Time Warping Algorithm." International Journal of Computer Applications 52, no. 9 (August 30, 2012): 7–13. http://dx.doi.org/10.5120/8228-0241.
Повний текст джерелаДисертації з теми "Dynamic time warping algorithm"
Operti, Felipe Gioachino. "Interpolation strategy based on Dynamic Time Warping." reponame:Repositório Institucional da UFC, 2015. http://www.repositorio.ufc.br/handle/riufc/11446.
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In oil industry, it is essential to have the knowledge of the stratified rocks’ lithology and, as consequence, where are placed the oil and the natural gases reserves, in order to efficiently drill the soil, without a major expense. In this context, the analysis of seismological data is highly relevant for the extraction of such hydrocarbons, producing predictions of profiles through reflection of mechanical waves in the soil. The image of the seismic mapping produced by wave refraction and reflection into the soil can be analysed to find geological formations of interest. In 1978, H. Sakoe et al. defined a model called Dynamic Time Warping (DTW)[23] for the local detection of similarity between two time series. We apply the Dynamic Time Warping Interpolation (DTWI) strategy to interpolate and simulate a seismic landscape formed by 129 depth-dependent sequences of length 201 using different values of known sequences m, where m = 2, 3, 5, 9, 17, 33, 65. For comparison, we done the same operation of interpolation using a Standard Linear Interpolation (SLI). Results show that the DTWI strategy works better than the SLI when m = 3, 5, 9, 17, or rather when distance between the known series has the same order size of the soil layers.
Sinkus, Skirmantas. "Kinect įrenginiui skirtų gestų atpažinimo algoritmų tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20140806_143213-09689.
Повний текст джерелаMicrosoft Kinect device was released in 2010. It was designed for Microsoft Xbox 360 gaming console, later on in 2012 was presented Kinect device for Windows personal computer. So this device is new and current. Many games has been created for Microsoft Kinect device, but this device could be used not only in games, one of the areas where we can use it its sport, specific training, which can be performed at home. At this moment in world are huge variety of games, software, training programs which allows user to control training course by following a person properly perform training provided movements. Since in Lithuania similar software is not available, so it is necessary to create software that would allow Lithuania coaches create training focused on the use of this device. The main goal of this work is to perform research of the Kinect device gesture recognition algorithms to study exactly how they can recognize gestures or gesture. It will focus on this issue mainly, but does not address the criteria for recognition as the time and difficulty of realization. In this paper, a program that recognizes movements and gestures are using the Golden section search algorithm. Algorhithm compares the two models or templates, and if it can not find a match, this is the first template slightly rotated and comparison process is started again, also a certain variable helping, we can modify the algorithm accuracy. Also for comparison we can use Hidden Markov models algorhithm received... [to full text]
Кононенко, Олексій Сергійович. "Дослідження системи розпізнавання голосових сигналів в умовах обмеженої обчисленої потужності". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23167.
Повний текст джерелаTheme urgency. Speech recognition systems are becoming increasingly popular and increasingly common. Successful examples of using speech recognition technology in mobile applications are: entering a voice address in Yandex.Navigator, Google Now voice search. In addition to mobile devices, speech recognition technology is widely used in various areas of human activity: ● Telephony: automates the processing of incoming and outgoing calls by creating voice self-service systems in particular for: receiving background information and advice, ordering services, goods, changing the parameters of current services, conducting surveys, questionnaires, collecting information, informing and any other scenarios; ● “Smart House” solutions: voice interface for intelligent home systems management; ● Household appliances and work: voice interface of electronic robots; voice control of home appliances, etc .; ● Cars: voice control in the car - for example, the navigation system; ● Social services for people with disabilities; ● Comprehensive information security systems. Voice authentication. ● Determination of the emotional color of the speaker's voice. Object of research are systems and algorithms for voice recognition. Subject of research is a usage of dynamic time warping algorithm in speech recognition systems in the conditions of limited computing power Research objective: development and modification of the dynamic time warping algorithm for recognizing a limited vocabulary. Research methods. Methods of mathematical modeling, methods of optimization, methods of system analysis, numerical methods are used in this work.
Актуальность темы. Сейчас системы распознавания речи приобретают все большую популярность и встречаются все чаще. Успешными примерами использования технологии распознавания речи в мобильных приложениях являются: ввод адреса голосом в Яндекс.Навигатор, голосовой поиск Google Now. Кроме мобильных устройств, технология распознавания речи находит широкое распространение в различных сферах человеческой деятельности: ● Телефония: автоматизация обработки входящих и исходящих звонков путем создания голосовых систем самообслуживания в частности для: получения справочной информации и консультирование, заказ услуг, товаров, изменения параметров действующих услуг, проведения опросов, анкетирования, сбора информации, информирование и любые другие сценарии; ● Решение "Умный дом": голосовой интерфейс управления системами «Умный дом»; ● Бытовая техника и работы: голосовой интерфейс электронных роботов голосовое управление бытовой техникой и т.д.; ● Автомобили: голосовое управление в салоне автомобиля - например, навигационной системой; ● Социальные сервисы для людей с ограниченными возможностями; ● Комплексные системы защиты информации. Голосовая аутентификация. ● Определение эмоциональной окраски голоса диктора. Объектом исследования являются системы и алгоритмы распознавания голосовых сигналов. Предметом исследования является алгоритм динамической трансформации временной шкалы в системах распознавания голосовых сигналов в условиях ограниченной вычислительной мощности. Цель работы: разработка и модификация алгоритма динамической трансформации временной шкалы для распознавания ограниченного словаря. Методы исследования. В работе используются методы математического моделирования, методы оптимизации, методы системного анализа, численные методы.
Fitriani. "Multiscale Dynamic Time and Space Warping." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45279.
Повний текст джерелаIncludes bibliographical references (p. 149-151).
Dynamic Time and Space Warping (DTSW) is a technique used in video matching applications to find the optimal alignment between two videos. Because DTSW requires O(N4) time and space complexity, it is only suitable for short and coarse resolution videos. In this thesis, we introduce Multiscale DTSW: a modification of DTSW that has linear time and space complexity (O(N)) with good accuracy. The first step in Multiscale DTSW is to apply the DTSW algorithm to coarse resolution input videos. In the next step, Multiscale DTSW projects the solution from coarse resolution to finer resolution. A solution for finer resolution can be found effectively by refining the projected solution. Multiscale DTSW then repeatedly projects a solution from the current resolution to finer resolution and refines it until the desired resolution is reached. I have explored the linear time and space complexity (O(N)) of Multiscale DTSW both theoretically and empirically. I also have shown that Multiscale DTSW achieves almost the same accuracy as DTSW. Because of its efficiency in computational cost, Multiscale DTSW is suitable for video detection and video classification applications. We have developed a Multiscale-DTSW-based video classification framework that achieves the same accuracy as a DTSW-based video classification framework with greater than 50 percent reduction in the execution time. We have also developed a video detection application that is based on Dynamic Space Warping (DSW) and Multiscale DTSW methods and is able to detect a query video inside a target video in a short time.
by Fitriani.
S.M.
Hounsinou, Sena Gladys N. "Hardware realization of speech-time warping algorithm /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1650508391&sid=1&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Повний текст джерелаJúnior, Sylvio Barbon. "Dynamic Time Warping baseado na transformada wavelet." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-15042008-211812/.
Повний текст джерелаDynamic TimeWarping (DTW) is a pattern matching technique for speech recognition, that is based on a temporal alignment of the input signal with the template models. One drawback of this technique is its high computational cost. This work presents a modified version of the DTW, based on the DiscreteWavelet Transform (DWT), that reduces the complexity of the original algorithm. The performance obtained with the proposed algorithm is very promising, improving the recognition in terms of time and memory allocation, while the precision is not affected. Tests were performed with speech data collected from TIMIT corpus provided by Linguistic Data Consortium (LDC).
Coelho, Mariana Sátiro. "Patterns in financial markets: Dynamic time warping." Master's thesis, NSBE - UNL, 2012. http://hdl.handle.net/10362/9539.
Повний текст джерелаThis work project introduces the performance of the algorithm Dynamic Time Warping amidst trading strategies in the financial markets. The employed procedure allows the comparison between any two sequences of data with different time lengths. Different features for the method were implemented, although those did not improve its promptness or accuracy in the outcomes obtained. Two potential investment strategies are presented within this theme. One yielded satisfactory outcomes whilst the other resulted in inconsistent values. The results point to the possible existence of patterns in the Equity Indexes’ behaviour, as well as their distortion across the time axis.
Ko, Ming Hsiao. "Using dynamic time warping for multi-sensor fusion." Thesis, Curtin University, 2009. http://hdl.handle.net/20.500.11937/384.
Повний текст джерелаKo, Ming Hsiao. "Using dynamic time warping for multi-sensor fusion." Curtin University of Technology, Department of Computing, 2009. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=129032.
Повний текст джерелаHence, we proposed a multi-agent framework for temporal fusion, which emphasises the time dimension of the fusion process, that is, fusion of the multi-sensor data or events derived over a period of time. The proposed multi-agent framework has three major layers: hardware, agents, and users. There are three different fusion architectures: centralized, hierarchical, and distributed, for organising the group of agents. The temporal fusion process of the proposed framework is elaborated by using the information graph. Finally, the core of the proposed temporal fusion framework – Dynamic Time Warping (DTW) temporal fusion agent is described in detail.
Fusing multisensory data over a period of time is a challenging task, since the data to be fused consists of complex sequences that are multi–dimensional, multimodal, interacting, and time–varying in nature. Additionally, performing temporal fusion efficiently in real–time is another challenge due to the large amount of data to be fused. To address these issues, we proposed the DTW temporal fusion agent that includes four major modules: data pre-processing, DTW recogniser, class templates, and decision making. The DTW recogniser is extended in various ways to deal with the variability of multimodal sequences acquired from multiple heterogeneous sensors, the problems of unknown start and end points, multimodal sequences of the same class that hence has different lengths locally and/or globally, and the challenges of online temporal fusion.
We evaluate the performance of the proposed DTW temporal fusion agent on two real world datasets: 1) accelerometer data acquired from performing two hand gestures, and 2) a benchmark dataset acquired from carrying a mobile device and performing pre-defined user scenarios. Performance results of the DTW based system are compared with those of a Hidden Markov Model (HMM) based system. The experimental results from both datasets demonstrate that the proposed DTW temporal fusion agent outperforms HMM based systems, and has the capability to perform online temporal fusion efficiently and accurately in real–time.
Aguiar, Rogerio Oliveira de. "Classificador Automático e Não-Supervisionado de Batimentos Cardíacos Baseado no Algoritmo Dynamic Tiime Warping." Universidade Federal do Espírito Santo, 2008. http://repositorio.ufes.br/handle/10/4066.
Повний текст джерелаO Projeto Telecardio é um projeto de pesquisa em telemonitoramento de pacientes cardíacos e identificação automática de situações de risco. Neste contexto, está sendo proposto um sistema de análise de eletrocardiograma como uma ferramenta de auxílio ao diagnóstico médico. O sistema classifica os batimentos de um registro de ECG ambulatorial tendo como referência o batimento predominante do paciente. A classificação se dá através de uma abordagem original não supervisionada que faz uso do método Alinhamento Temporal Dinâmico na comparação entre batimentos com tamanhos e formas diferentes. Além disso, é tratado neste trabalho o problema da classificação de batimentos prematuros a partir do estudo de rótulos feitos por cardiologistas nos batimentos da base utilizada neste trabalho. Por fim, é proposta uma interface gráfica que apresenta o resultado da análise realizada pelo sistema de classificação, destacando-se informações importantes e a morfologias dos batimentos predominantes ao longo de trechos do ECG. Os batimentos predominantes são determinados por um algoritmo original que realiza o cálculo do batimento médio a partir de um conjunto de batimentos. O sistema foi testado na MIT-BIH Arrhythmia Database e os resultados alcançados validaram a estratégia proposta.
Книги з теми "Dynamic time warping algorithm"
Koehler, Loren M. Cursive script recognition using a dynamic time warping method. 1988.
Знайти повний текст джерелаЧастини книг з теми "Dynamic time warping algorithm"
Bringmann, Karl, Nick Fischer, Ivor van der Hoog, Evangelos Kipouridis, Tomasz Kociumaka, and Eva Rotenberg. "Dynamic Dynamic Time Warping." In Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 208–42. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2024. http://dx.doi.org/10.1137/1.9781611977912.10.
Повний текст джерелаBugdol, Marcin, Zuzanna Segiet, and Michał Kręcichwost. "Pronunciation Error Detection Using Dynamic Time Warping Algorithm." In Advances in Intelligent Systems and Computing, 345–54. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06596-0_32.
Повний текст джерелаTsinaslanidis, Prodromos E., and Achilleas D. Zapranis. "Dynamic Time Warping for Pattern Recognition." In Technical Analysis for Algorithmic Pattern Recognition, 193–204. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23636-0_9.
Повний текст джерелаSood, Meenakshi, and Shruti Jain. "Speech Recognition Employing MFCC and Dynamic Time Warping Algorithm." In Innovations in Information and Communication Technologies (IICT-2020), 235–42. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66218-9_27.
Повний текст джерелаChen, Bo-Xian, Kuo-Tsung Tseng, and Chang-Biau Yang. "A Minimum-First Algorithm for Dynamic Time Warping on Time Series." In Communications in Computer and Information Science, 449–56. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9190-3_48.
Повний текст джерелаZhou, Mi. "An OGS-Based Dynamic Time Warping Algorithm for Time Series Data." In Contributions to Economics, 115–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41585-2_10.
Повний текст джерелаBuchin, Maike, Anne Driemel, Koen van Greevenbroek, Ioannis Psarros, and Dennis Rohde. "Approximating Length-Restricted Means Under Dynamic Time Warping." In Approximation and Online Algorithms, 225–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18367-6_12.
Повний текст джерелаMaouche, Fadila, and Mohamed Benmohammed. "Dynamic Time Warping Inside a Genetic Algorithm for Automatic Speech Recognition." In Modelling and Implementation of Complex Systems, 180–92. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05481-6_14.
Повний текст джерелаLong, Wei, Shuyan Pan, and Hai Wu. "Distribution Network Differential Protection Method Based on Dynamic Time Warping Algorithm." In Lecture Notes in Electrical Engineering, 29–42. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7156-2_3.
Повний текст джерелаGraniero, Paolo, and Marco Gärtler. "Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis." In Machine Learning for Cyber Physical Systems, 53–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_6.
Повний текст джерелаТези доповідей конференцій з теми "Dynamic time warping algorithm"
Zaharia, Tiberius, Svetlana Segarceanu, Marius Cotescu, and Alexandru Spataru. "Quantized Dynamic Time Warping (DTW) algorithm." In 2010 8th International Conference on Communications (COMM). IEEE, 2010. http://dx.doi.org/10.1109/iccomm.2010.5509068.
Повний текст джерелаLou, Yuansheng, Huanhuan Ao, and Yuchao Dong. "Improvement of Dynamic Time Warping (DTW) Algorithm." In 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). IEEE, 2015. http://dx.doi.org/10.1109/dcabes.2015.103.
Повний текст джерелаTaghavi, Nazita, Jacob Berdichevsky, Namrata Balakrishnan, Karla C. Welch, Sumit Kumar Das, and Dan O. Popa. "Online Dynamic Time Warping Algorithm for Human-Robot Imitation." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9562110.
Повний текст джерелаTai, James Shueyen, Kin Fun Li, and Haytham Elmiligi. "Dynamic Time Warping Algorithm: A Hardware Realization in VHDL." In 2013 International Conference on IT Convergence and Security (ICITCS). IEEE, 2013. http://dx.doi.org/10.1109/icitcs.2013.6717829.
Повний текст джерелаYing, Rex, Jiangwei Pan, Kyle Fox, and Pankaj K. Agarwal. "A simple efficient approximation algorithm for dynamic time warping." In SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2996913.2996954.
Повний текст джерелаJunhua, Chang, Li Hua, Li Xinhao, and Li Xianing. "Application of Dynamic Time Warping Algorithm in Oilfield Development." In 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2019. http://dx.doi.org/10.1109/icaica.2019.8873502.
Повний текст джерелаAngus, J. A. S., and M. T. Whitaker. "An algorithm for increasing speed in dynamic time warping." In European Conference on Speech Technology. ISCA: ISCA, 1987. http://dx.doi.org/10.21437/ecst.1987-43.
Повний текст джерелаBisht, Yashwant Singh. "Low Energy Data Aggregation Using Dynamic Time Warping Algorithm." In 2024 3rd International Conference for Innovation in Technology (INOCON). IEEE, 2024. http://dx.doi.org/10.1109/inocon60754.2024.10511314.
Повний текст джерелаZhou, Mi. "An OGS-based Dynamic Time Warping algorithm for time series data." In 2013 International Conference on Engineering, Management Science and Innovation (ICEMSI). IEEE, 2013. http://dx.doi.org/10.1109/icemsi.2013.6913981.
Повний текст джерелаYuxin, Zhang, and Yoshikazu Miyanaga. "An improved dynamic time warping algorithm employing nonlinear median filtering." In 2011 11th International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2011. http://dx.doi.org/10.1109/iscit.2011.6089967.
Повний текст джерелаЗвіти організацій з теми "Dynamic time warping algorithm"
Eslami, Keyvan, and Thomas M. Phelan. The Art of Temporal Approximation: An Investigation into Numerical Solutions to Discrete and Continuous-Time Problems in Economics. Federal Reserve Bank of Cleveland, May 2023. http://dx.doi.org/10.26509/frbc-wp-202310.
Повний текст джерелаKuznetsov, Victor, Vladislav Litvinenko, Egor Bykov, and Vadim Lukin. A program for determining the area of the object entering the IR sensor grid, as well as determining the dynamic characteristics. Science and Innovation Center Publishing House, April 2021. http://dx.doi.org/10.12731/bykov.0415.15042021.
Повний текст джерелаMorkun, Vladimir S., Natalia V. Morkun, and Andrey V. Pikilnyak. Augmented reality as a tool for visualization of ultrasound propagation in heterogeneous media based on the k-space method. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3757.
Повний текст джерелаWitzig, Andreas, Camilo Tello, Franziska Schranz, Johannes Bruderer, and Matthias Haase. Quantifying energy-saving measures in office buildings by simulation in 2D cross sections. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541623658.
Повний текст джерелаBARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.
Повний текст джерелаTan, Peng, and Nicholas Sitar. Parallel Level-Set DEM (LS-DEM) Development and Application to the Study of Deformation and Flow of Granular Media. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, March 2023. http://dx.doi.org/10.55461/kmiz5819.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерела