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Статті в журналах з теми "Wavelet windows"
Liu, Zhishuai, Guihua Yao, Qing Zhang, Junpu Zhang, and Xueying Zeng. "Wavelet Scattering Transform for ECG Beat Classification." Computational and Mathematical Methods in Medicine 2020 (October 9, 2020): 1–11. http://dx.doi.org/10.1155/2020/3215681.
Повний текст джерелаCOLAK, O. H., T. C. DESTICI, S. OZEN, H. ARMAN, and O. CEREZCI. "FREQUENCY-ENERGY VARIABILITY CHARACTERIZATION OF LOCAL REAL-TIME NOISY SEISMIC RECORDS." Fluctuation and Noise Letters 08, no. 01 (March 2008): L31—L39. http://dx.doi.org/10.1142/s0219477508004246.
Повний текст джерелаEom, I. K., and Y. S. Kim. "Wavelet-Based Denoising With Nearly Arbitrarily Shaped Windows." IEEE Signal Processing Letters 11, no. 12 (December 2004): 937–40. http://dx.doi.org/10.1109/lsp.2004.836940.
Повний текст джерелаScheuer, T. E., and D. E. Wagner. "Deconvolution by autocepstral windowing." GEOPHYSICS 50, no. 10 (October 1985): 1533–40. http://dx.doi.org/10.1190/1.1441843.
Повний текст джерелаHussein, Ameer M., Adel A. Obed, Rana H. A. Zubo, Yasir I. A. Al-Yasir, Ameer L. Saleh, Hussein Fadhel, Akbar Sheikh-Akbari, Geev Mokryani, and Raed A. Abd-Alhameed. "Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach." Electronics 11, no. 8 (April 15, 2022): 1253. http://dx.doi.org/10.3390/electronics11081253.
Повний текст джерелаReine, Carl, Mirko van der Baan, and Roger Clark. "The robustness of seismic attenuation measurements using fixed- and variable-window time-frequency transforms." GEOPHYSICS 74, no. 2 (March 2009): WA123—WA135. http://dx.doi.org/10.1190/1.3043726.
Повний текст джерелаChan, Lipchen Alex, and Nasser M. Nasrabadi. "An Application of Wavelet-Based Vector Quantization in Target Recognition." International Journal on Artificial Intelligence Tools 06, no. 02 (June 1997): 165–78. http://dx.doi.org/10.1142/s0218213097000098.
Повний текст джерелаLiu, Ken-Hao, Wei-Guang Teng, and Ming-Syan Chen. "Dynamic Wavelet Synopses Management over Sliding Windows in Sensor Networks." IEEE Transactions on Knowledge and Data Engineering 22, no. 2 (February 2010): 193–206. http://dx.doi.org/10.1109/tkde.2009.51.
Повний текст джерелаLiu, Cai Xia. "A New Preprocessing Algorithm of Hand Vein Image." Applied Mechanics and Materials 462-463 (November 2013): 312–15. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.312.
Повний текст джерелаSingh, Omkar, and Ramesh Kumar Sunkaria. "A Unified Approach for Heart Rate Estimation from Electrocardiogram and Arterial Blood Pressure Pulses." Advanced Science, Engineering and Medicine 12, no. 5 (May 1, 2020): 588–92. http://dx.doi.org/10.1166/asem.2020.2556.
Повний текст джерелаДисертації з теми "Wavelet windows"
Bařina, David. "Videokodek - komprese videosekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236699.
Повний текст джерелаБратова, Дар'я Романівна. "Формування вейвлет вікон для фільтрації оптичної інформації". Master's thesis, КиЇв, 2019. https://ela.kpi.ua/handle/123456789/30424.
Повний текст джерелаThe dissertation is dedicated to developing a method for optical information processing. In engineering practice, different classes of transformation - Fourier, Laplace, etc. - are used to investigate the various signals of natural and artificial origin. Since the 1980s, wavelet transform (WF) has been predominantly used for frequency analysis of unsteady signals. Morle and Grossman were the first to do so, analyzing seismic data and coherent quantum states, respectively. The mathematical foundations of the WT were laid down by Meyer, who showed the existence of corresponding functions (wavelets) forming an orthogonal basis in the space L2 (R), that is, in the space of real functions whose square is integrated. Dobeshi made the transition from continuous to discrete WT and developed a class of wavelets that have maximum smoothness at a fixed length of their carrier. Currently, the scope of the WT is the approximation of functions and signals, their filtering and compression, searching for a signal of certain features, and more. The master's thesis consists of four sections. The first section analyzes the main advantages and disadvantages of wavelet and Fourier transforms and the features of their use. Examples of the main types of wavelets are also given. The second section provides a general classification of wavelets and each of them in general. In addition, the general characteristics of various wavelets and their calculation methods are considered. The third section is devoted to the development of a method of forming wavelet windows for filtering optical information. The third section presents the results of an analysis of the previous experimental works that show the possibility of creating synthesized digital nonlinear holograms as wavelet filters. The fourth section is devoted to the development of a startup project "Formation of wavelet windows for filtering optical information" and to analyze the prospects of entering the market from a marketing point of view.
Zhang, Junbo. "EMPIRICAL COMPARISON OF THREE SIGNAL PROCESSING METHODS: ADAPTIVE PERIODOGRAM TECHNIQUE, MORLET WAVELET TRANSFORM, AND ADAPTIVE WINDOWED FOURIER TRANSFORM AND THEIR APPLICATION ON GRAVITY WAVES." Oxford, Ohio : Miami University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=miami1145385180.
Повний текст джерелаCavazzi, Stefano. "Spatial scale analysis of landscape processes for digital soil mapping in Ireland." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/8591.
Повний текст джерелаRen, Peng. "Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/838.
Повний текст джерелаLima, Miguel Francisco Martins de. "Análise dinâmica de vibrações em manipuladores robóticos." Doctoral thesis, 2009. http://hdl.handle.net/10316/10433.
Повний текст джерелаOs manipuladores robóticos apresentam vibrações indesejadas durante o seu funcionamento. Por um lado, estas vibrações resultam de numerosos factores, tais como, folgas, flexibilidades, atritos, não-linearidades e outras causas. Por outro lado, os robôs, ao interagirem com o meio ambiente, geram frequentemente impactos que produzem vibrações que se propagam através de toda a estrutura mecânica. Neste contexto, de modo a reduzir, ou eliminar, o efeito das vibrações e dos impactos, é fundamental estudar as variáveis envolvidas para se poderem definir estratégias adequadas. Nesta ordem de ideias, este trabalho estuda e desenvolve metodologias de análise para aplicações em estruturas de manipulação sujeitas a impactos e a vibrações. As experiências realizadas com o sistema robótico desenvolvido, na presença de impactos, vibrações e na movimentação de líquidos, evidenciaram o comportamento de ordem fraccionária de alguns sinais. A transformada de Fourier com janela, utilizada no estudo dos sinais robóticos, revelou-se uma ferramenta adequada para a análise dos sinais não estacionários, como é o caso dos sinais originados nos fenómenos referidos. Os robôs utilizam uma multiplicidade de sensores de forma a adaptarem-se a perturbações ou a mudanças inesperadas no espaço de trabalho. Os dados assim obtidos podem ser redundantes, uma vez que a mesma informação pode ser captada por dois ou mais sensores. Neste contexto, faz-se um estudo do comportamento do espectro dos sinais e apresenta-se um método de classificação dos sinais que pode contribuir para a optimização da instrumentação utilizada nos sistemas robóticos. No estudo dos sinais robóticos apresentam-se várias experiências suportadas por conceitos da teoria da informação e implementadas através de uma reconstrução do espaço de estados. Assim, determina-se, experimentalmente, uma relação entre os declives das linhas de tendência dos espectros com a dimensão fractal do espaço de estados reconstruído e o correspondente tempo de atraso. Propõem-se ainda dois índices para determinação do grau das folgas em sistemas mecânicos sujeitos a oscilações periódicas. Desenvolve-se também um novo método, baseado na informação mútua, para sintonia da transformada de Fourier com janela.
The operation of robotic manipulators reveals unwanted vibrations. On one hand, these vibrations occur due to several factors, such as, backlash, flexibilities, friction, non-linearities and other effects. On the other hand, the robots, interacting with the environment, generate often impacts that produce vibrations which are propagated through the mechanical structure. In this perspective, in order to adopt adequate strategies for reducing or eliminating the effect of vibrations and impacts, it is important to study the involved variables. Bearing these ideas in mind, this work studies and develops analysis methodologies for applying to mechanical manipulators structures subject to impacts and vibrations. Several experiments are performed with the developed robotic system in the presence of impacts, vibrations, or when carrying liquid containers. Some of the captured signals reveal a fractional order behavior. The windowed Fourier transform is applied in the study of the robotic signals and reveals to be an adequate tool to deal with this type of non stationary signals. The robots use a multiplicity of sensors necessary to deal with the perturbations or with unexpected changes in its work space. Therefore, the data obtained can be redundant because the same type of information can be obtained by two or more sensors. In this context, is established the study of the signal spectra. A sensor classification scheme is developed that can help in the design optimization of the robotic instrumentation. Several experiments are performed for analyzing the robotic signals, based on the information theory, and implemented through the pseudo phase space. An experimental relationship is determined between the slopes of the trendlines spectra, with the fractal dimension of the pseudo phase space and the corresponding time lag. Additionally, two indices are proposed to detect the backlash effect on mechanical systems with periodic oscillations. Finally, a new method based on the mutual information, for tuning the windowed Fourier transform, is presented.
Книги з теми "Wavelet windows"
Scheck, Christian. Wavelab for Windows. [Waldorf, Germany]: Steinberg, 1996.
Знайти повний текст джерелаNathorst-Böös, Ernst. Wavelab for Windows: Operation manual. [Waldorf, Germany]: Steinberg, 1995.
Знайти повний текст джерелаPeters, A. Wavelet Packet Laboratory for Windows (Disc and Book). AK Peters, 1994.
Знайти повний текст джерелаЧастини книг з теми "Wavelet windows"
Kaiser, Gerald. "Windowed Fourier Transforms." In A Friendly Guide to Wavelets, 44–59. Boston: Birkhäuser Boston, 2010. http://dx.doi.org/10.1007/978-0-8176-8111-1_2.
Повний текст джерелаGomes, Jonas, and Luiz Velho. "Windowed Fourier Transform." In From Fourier Analysis to Wavelets, 47–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22075-8_4.
Повний текст джерелаGalan-Hernandez, J. C., V. Alarcon-Aquino, O. Starostenko, and J. M. Ramirez-Cortes. "Fovea Window for Wavelet-Based Compression." In Lecture Notes in Electrical Engineering, 661–72. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3535-8_55.
Повний текст джерелаFu, Yingxiong, Uwe Kähler, and Paula Cerejeiras. "The Balian–Low Theorem for the Windowed Clifford–Fourier Transform." In Quaternion and Clifford Fourier Transforms and Wavelets, 299–319. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0603-9_15.
Повний текст джерелаLiu, Yun-Xia, Yang Yang, and Ngai-Fong Law. "Accurate Prior Modeling in the Locally Adaptive Window-Based Wavelet Denoising." In Intelligent Computing Theories and Application, 523–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42294-7_47.
Повний текст джерелаLépine, Sébastien. "Wavelet Analysis of Variable Wolf-Rayet Emission Lines." In Wolf-Rayet Stars: Binaries, Colliding Winds, Evolution, 60–61. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0205-6_13.
Повний текст джерелаBahri, Mawardi. "A Generalized Windowed Fourier Transform in Real Clifford Algebra Cl 0,n." In Quaternion and Clifford Fourier Transforms and Wavelets, 285–98. Basel: Springer Basel, 2013. http://dx.doi.org/10.1007/978-3-0348-0603-9_14.
Повний текст джерелаWang, Lin, Yongping Li, Hongzhou Zhang, and Chengbo Wang. "A Novel 2D Gabor Wavelets Window Method for Face Recognition." In Multimedia Content Representation, Classification and Security, 497–504. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11848035_66.
Повний текст джерелаHuptych, Michal, and Lenka Lhotská. "ECG Beat Classification Using Feature Extraction from Wavelet Packets of R Wave Window." In IFMBE Proceedings, 2257–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_600.
Повний текст джерелаQin, Huayang, Zengqiang Chen, Mingwei Sun, and Qinglin Sun. "Application of Real-Time Wavelet De-noising Based on Sliding Window in LADRC." In Lecture Notes in Electrical Engineering, 1–10. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8458-9_1.
Повний текст джерелаТези доповідей конференцій з теми "Wavelet windows"
Wee Sun Lee. "Trees, windows and tiles for wavelet image compression." In Proceedings DCC 2000. Data Compression Conference. IEEE, 2000. http://dx.doi.org/10.1109/dcc.2000.838168.
Повний текст джерелаLuo, Bing, and Yue-Hua Gao. "Knowledge inductive search based machine vision inspecting windows optimization." In 2012 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2012. http://dx.doi.org/10.1109/icwapr.2012.6294780.
Повний текст джерелаResnikoff, Howard L. "Perfect reconstruction and wavelet matrix windows for harmonic analysis." In SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, edited by Andrew G. Tescher. SPIE, 1994. http://dx.doi.org/10.1117/12.186541.
Повний текст джерелаMing-Xin Zhang, Jin-Long Zheng, Hua Li, and Jin-Yi Chang. "A novel shot segmentation algorithm based on grid-mapping dynamic windows in compressed videos." In 2009 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2009. http://dx.doi.org/10.1109/icwapr.2009.5207417.
Повний текст джерелаP. Shatilo, A. "Direct estimation of the wavelet phase spectrum via short overlapping windows." In 56th EAEG Meeting. European Association of Geoscientists & Engineers, 1994. http://dx.doi.org/10.3997/2214-4609.201410099.
Повний текст джерелаSrinivasan, M., S. C. Prema, and S. A. Durai. "Improved MAP Estimation of Variance Through Arbitrary Windows For Wavelet Denoising." In 2005 Annual IEEE India Conference - Indicon. IEEE, 2005. http://dx.doi.org/10.1109/indcon.2005.1590117.
Повний текст джерелаMooney, James A., and Andres Soom. "Optimal Windows for the Time-Frequency Analysis of Arbitrary Swept Frequency Signals." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0382.
Повний текст джерелаShirai, Shota, Masaki Nakai, Takeshi Kumaki, Tomohiro Fujita, Mamoru Nakanishi, and Takeshi Ogura. "Morphological wavelet transform using multiple directional sampling windows on cellular hardware platform." In 2011 IEEE 9th International New Circuits and Systems Conference (NEWCAS). IEEE, 2011. http://dx.doi.org/10.1109/newcas.2011.5981243.
Повний текст джерелаKulaglic, Ajla, and Burak Berk Ustundag. "Stock Price Forecast using Wavelet Transformations in Multiple Time Windows and Neural Networks." In 2018 3rd International Conference on Computer Science and Engineering (UBMK). IEEE, 2018. http://dx.doi.org/10.1109/ubmk.2018.8566614.
Повний текст джерелаOnchis, Darian M., and Simone Zappala. "Constructive Realizable Multi-resolution Wavelet-Like Systems Based on Multi-windows Spline-Type Spaces." In 2017 19th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2017. http://dx.doi.org/10.1109/synasc.2017.00027.
Повний текст джерелаЗвіти організацій з теми "Wavelet windows"
Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.
Повний текст джерела