Relevant bibliographies by topics / Wavelet windows

Academic literature on the topic 'Wavelet windows'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Wavelet windows"

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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.

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An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.
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COLAK, 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.

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In this study, we have presented a new approach to separate noisy components and to characterize frequency-energy variability for local real-time noisy earthquakes where epicentral distance is 0–10°. This approach is based on wavelet transform and deals with energy variations in different frequency bands. All records have been decomposed to approximation and detail components with using overlapping window design and wavelet transform. Energy components of each window were determined and highest energy component has been selected in all windows. When selected energy values have been associated in a vector, two different types of frequency-energy characteristics which include critical points to detect P (longitudinal) and S (transverse) waves have been obtained.
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Eom, 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.

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Scheuer, 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.

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The autocepstrum of a reflection seismogram is defined by the cepstrum of its autocorrelation function. Using the autocepstrum extends the basic deconvolution method for removing a minimum‐phase source wavelet to unmask subsurface reflectivity. When we record only the seismic trace and assume a minimumphase source wavelet, deconvolution reduces to estimating the wavelet autocorrelation. In practice, a portion of the seismic trace autocorrelation is used as an estimate of the wavelet autocorrelation. This can be justified by assuming a random reflectivity series with a white power spectrum. However, in cases where the reflectivity spectrum is not white, a preferred wavelet autocorrelation may be obtained by low‐pass windowing the trace autocepstrum. This approach liberates the selection of various deconvolution parameters such as filter length and design window length that are typically chosen to reinforce the assumption of a white reflectivity spectrum. For problems that require short, deconvolution‐filter design windows, and thus nonwhite reflectivity spectra, windowing the trace autocepstrum is an appropriate alternative to the conventional practice of windowing the trace autocorrelation.
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Hussein, 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.

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This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages.
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Reine, 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.

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Frequency-based methods for measuring seismic attenuation are used commonly in exploration geophysics. To measure the spectrum of a nonstationary seismic signal, different methods are available, including transforms with time windows that are either fixed or systematically varying with the frequency being analyzed. We compare four time-frequency transforms and show that the choice of a fixed- or variable-window transform affects the robustness and accuracy of the resulting attenuation measurements. For fixed-window transforms, we use the short-time Fourier transform and Gabor transform. The S-transform and continuous wavelet transform are analyzed as the variable-length transforms. First we conduct a synthetic transmission experiment, and compare the frequency-dependent scattering attenuation to the theoretically predicted values. From this procedure, we find that variable-window transforms reduce the uncertainty and biasof the resulting attenuation estimate, specifically at the upper and lower ends of the signal bandwidth. Our second experiment measures attenuation from a zero-offset reflection synthetic using a linear regression of spectral ratios. Estimates for constant-[Formula: see text] attenuation obtained with the variable-window transforms depend less on the choice of regression bandwidth, resulting in a more precise attenuation estimate. These results are repeated in our analysis of surface seismic data, whereby we also find that the attenuation measurements made by variable-window transforms have a stronger match to their expected trend with offset. We conclude that time-frequency transforms with a systematically varying time window, such as the S-transform and continuous wavelet transform, allow for more robust estimates of seismic attenuation. Peaks and notches in the measured spectrum are reduced because the analyzed primary signal is better isolated from the coda, and because of high-frequency spectral smoothing implicit in the use of short-analysis windows.
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Chan, 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.

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An automatic target recognition (ATR) classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics.
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Liu, 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.

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Liu, 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.

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Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.
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Singh, 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.

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The objective of this manuscript is to propose a unique methodology for heart rate estimation derived from Electrocardiogram (ECG) or arterial blood pressure (abp) signal. This methodology relies on the identification of a signal's fundamental frequency by use of empirical wavelet analysis, followed by peak identification within windows based on pseudo-periodic assumption. The proposed methodology is based on the concept that the most of the cardiovascular signals are quasi-periodic in nature. The proposed technique estimates the fundamental frequency of the signal from its corresponding Fourier spectrum using empirical wavelet transform and then utilizes a search window for locating the peaks in the corresponding signal which identifies the R peaks in ECG or Systolic peaks in blood pressure pulses. This approach was validated on 100 recordings of the computing in cardiology challenge 2014 training data set and performance parameters were compared with methods running only on ECG or ABP signals independently.
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Dissertations / Theses on the topic "Wavelet windows"

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Bařina, David. "Videokodek - komprese videosekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236699.

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This thesis deals with modern methods of a lossy still image and video compression. Wavelet transformation and SPIHT algorithm also belong to these methods. In second half of this thesis, a videocodec is implemented based on acquired knowledge. This codec uses Daubechies wavelets to analyse an image. Afterwards there is a modified SPIHT algorithm applied on gained coefficients. A lot of effort was put in order to optimize this computation. It is possible to use the created codec in Video for Windows, DirectShow and FFmpeg multimedia frameworks. At the end of this thesis, commonly used codecs are compared with newly created one.
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Братова, Дар'я Романівна. "Формування вейвлет вікон для фільтрації оптичної інформації." Master's thesis, КиЇв, 2019. https://ela.kpi.ua/handle/123456789/30424.

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Дисертаційна робота присвячена розробці методу для оптичної обробки інформації. В інженерній практиці для дослідження різноманітних сигналів природного та штучного походження застосовуються різні класи перетворень – Фур’є, Лапласа тощо. З 80-х років минулого століття для частотночасового аналізу нестаціонарних сигналів переважно використовують вейвлетперетворення (ВП). Першими це зробили Морле та Гроссман, займаючись аналізом сейсмічних даних та когерентними квантовими станами відповідно. Математичні засади ВП було закладено Мейєром, який показав існування відповідних функцій (вейвлетів), що утворюють ортогональний базис в просторі L2(R), тобто в просторі дійсних функцій, квадрат котрих є інтегрованим. Добеші здійснила перехід від неперервного до дискретного ВП та розробила клас вейвлетів, що мають максимальну гладкість при фіксованій довжині свого носія. Наразі область застосування ВП – наближення функцій і сигналів, їх фільтрація та стиснення, пошук в сигналі певних особливостей тощо. Магістерська дисертація складається з чотирьох розділів. У першому розділі проаналізовано основні переваги і недоліки вейвлет та Фур’є перетворень та особливості їх використання. Також приведено приклади основних типів вейвлетів. У другому розділі приведено загальну класифікацію вейвлетів та кожного з загальних окремо. Окрім цього розглянуто узагальнені характеристики різноманітних вейвлетів та методи їх розрахунку. Третій розділ присвячено розробці метода формування вейвлет вікон для фільтраціі оптичної інформації. В третьому підрозділі продемонстровані результати аналізу експериментальних робіт попередників, які показують можливість створення синтезованих цифрових нелінійних голограм у якості вейвлет-фільтрів. Четвертий розділ присвячено розробці стартап-проекту «Формування вейвлет вікон для фільтрації оптичної інформації» і аналізу перспектив входження розробки на ринок з маркетологічної точки зору.
The 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.
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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.

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Cavazzi, 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.

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Soil is one of the most precious resources on Earth because of its role in storing and recycling water and nutrients essential for life, providing a variety of ecosystem services. This vulnerable resource is at risk from degradation by erosion, salinity, contamination and other effects of mismanagement. Information from soil is therefore crucial for its sustainable management. While the demand for soil information is growing, the quantity of data collected in the field is reducing due to financial constraints. Digital Soil Mapping (DSM) supports the creation of geographically referenced soil databases generated by using field observations or legacy data coupled, through quantitative relationships, with environmental covariates. This enables the creation of soil maps at unexplored locations at reduced costs. The selection of an optimal scale for environmental covariates is still an unsolved issue affecting the accuracy of DSM. The overall aim of this research was to explore the effect of spatial scale alterations of environmental covariates in DSM. Three main targets were identified: assessing the impact of spatial scale alterations on classifying soil taxonomic units; investigating existing approaches from related scientific fields for the detection of scale patterns and finally enabling practitioners to find a suitable scale for environmental covariates by developing a new methodology for spatial scale analysis in DSM. Three study areas, covered by detailed reconnaissance soil survey, were identified in the Republic of Ireland. Their different pedological and geomorphological characteristics allowed to test scale behaviours across the spectrum of conditions present in the Irish landscape. The investigation started by examining the effects of scale alteration of the finest resolution environmental covariate, the Digital Elevation Model (DEM), on the classification of soil taxonomic units. Empirical approaches from related scientific fields were subsequently selected from the literature, applied to the study areas and compared with the experimental methodology. Wavelet analysis was also employed to decompose the DEMs into a series of independent components at varying scales and then used in DSM analysis of soil taxonomic units. Finally, a new multiscale methodology was developed and evaluated against the previously presented experimental results. The results obtained by the experimental methodology have proved the significant role of scale alterations in the classification accuracy of soil taxonomic units, challenging the common practice of using the finest available resolution of DEM in DSM analysis. The set of eight empirical approaches selected in the literature have been proved to have a detrimental effect on the selection of an optimal DEM scale for DSM applications. Wavelet analysis was shown effective in removing DEM sources of variation, increasing DSM model performance by spatially decomposing the DEM. Finally, my main contribution to knowledge has been developing a new multiscale methodology for DSM applications by combining a DEM segmentation technique performed by k-means clustering of local variograms parameters calculated in a moving window with an experimental methodology altering DEM scales. The newly developed multiscale methodology offers a way to significantly improve classification accuracy of soil taxonomic units in DSM. In conclusion, this research has shown that spatial scale analysis of environmental covariates significantly enhances the practice of DSM, improving overall classification accuracy of soil taxonomic units. The newly developed multiscale methodology can be successfully integrated in current DSM analysis of soil taxonomic units performed with data mining techniques, so advancing the practice of soil mapping. The future of DSM, as it successfully progresses from the early pioneering years into an established discipline, will have to include scale and in particular multiscale investigations in its methodology. DSM will have to move from a methodology of spatial data with scale to a spatial scale methodology. It is now time to consider scale as a key soil and modelling attribute in DSM.
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Ren, 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.

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Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.
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Lima, Miguel Francisco Martins de. "Análise dinâmica de vibrações em manipuladores robóticos." Doctoral thesis, 2009. http://hdl.handle.net/10316/10433.

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Tese de doutoramento em Engenharia Electrotécnica (Instrumentação e Controlo) apresentada à Fac. Ciências e Tecnologia da Univ. Coimbra
Os 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.
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Books on the topic "Wavelet windows"

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Scheck, Christian. Wavelab for Windows. [Waldorf, Germany]: Steinberg, 1996.

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Nathorst-Böös, Ernst. Wavelab for Windows: Operation manual. [Waldorf, Germany]: Steinberg, 1995.

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Peters, A. Wavelet Packet Laboratory for Windows (Disc and Book). AK Peters, 1994.

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Book chapters on the topic "Wavelet windows"

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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.

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Gomes, 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.

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Galan-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.

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Fu, 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.

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Liu, 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.

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Lépine, 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.

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Bahri, 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.

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Wang, 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.

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Huptych, 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.

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Qin, 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.

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Conference papers on the topic "Wavelet windows"

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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.

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Luo, 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.

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Resnikoff, 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.

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Ming-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.

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P. 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.

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Srinivasan, 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.

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Mooney, 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.

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Abstract In noise and vibration analysis, as well as in many other engineering applications, it may be necessary to extract or analyze signals with time-varying frequency components. Examples include start-up and shut-down of rotating machinery, transient structural vibrations, vehicle passing noise, and speech analysis. Both Short-Time Fourier Transforms (STFT), representing a set of non-causal filters of constant bandwidth, and Wavelet Transforms, representing a set of non-causal filters of constant Q or constant percent bandwidth, have been used for such Joint Time Frequency Analysis (JTFA). In the present work, an arbitrary swept frequency signal is approximated locally, in time, by a linearized frequency sweep. We show that an optimal time window can be identified which, at a given frequency, is inversely proportional to the square root of the instantaneous rate of change of frequency. We find that the constant bandwidth of the STFT and the constant-Q of the Wavelet transform represent extreme cases which are each optimal for certain types of signals. In between the two extremes there lies a continuous range of variation of the effective bandwidth with frequency. Many important types of signals require analysis window variation in this range between STFT and Wavelet analysis. The paper concludes with some simple rules for optimizing the variation of the analysis window with frequency for various types of signals.
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Shirai, 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.

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Kulaglic, 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.

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Onchis, 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.

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Reports on the topic "Wavelet windows"

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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.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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