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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Yu, Zhou, George A. McMechan, Phil D. Anno, and John F. Ferguson. "Wavelet‐transform‐based prestack multiscale Kirchhoff migration." GEOPHYSICS 69, no. 6 (November 2004): 1505–12. http://dx.doi.org/10.1190/1.1836823.
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We propose a Kirchhoff‐style algorithm that migrates coefficients obtained by wavelet decomposition of seismic traces over time. Wavelet‐based prestack multiscale Kirchhoff migration involves four steps: wavelet decomposition of the seismic data, thresholding of the resulting wavelet coefficients, multiscale Kirchhoff migration, and image reconstruction from the multiscale images. The migration procedure applied to each wavelet scale is the same as conventional Kirchhoff migration but operates on wavelet coefficients. Since only the wavelet coefficients are migrated, the cost of wavelet‐based migration is reduced compared to that of conventional Kirchhoff migration. Kirchhoff migration of wavelet‐decomposed data, followed by wavelet reconstruction, is kinematically equivalent to and yields similar migrated signal shapes and amplitudes as conventional Kirchhoff migration when data at all wavelet scales are included. The decimation in the conventional discrete pyramid wavelet decomposition introduces a translation‐variant phase distortion in the wavelet domain. This phase distortion is overcome by using a stationary wavelet‐transform rather than the conventional discrete wavelet‐transform of the data to be migrated. A wavelet reconstruction operator produces a single composite broadband migrated space‐domain image from multiscale images. Multiscale images correspond to responses in different frequency windows, and migrating the data at each scale has a different cost. Migrating some, or only one, of the individual scale data sets considerably reduces the computational cost of the migration. Successful 2D tests are shown for migrations of synthetic data for a point‐diffractor model, a multilayer model, and the Marmousi model.
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KUMAR, ASHWANI, D. P. AGRAWAL, and S. D. JOSHI. "MULTIRESOLUTION FORECASTING FOR US RETAILING USING WAVELET DECOMPOSITIONS." International Journal of Wavelets, Multiresolution and Information Processing 01, no. 04 (December 2003): 449–63. http://dx.doi.org/10.1142/s0219691303000281.
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In this paper we propose a simple forecasting strategy which exploits the multiresolution property of the wavelet transform. US aggregate retail sales data have strong trend and seasonal patterns, providing a good testing ground for the proposed forecasting method. First a wavelet transform is used to decompose the time series into varying scales of resolution so that the underlying temporal structures of the original time series become more tractable; the decomposition is additive in details and approximation. Then a forecasting engine (neural network or fuzzy inference system) is trained on each of the relevant resolution scales, and individual wavelet scale forecasts are recombined to form the overall forecast. Substantial information in both the dynamic nonlinear trend and seasonal patterns of the time series is efficiently exploited: we choose short past windows for the inputs to the forecasting engines at lower scales and long past windows at higher scales. The forecasting engines learn the mapping hierarchically: using a scale-recursive strategy, we combine only those scales where significant events are detected. Univariate simulation results on US aggregate retailing indicate that the proposed method fares favourably in relation to forecasting results obtained by training a neural network on original time series. Multivariate simulation results obtained by including structural components inflation, recession, interest rates, unemployment, show improvement in sales-trend forecast.
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Zheng, Peng, Bin Liu, and Zhongli Zhou. "Memories of the Gold Foreign Exchange Market Based on a Moving V-Statistic and Wavelet-Based Multiresolution Analysis." Discrete Dynamics in Nature and Society 2018 (August 23, 2018): 1–7. http://dx.doi.org/10.1155/2018/3051632.
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Memory in finance is the foundation of a well-established forecasting model, and new financial theory research shows that the stochastic memory model depends on different time windows. To accurately identify the multivariate long memory model in the financial market, this paper proposes the concept of a moving V-statistic on the basis of a modified R/S method to determine whether the time series has a long-range dependence and subsequently to apply wavelet-based multiresolution analysis to study the multifractality of the financial time series to determine the initial data windows. Finally, we check the moving V-statistic estimation in wavelet analysis in the same condition; the paper selects the volatilities of the gold foreign exchange rates to evaluate the moving V-statistic. According to the results, the method of testing memory established in this paper can identify the breakpoint of the memories effectively. Furthermore, this method can provide support for forecasting returns in the financial market.
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Laxague, Nathan J. M., Brian K. Haus, David G. Ortiz-Suslow, and Hans C. Graber. "Quantifying Highly Variable Air–Sea Momentum Flux Using Wavelet Analysis." Journal of Atmospheric and Oceanic Technology 35, no. 9 (September 2018): 1849–63. http://dx.doi.org/10.1175/jtech-d-18-0064.1.
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AbstractSurface wind stress is a crucial driver of upper-ocean processes, impacting air–sea gas flux, wind-wave development, and material transport. Conventional eddy covariance (EC) processing requires imposing a fixed averaging window on the wind velocity time series in order to estimate the downward flux of momentum. While this method has become the standard means of directly measuring the wind stress, the use of a fixed averaging interval inherently constrains one’s ability to resolve transient signals that may have net effects on the air–sea interactions. Here we utilize the wavelet transform to develop a new technique for directly quantifying the wind stress magnitude from the wavelet coscalogram products. The time averages of these products evaluated at the scale of maximum amplitude are highly correlated with the EC estimates (R2 = 0.99; 5-min time windows), suggesting that stress is particularly sensitive to the dominant turbulent eddies. By taking advantage of the new method’s high temporal resolution, transient wind forcing and its dominant scales may be explicitly computed and analyzed. This technique will allow for more general investigations into air–sea dynamics under nonstationary or spatially inhomogeneous conditions, such as within the nearshore region.
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Pinnegar, C. Robert, and Lalu Mansinha. "The S‐transform with windows of arbitrary and varying shape." GEOPHYSICS 68, no. 1 (January 2003): 381–85. http://dx.doi.org/10.1190/1.1543223.
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The S‐transform is an invertible time‐frequency spectral localization technique which combines elements of wavelet transforms and short‐time Fourier transforms. In previous usage, the frequency dependence of the analyzing window of the S‐transform has been through horizontal and vertical dilations of a basic functional form, usually a Gaussian. In this paper, we present a generalized S‐transform in which two prescribed functions of frequency control the scale and the shape of the analyzing window, and apply it to determining P‐wave arrival time in a noisy seismogram. The S‐transform is also used as a time‐frequency filter; this helps in determining the sign of the P arrival.
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ACHARYYA, MAUSUMI, and MALAY K. KUNDU. "EXTRACTION OF NOISE TOLERANT, GRAY-SCALE TRANSFORM AND ROTATION INVARIANT FEATURES FOR TEXTURE SEGMENTATION USING WAVELET FRAMES." International Journal of Wavelets, Multiresolution and Information Processing 06, no. 03 (May 2008): 391–417. http://dx.doi.org/10.1142/s0219691308002252.
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In this paper, we propose a texture feature extraction scheme at multiple scales and discuss the issues of rotation and gray-scale transform invariance as well as noise tolerance of a texture analysis system. The nonseparable discrete wavelet frame analysis is employed which gives an overcomplete wavelet decomposition of the image. The texture is decomposed into a set of frequency channels by a circularly symmetric wavelet filter, which in essence gives a measure of edge magnitudes of the texture at different scales. The texture is characterized by local energies over small overlapping windows around each pixel at different scales. The features so extracted are used for the purpose of multi-texture segmentation. A simple clustering algorithm is applied to this signature to achieve the desired segmentation. The performance of the segmentation algorithm is evaluated through extensive testing over various types of test images.
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Jain, Paras, and Vipin Tyagi. "Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain." International Journal of Computer Applications 114, no. 16 (March 18, 2015): 33–45. http://dx.doi.org/10.5120/20065-2141.
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Mor, Y., and A. Lev-Tov. "Analysis of Rhythmic Patterns Produced by Spinal Neural Networks." Journal of Neurophysiology 98, no. 5 (November 2007): 2807–17. http://dx.doi.org/10.1152/jn.00740.2007.
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A network of spinal neurons known as central pattern generator (CPG) produces the rhythmic motor patterns required for coordinated swimming, walking, and running in mammals. Because the output of this network varies with time, its analysis cannot be performed by statistical methods that assume data stationarity. The present work uses short-time Fourier (STFT) and wavelet-transform (WT) algorithms to analyze the nonstationary rhythmic signals produced in isolated spinal cords of neonatal rats during activation of the CPGs. The STFT algorithm divides the time series into consecutive overlapping or nonoverlapping windows and repeatedly applies the Fourier transform across the signal. The WT algorithm decomposes the signal using a family of wavelets varying in scale, resulting in a set of wavelet coefficients presented onto a continuous frequency range over time. Our studies revealed that a Morlet WT algorithm was the tool of choice for analyzing the CPG output. Cross-WT and wavelet coherence were used to determine interrelations between pairs of time series in time and frequency domain, while determining the critical values for statistical significance of the coherence spectra using Monte Carlo simulations of white-noise series. The ability of the cross-Morlet WT and cross-WT coherence algorithms to efficiently extract the rhythmic parameters of complex nonstationary output of spinal pattern generators over a wide range of frequencies with time is demonstrated in this work under different experimental conditions. This ability can be exploited to create a quantitative dynamic portrait of experimental and clinical data under various physiological and pathological conditions.
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MOVIT, STEVEN M. "FLARE DETECTION USING WAVELET DENOISING AND SWIFT BAT DATA FOR NEUTRINO MULTIMESSENGER STUDIES." International Journal of Modern Physics D 18, no. 10 (October 2009): 1499–503. http://dx.doi.org/10.1142/s0218271809015552.
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Theory predicts a neutrino signal is possible from jets from active galactic nuclei (AGN) if there is hadronic acceleration in the jets. Flaring activity in X-rays and γ-rays from blazars could indicate similar activity in neutrinos in the jets. Short duration flares, on the scale of hours, could allow for narrow time windows to be searched for a Neutrino signal above the background of atmospheric neutrinos. A wavelet technique will be run over orbit-by-orbit Swift BAT (Burst Alert Telescope) data to identify flaring periods to accurately determine start and end times. The wavelet technique is notable as it accounts for both uneven time sampling and varying error bars across a time series.
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Taroudakis, Michael, Costas Smaragdakis, and N. Ross Chapman. "Denoising Underwater Acoustic Signals for Applications in Acoustical Oceanography." Journal of Computational Acoustics 25, no. 02 (January 25, 2017): 1750015. http://dx.doi.org/10.1142/s0218396x17500151.
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A method for denoising underwater acoustic signals used in applications of acoustical oceanography is presented. The method has been introduced for imaging denoising and has been modified to be applied with acoustic signals. The method keeps the energy significant part of the raw signal and reduces the effects of noise by comparing overlapping signal windows and keeping components which resemble true signal energy. It is shown by means of characteristic experiments in connection with a statistical signal characterization scheme based on wavelet transform, that using the statistical features of the wavelet sub-band coefficients of the denoised signal, tomography or geoacoustic inversions lead to a reliable estimation of the parameters of a marine environment.
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Bartolini, P., G. Calcagnini, F. Censi, R. Macioce, A. Michelucci, S. Poli, and V. Barbaro. "Effects of Subthreshold Shocks on Wavelet Propagation during Atrial Fibrillation in Humans." Methods of Information in Medicine 43, no. 01 (2004): 39–42. http://dx.doi.org/10.1055/s-0038-1633831.
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Summary Objectives: Our objectives are: first to investigate the effects of internal cardioversion energies on the wave fronts propagation in the right atrium immediately after the energy delivery; second, to track the time course of these effects. Methods: The study is based on a measure of organization of the endoatrial electrograms obtained by a multipolar basket catheter inserted in the right atrium. We estimated the level of organization by computing the percentage of points laying on the signal baseline (i.e., number of occurrences, NO). NO values were computed on two-second long windows. Six non-overlapped windows were selected, one just before and five just after the last unsuccessful shock. Results: Immediately after the shock most of the patients exhibited an increase in the organization patterns. This increase was more evident in those patients with rather disorganized patterns and higher energy threshold. This effect fades within a few seconds after the shock delivery. Conclusions: Our data confirm the idea that the electrical shock causes a widespread extinction of electrical wavefronts, which regenerates after the shock. Since an increase of organization may lead to a reduction of energy threshold, a potential application of these findings might consist in the delivery of multiple subthreshold shocks instead of a single one.
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Gilbert, Markus Aminius, Nabila Husna Shabrina, Andre Wijaya, and Jeremy Pratama Wijaya. "Perbandingan Pola Sinyal Penyakit Myocardial Infarction dengan Jantung Normal Menggunakan Metode Wavelet Symlet." Ultima Computing : Jurnal Sistem Komputer 12, no. 1 (July 2, 2020): 49–56. http://dx.doi.org/10.31937/sk.v12i1.1631.
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Pada sinyal kardiografi manusia sehat maupun pengidap myocardial infarction mengandung banyak derau. Untuk itu perlu pengolahan sinyal yang sesuai sehingga informasi yang dikandung oleh sinyal tersebut dapat dideteksi dengan mudah.
Tahapan penelitian meliputi pencarian data, pra pemrosesan, pengolahan sinyal dengan metode denoise Wavelet Symlet, dan perbandingan kualitatif pola sinyal kardiografi hasil pengolahan sinyal manusia normal dengan pengidap penyakit myocardial infarction.
Untuk menghilangkan derau pada sinyal EKG, metode denoise menggunakan Wavelet Symlet terbukti lebih baik daripada FIR filter Hamming Windows. MATLAB menjadi salah satu pilihan perangkat lunak yang dapat digunakan dalam memproses sinyal kardiografi dengam metode denoise Wavelet Symlet, terbukti memiliki reliabilitas yang yang cukup tinggi berdasarkan analisis kualitatif. Percobaan ini juga membuktikan bahwa pola sinyal kardiografi manusia dengan gangguan myocardial infarction cukup acak dan dan berbeda antara satu pasien dengan pasien lainnya. Namun, pola detakan jantung yang lebih lambat, amplitudo yang lebih besar, dan kelainan pada bagian sinyal P,T serta jarak Q-S yang regang dapat menjadi acuan diagnosa seseorang mengalami gangguan myocardial infarction.
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Cheng, Zixiang, Wei Chen, Yangkang Chen, Ying Liu, Wei Liu, Huijian Li, and Runfei Yang. "Application of bi-Gaussian S-transform in high-resolution seismic time-frequency analysis." Interpretation 5, no. 1 (February 1, 2017): SC1—SC7. http://dx.doi.org/10.1190/int-2016-0041.1.
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The S-transform is one of the most widely used methods of time-frequency analysis. It combines the respective advantages of the short-time Fourier transform and wavelet transforms with scale-dependent resolution using Gaussian windows, scaled inversely with frequency. One of the problems with the traditional symmetric Gaussian window is the degradation of time resolution in the time-frequency spectrum due to the long front taper. We have studied the performance of an improved S-transform with an asymmetric bi-Gaussian window. The asymmetric bi-Gaussian window can obtain an increased time resolution in the front direction. The increased time resolution can make event picking high resolution, which will facilitate an improved time-frequency characterization for oil and gas trap prediction. We have applied the slightly modified bi-Gaussian S-transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to indicate the superior performance of the bi-Gaussian S-transform in analyzing nonstationary signal components, hydrocarbon reservoir predictions, and paleochannels delineations with an obviously higher resolution.
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Espinoza, Christian, and Juan Gorigoitía. "An application of Rolling chaos 0-1 test on Stock Market." Estudios de Administración 20, no. 1 (February 3, 2020): 37. http://dx.doi.org/10.5354/0719-0816.2013.56390.
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In this paper we apply a rolling 0-1 test for chaos on different stock market indices returns in the world, considering different time period windows to capture the effects of adding new information. A rolling sample is defined for each index and at the same time, wavelet denoising has been employed since approximately 1995 to the end of 2012. Empirical evidence of continuous chaotic behavior for all indices is found.
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Peng-Lang Shui. "Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain." IEEE Signal Processing Letters 12, no. 10 (October 2005): 681–84. http://dx.doi.org/10.1109/lsp.2005.855555.
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Huang, Wei, and Guo Jing He. "Application of Wavelet Transform to Modal Parameter Identification of the Concrete-Filled Steel Tube Arch Bridge." Advanced Materials Research 250-253 (May 2011): 2446–50. http://dx.doi.org/10.4028/www.scientific.net/amr.250-253.2446.
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It’s important to identify structural modal parameter in time and accurately for structural health monitoring and damage identification. Wavelet analysis is one of the various kinds of identification methods, which has been used in linear and nonlinear system response data since it can decompose signals simultaneously both in time-domain and frequency-domain with adaptive windows. In this paper, taking Bariba Bridge as an example, the modal analysis results obtained from the finite element model are compared with those estimated from the wavelet transform method. Good coincidence of results can be observed, which demonstrates that the built-up finite element model reflects the bridge’s real dynamic properties, and can serve as a baseline model for its dynamic response analysis under complicated excitations, long-term health monitoring and structural service state assessment.
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Mr. Rahul Sharma. "Modified Golomb-Rice Algorithm for Color Image Compression." International Journal of New Practices in Management and Engineering 2, no. 01 (March 31, 2013): 17–21. http://dx.doi.org/10.17762/ijnpme.v2i01.13.
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The memory required to store the color image is more. We have reduced the memory requirements using Golomb-rice algorithm. Golomb-rice algorithm consists of the following two steps. In Golomb-Rice algorithm the first step is to compress the image using discrete wavelet transform. By using DWT compression the 8 × 8 image is converted into m × n sub-windows and it is converted into raster file format for producing m × n-1 differential data. Encoding is done by using Golomb-Rice coding. After encoding, the process length, code word and size are calculated by using GR coding.In the second step decoding is done by GR coding based on the obtained length and code word. After that decoded image is decompressed in order to get the original image by using the inverse discrete wavelet transform.
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Ventosa, Sergi, Sylvain Le Roy, Iréne Huard, Antonio Pica, Hérald Rabeson, Patrice Ricarte, and Laurent Duval. "Adaptive multiple subtraction with wavelet-based complex unary Wiener filters." GEOPHYSICS 77, no. 6 (November 1, 2012): V183—V192. http://dx.doi.org/10.1190/geo2011-0318.1.
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Adaptive subtraction is a key element in predictive multiple-suppression methods. It minimizes misalignments and amplitude differences between modeled and actual multiples, and thus reduces multiple contamination in the data set after subtraction. Due to the high crosscorrelation between their waveforms, the main challenge resides in attenuating multiples without distorting primaries. As they overlap on a wide frequency range, we split this wide-band problem into a set of more tractable narrow-band filter designs, using a 1D complex wavelet frame. This decomposition enables a single-pass adaptive subtraction via complex, single-sample (unary) Wiener filters, consistently estimated on overlapping windows in a complex wavelet transformed domain. Each unary filter compensates for amplitude differences within its frequency support, and can correct small and large misalignment errors through phase and integer delay corrections. This approach greatly simplifies the matching filter estimation and, despite its simplicity, narrows the gap between 1D and standard adaptive 2D methods on field data.
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Zhou, Zuo-feng, and Peng-lang Shui. "Wavelet-Based Image Denoising via Doubly Local Wiener Filtering Using Directional Windows and Mathematical Morphology." Journal of Electronics & Information Technology 30, no. 4 (March 11, 2011): 885–88. http://dx.doi.org/10.3724/sp.j.1146.2006.01453.
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Shui, Peng-lang, and Yong-Bo Zhao. "Image denoising algorithm using doubly local Wiener filtering with block-adaptive windows in wavelet domain." Signal Processing 87, no. 7 (July 2007): 1721–34. http://dx.doi.org/10.1016/j.sigpro.2007.01.021.
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Hoar Siki, Yovinia Carmeneja, and Natalia Magdalena Rafu Mamulak. "Time-frequency analysis on gong timor music using short-time fourier transform and continuous wavelet transform." International Journal of Advances in Intelligent Informatics 3, no. 3 (December 1, 2017): 146. http://dx.doi.org/10.26555/ijain.v3i3.114.
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Time-Frequency Analysis on Gong Timor Music has an important role in the application of signal-processing music such as tone tracking and music transcription or music signal notation. Some of Gong characters is heard by different ways of forcing Gong himself, such as how to play Gong based on the Player’s senses, a set of Gong, and by changing the tempo of Gong instruments. Gong's musical signals have more complex analytical criteria than Western music instrument analysis. This research uses a Gong instrument and two notations; frequency analysis of Gong music frequency compared by the Short-time Fourier Transform (STFT), Overlap Short-time Fourier Transform (OSTFT), and Continuous Wavelet Transform (CWT) method. In the STFT and OSTFT methods, time-frequency analysis Gong music is used with different windows and hop size while CWT method uses Morlet wavelet. The results show that the CWT is better than the STFT methods.
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Jarchi, Delaram, Dario Salvi, Lionel Tarassenko, and David Clifton. "Validation of Instantaneous Respiratory Rate Using Reflectance PPG from Different Body Positions." Sensors 18, no. 11 (October 31, 2018): 3705. http://dx.doi.org/10.3390/s18113705.
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Respiratory rate (RR) is a key parameter used in healthcare for monitoring and predicting patient deterioration. However, continuous and automatic estimation of this parameter from wearable sensors is still a challenging task. Various methods have been proposed to estimate RR from wearable sensors using windowed segments of the data; e.g., often using a minimum of 32 s. Little research has been reported in the literature concerning the instantaneous detection of respiratory rate from such sources. In this paper, we develop and evaluate a method to estimate instantaneous respiratory rate (IRR) from body-worn reflectance photoplethysmography (PPG) sensors. The proposed method relies on a nonlinear time-frequency representation, termed the wavelet synchrosqueezed transform (WSST). We apply the latter to derived modulations of the PPG that arise from the act of breathing.We validate the proposed algorithm using (i) a custom device with a PPG probe placed on various body positions and (ii) a commercial wrist-worn device (WaveletHealth Inc., Mountain View, CA, USA). Comparator reference data were obtained via a thermocouple placed under the nostrils, providing ground-truth information concerning respiration cycles. Tracking instantaneous frequencies was performed in the joint time-frequency spectrum of the (4 Hz re-sampled) respiratory-induced modulation using the WSST, from data obtained from 10 healthy subjects. The estimated instantaneous respiratory rates have shown to be highly correlated with breath-by-breath variations derived from the reference signals. The proposed method produced more accurate results compared to averaged RR obtained using 32 s windows investigated with overlap between successive windows of (i) zero and (ii) 28 s. For a set of five healthy subjects, the averaged similarity between reference RR and instantaneous RR, given by the longest common subsequence (LCSS) algorithm, was calculated as 0.69; this compares with averaged similarity of 0.49 using 32 s windows with 28 s overlap between successive windows. The results provide insight into estimation of IRR and show that upper body positions produced PPG signals from which a better respiration signal was extracted than for other body locations.
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LI, NA, MARTIN CRANE, and HEATHER J. RUSKIN. "AUTOMATICALLY DETECTING "SIGNIFICANT EVENTS" ON SenseCam." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 06 (November 2013): 1350050. http://dx.doi.org/10.1142/s0219691313500501.
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SenseCam is an effective memory-aid device that can automatically record images and other data from the wearer's whole day. The main issue is that, while SenseCam produces a sizeable collection of images over the time period, the vast quantity of captured data contains a large percentage of routine events, which are of little interest to review. In this article, the aim is to detect "Significant Events" for the wearers. We use several time series analysis methods such as Detrended Fluctuation Analysis (DFA), Eigenvalue dynamics and Wavelet Correlations to analyse the multiple time series generated by the SenseCam. We show that Detrended Fluctuation Analysis exposes a strong long-range correlation relationship in SenseCam collections. Maximum Overlap Discrete Wavelet Transform (MODWT) was used to calculate equal-time Correlation Matrices over different time scales and then explore the granularity of the largest eigenvalue and changes of the ratio of the sub-dominant eigenvalue spectrum dynamics over sliding time windows. By examination of the eigenspectrum, we show that these approaches enable detection of major events in the time SenseCam recording, with MODWT also providing useful insight on details of major events. We suggest that some wavelet scales (e.g., 8 minutes–16 minutes) have the potential to identify distinct events or activities.
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Qi, Liping, Xiao-Chi Ma, Dong-Dong Zhou, Shuo Guan, Feng-Shan Gao, and Pei-Xin Cong. "Wavelet and principal component analysis of electromyographic activity and slow component of oxygen uptake during heavy and severe cycling exercise." Applied Physiology, Nutrition, and Metabolism 45, no. 2 (February 2020): 187–92. http://dx.doi.org/10.1139/apnm-2019-0037.
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The aim of the study was to investigate whether the slow component of oxygen uptake was concurrent with the recruitment of large α-motoneuron muscle fibres by using wavelet and principal component analysis (PCA) of electromyography (EMG) during heavy and severe cycling exercise. Eleven male subjects participated in the study. After establishing each subject’s maximum value of oxygen uptake through an incremental test on the cycle ergometer, the subjects performed 6-min cycling tests at heavy and severe intensity. EMG signals were collected from rectus femoris, biceps femoris long head, tibialis anterior, and medial gastrocnemius and processed by combined use of wavelet and PCA analysis. The time delays to the onset of slow component occurred significantly earlier during severe (105.22 ± 5.45 s) compared with during heavy (138.78 ± 15.09 s) exercise. ANOVA with repeated measures showed that for all muscles tested, the angle θ formed by the first and second principal components decreased significantly between time windows during heavy and severe exercise. However, significant increases of EMG mean power frequency (MPF) were found only during heavy exercise. Our results show the concurrence of the oxygen uptake slow component with the additional recruitment of muscle fibres, presumably less efficient large α-motoneuron fibres. Novelty The expected rise in MPF may be offset by muscle fatigue occurring in the later time windows of the slow component during severe exercise. The gradual shift to higher EMG frequencies throughout the slow-component phase was reflected in the progressive and significant decrease of angle θ.
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Wei, Jianjun, Zhenyuan Wang, and Xinpeng Xing. "A Wireless High-Sensitivity Fetal Heart Sound Monitoring System." Sensors 21, no. 1 (December 30, 2020): 193. http://dx.doi.org/10.3390/s21010193.
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In certain cases, the condition of the fetus can be revealed by the fetal heart sound. However, when the sound is detected, it is mixed with noise from the external environment as well as internal disturbances. Our exclusive sensor, which was constructed of copper with an enclosed cavity, was designed to prevent external noise. In the sensor, a polyvinylidene fluoride (PVDF) piezoelectric film, with a frequency range covering that of the fetal heart sound, was adopted to convert the sound into an electrical signal. The adaptive support vector regression (SVR) algorithm was proposed to reduce internal disturbance. The weighted-index average algorithm with deviation correction was proposed to calculate the fetal heart rate. The fetal heart sound data were weighted automatically in the window and the weight was modified with an exponent between windows. The experiments show that the adaptive SVR algorithm was superior to empirical mode decomposition (EMD), the self-adaptive least square method (LSM), and wavelet transform. The weighted-index average algorithm weakens fetal heart rate jumps and the results are consistent with reality.
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Ferber, Ralf, Philippe Caprioli, and Lee West. "L1 pseudo-Vz estimation and deghosting of single-component marine towed-streamer data." GEOPHYSICS 78, no. 2 (March 1, 2013): WA21—WA6. http://dx.doi.org/10.1190/geo2012-0293.1.
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We present a novel technique estimating the vertical component of particle motion from marine single-component pressure data. The particle motion data, bar an angle-dependent obliquity factor, is computed by convolution of the output from L1 deconvolution of the pressure ghost wavelet with the corresponding ghost wavelet of the particle motion. The estimated particle motion data is then used in a conventional 2D technique for receiver ghost attenuation by combination with the original pressure-wave data. The proposed new technique operates in the τ-[Formula: see text] domain of individual shot-streamer records and in overlapping windows along the intercept-time axis. In each window, the L1 deconvolution is achieved by an iteratively reweighted-norm least squares algorithm. We applied our technique to deep-tow streamer data of a 3D over/sparse-under marine survey, in which six streamers were towed at a shallow depth, with two additional streamers towed deeper. Over/sparse-under technology allows using seismic measurements from a shallow streamer to be complemented by a low-frequency limited measurement from a deep streamer to achieve an estimate of the up-going pressure wave recording. The low frequencies of the deep streamer are used to boost the low frequencies of the shallow streamer, which have been heavily attenuated by the shallow tow ghost response. Our technique achieves, on this particular data, set improvements in bandwidth of the single-component pressure data, while not fully reaching the quality of the optimally deghosted data from the over/sparse-under survey.
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Tsinganos, Panagiotis, Bruno Cornelis, Jan Cornelis, Bart Jansen, and Athanassios Skodras. "Data Augmentation of Surface Electromyography for Hand Gesture Recognition." Sensors 20, no. 17 (August 29, 2020): 4892. http://dx.doi.org/10.3390/s20174892.
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The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.
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Lainscsek, Claudia, Manuel E. Hernandez, Howard Poizner, and Terrence J. Sejnowski. "Delay Differential Analysis of Electroencephalographic Data." Neural Computation 27, no. 3 (March 2015): 615–27. http://dx.doi.org/10.1162/neco_a_00656.
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We propose a time-domain approach to detect frequencies, frequency couplings, and phases using nonlinear correlation functions. For frequency analysis, this approach is a multivariate extension of discrete Fourier transform, and for higher-order spectra, it is a linear and multivariate alternative to multidimensional fast Fourier transform of multidimensional correlations. This method can be applied to short and sparse time series and can be extended to cross-trial and cross-channel spectra (CTS) for electroencephalography data where multiple short data segments from multiple trials of the same experiment are available. There are two versions of CTS. The first one assumes some phase coherency across the trials, while the second one is independent of phase coherency. We demonstrate that the phase-dependent version is more consistent with event-related spectral perturbation analysis and traditional Morlet wavelet analysis. We show that CTS can be applied to short data windows and yields higher temporal resolution than traditional Morlet wavelet analysis. Furthermore, the CTS can be used to reconstruct the event-related potential using all linear components of the CTS.
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Stephenson, J. A. E., and A. D. M. Walker. "Coherence between radar observations of magnetospheric field line resonances and discrete oscillations in the solar wind." Annales Geophysicae 28, no. 1 (January 13, 2010): 47–59. http://dx.doi.org/10.5194/angeo-28-47-2010.
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Abstract. Field line resonances have been observed for decades by ground-based and in situ instruments. The driving mechanism(s) are still unclear, although previous work has provided strong grounds that coherent waves in the solar wind may be a source. Here we present further evidence, with the use of multitaper analysis, a sophisticated spectrum estimation technique. A set of windows (dpss tapers) is chosen with characteristics that best suit the width of the narrowband peaks to be identified. The orthogonality of the windows allows for a confidence level (of say 95%) against a null hypothesis of a noisy spectrum, so that significant peaks can be identified. Employing multitaper analysis we can determine the phase and amplitude coherence at the sampling rate of the data sets and, over their entire duration. These characteristics make this technique superior to single windowing or wavelet analysis. A high degree of phase and amplitude (greater then 95%) coherence is demonstrated between a 2.1 mHz field line resonance observed by the SHARE radar at Sanae, Antarctica and the solar wind oscillation detected by WIND and ACE satellites.
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Mansoor, Mohammed Sheikh. "AUTODETECTION ALGORITHM OF PPG AND ECG PEAKS BASED ON 2 MOVING WINDOWS." Electronic Journal of University of Aden for Basic and Applied Sciences 2, no. 1 (March 31, 2021): 07–13. http://dx.doi.org/10.47372/ejua-ba.2021.1.84.
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In this study, an algorithm autodetection of PPG (Photoplethysmography) and ECG in an electrocardiogram is proposed. Many researches have been done for developing a new approach in this field, using different algorithms ranging from filtering and threshold methods, through wavelet methods, to neural networks, and others, each of which has different effectiveness and weaknesses. Although their performance in general good, but, the main weakness is that they are threshold dependent. Threshold-free detection is another proposed algorithm, where RR moving interval is calculated based on normal maximum and minimum heart rate (HR). This has the advantage of ensuring that every R-peak is contained between the edges of the moving interval. Thus, the effectiveness of this algorithm is that it is threshold independent, but its weaknesses are in the change in the RR interval according to the change in the heart rate frequency, which leads to missing some peaks. The effectiveness of the new algorithm autodetection peak is developed to overcome the weaknesses of threshold dependent and threshold independent algorithms. It based on a threshold-free algorithm with double moving windows. The complete algorithm is implemented using MATLAB 7.4. The method is validated using 18 recorded signals. The average sensitivity and average positive predictivity of PPG are 99.5% and 99.6% and of ECG are 99.3% and 99.4% respectively.
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Cao, Juanjuan, and George McMechan. "Multiple prediction and subtraction from apparent slowness relations in 2D synthetic and field ocean-bottom cable data." GEOPHYSICS 75, no. 6 (November 2010): V89—V99. http://dx.doi.org/10.1190/1.3506145.
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A target-oriented algorithm is developed for the prediction of multiples recorded on ocean-bottom cables by utilizing apparent slowness relations in common-source and common-receiver gathers. It is based on combining offsets and times of direct waves and primary reflections to predict multiples by matching apparent slownesses at all source and receiver locations; all higher-order multiples can be predicted by matching apparent slownesses alternately in common-source and common-receiver gathers. No knowledge of the subsurface velocity is required. Traveltimes of the direct waves and primary reflections need to be picked from common-source gathers. The subtraction of multiples involves flattening the predicted times of the multiple events, subtracting a local spatial average trace from each trace, within a fixed time window containing the wavelet of the multiple, and then shifting the data back to its original times. Tests of synthetic and field data indicate that the proposed method predicts multiples very well and removes them from seismic data efficiently with negligible affect on the primary reflections, as long as the primary and multiple reflections do not overlap in time and slowness over substantial windows in the domain in which the removal is done.
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Boyd, John P. "Limited-Area Fourier Spectral Models and Data Analysis Schemes: Windows, Fourier Extension, Davies Relaxation, and All That." Monthly Weather Review 133, no. 7 (July 1, 2005): 2030–42. http://dx.doi.org/10.1175/mwr2960.1.
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Abstract Regional spectral models have previously periodized and blended limited-area data through ad hoc low-order schemes justified by intuition and empiricism. By using infinitely differentiable “window functions” or “bells” borrowed from wavelet theory, one can periodize with preservation of spectral accuracy. Similarly, it is shown through a mixture of theory and numerical examples that Davies relaxation for blending limited-area and global data in one-way nested forecasting can be performed using the same C∞ bells as employed for the Fourier blending.“The relative success of empirical methods . . . may be used as partial justification to allow us to make the daring approximation that the data on a limited area domain may be decomposed into a trend and a periodic perturbation, and to proceed with Fourier transformation of the latter.” Laprise (2003, p. 775)
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Casati, B., and L. J. Wilson. "A New Spatial-Scale Decomposition of the Brier Score: Application to the Verification of Lightning Probability Forecasts." Monthly Weather Review 135, no. 9 (September 1, 2007): 3052–69. http://dx.doi.org/10.1175/mwr3442.1.
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Abstract A new scale decomposition of the Brier score for the verification of probabilistic forecasts defined on a spatial domain is introduced. The technique is illustrated on the Canadian Meteorological Centre (CMC) lightning probability forecasts. Probability forecasts of lightning occurrence in 3-h time windows and 24-km spatial resolution are verified against lightning observations from the North American Lightning Detection Network (NALDN) on a domain encompassing Canada and the northern United States. Verification is performed for lightning occurrences exceeding two different thresholds, to assess the forecast performance both for modest and intense lightning activity. Observation and forecast fields are decomposed into the sum of components on different spatial scales by performing a discrete 2D Haar wavelet decomposition. Wavelets, rather than Fourier transforms, were chosen because they are locally defined, and therefore more suitable for representing discontinuous spatial fields characterized by the presence of a few sparse nonzero values, such as lightning. Verification at different spatial scales is performed by evaluating Brier score and Brier skill score for each spatial-scale component. Reliability and resolution are also evaluated on different scales. Moreover, the bias on different scales is assessed, along with the ability of the forecasts to reproduce the observed-scale structure.
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Ochoa Gutierrez, Luis Hernán, Luis Fernando Niño Vasquez, and Carlos Alberto Vargas Jimenez. "Support vector machines applied to fast determination of the geographical coordinates of earthquakes, The case of El Rosal seismological station, Bogotá Colombia." DYNA 86, no. 209 (April 1, 2019): 230–37. http://dx.doi.org/10.15446/dyna.v86n209.75444.
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AbstractThe objective of this research was to determine seismic events latitude and longitude using support vector machines (SVMs) and seismic records from “El Rosal” station, which is located 40 kilometers northwest of Bogotá, Colombia. A total of 504 SVMs models were tested to determine latitude and 504 models for longitude, with various combinations of complexity factor and kernel function exponent, applied to earthquakes of 2, 2.5, 3 and 3.5 ML in time windows of 15 , 10 and 5 seconds. The best results showed errors of 40 kilometers for latitude and 30 kilometers for longitude, with respect to the place where the earthquakes were generated. These outcomes might be improved by applying additional descriptors during SVMs training stages, such descriptors can be related to Fourier frequency spectra, predominant period and wavelet transform coefficients.
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Nafati, N. M., S. Antonczak, J. Topin, and J. Golebiowski. "Multiscale Convergence Optimization in Constrained Molecular Dynamics Simulations." International Journal of Energy 16 (March 9, 2022): 45–51. http://dx.doi.org/10.46300/91010.2022.16.7.
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The energy analysis is essential for studying chemical or biochemical reactions but also for characterizing interactions between two protagonists. Molecular Dynamics Simulations are well suited to sampling interaction structures but under minimum energy. To sample unstable or high energy structures, it is necessary to apply a bias-constraint in the simulation, in order to maintain the system in a stable energy state. In MD constrained simulations of ""Umbrella Sampling"" type, the phenomenon of ligand-receptor dissociation is divided into a series of windows (space sampling) in which the simulation time is fixed in advance. A step of de-biasing and statistical processing then allows accessing to the Potential Force Medium (PMF) of the studied process. In this context, we have developed an algorithm that optimizes the DM computation time regarding each reaction coordinate (distance between the ligand and the receptor); and thus can dynamically adjust the sampling time in each US-Window. The data processing consists in studying the convergence of the distributions of the coordinate constraint and its performance is tested on different ligand-receptor systems. Its originality lies in the used processing technique which combines wavelet thresholding with statistical-tests decision in relation to distribution convergence. In this paper, we briefly describe a Molecular Dynamic Simulation, then by assumption we consider that distribution data are series of random-variables vectors obeying to a normal probality law. These vectors are first analyzed by a wavelet technique, thresholded and in a second step, their law probability is computed for comparison in terms of convergence. In this context, we give the result of PMF and computation time according to statistic-tests convergence criteria, such as Kolmogorov Smirnov, Student tTest, and ANOVA Tests. We also compare these results with those obtained after a preprocessing with Gaussian low-pass filtering in order to follow the influence of thresholding. Finally, the results are discussed and analyzed regarding the contribution of the muli-scale processing and the more suited criteria for time optimization.
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CONLON, T., H. J. RUSKIN, and M. CRANE. "MULTISCALED CROSS-CORRELATION DYNAMICS IN FINANCIAL TIME-SERIES." Advances in Complex Systems 12, no. 04n05 (August 2009): 439–54. http://dx.doi.org/10.1142/s0219525909002325.
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The cross-correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different time–scales and then explore the eigenvalue spectrum over sliding time-windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium, and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of time–scale information in the context of risk management.
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Lin, Cheng-Yu, Yi-Wen Wang, Febryan Setiawan, Nguyen Thi Hoang Trang, and Che-Wei Lin. "Sleep Apnea Classification Algorithm Development Using a Machine-Learning Framework and Bag-of-Features Derived from Electrocardiogram Spectrograms." Journal of Clinical Medicine 11, no. 1 (December 30, 2021): 192. http://dx.doi.org/10.3390/jcm11010192.
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Background: Heart rate variability (HRV) and electrocardiogram (ECG)-derived respiration (EDR) have been used to detect sleep apnea (SA) for decades. The present study proposes an SA-detection algorithm using a machine-learning framework and bag-of-features (BoF) derived from an ECG spectrogram. Methods: This study was verified using overnight ECG recordings from 83 subjects with an average apnea–hypopnea index (AHI) 29.63 (/h) derived from the Physionet Apnea-ECG and National Cheng Kung University Hospital Sleep Center database. The study used signal preprocessing to filter noise and artifacts, ECG time–frequency transformation using continuous wavelet transform (CWT), BoF feature generation, machine-learning classification using support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN) classification, and cross-validation. The time length of the spectrogram was set as 10 and 60 s to examine the required minimum spectrogram window time length to achieve satisfactory accuracy. Specific frequency bands of 0.1–50, 8–50, 0.8–10, and 0–0.8 Hz were also extracted to generate the BoF to determine the band frequency best suited for SA detection. Results: The five-fold cross-validation accuracy using the BoF derived from the ECG spectrogram with 10 and 60 s time windows were 90.5% and 91.4% for the 0.1–50 Hz and 8–50 Hz frequency bands, respectively. Conclusion: An SA-detection algorithm utilizing BoF and a machine-learning framework was successfully developed in this study with satisfactory classification accuracy and high temporal resolution.
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Marrouch, Natasza, Joanna Slawinska, Dimitrios Giannakis, and Heather L. Read. "Data-driven Koopman operator approach for computational neuroscience." Annals of Mathematics and Artificial Intelligence 88, no. 11-12 (November 11, 2019): 1155–73. http://dx.doi.org/10.1007/s10472-019-09666-2.
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Abstract This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta ($ \sim $ ∼ 13 Hz) and high Gamma ($ \sim $ ∼ 50 Hz) frequencies, and explore the spatiotemporal dynamics of high- and low- frequency components.
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Setiawan, Febryan, and Che-Wei Lin. "Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time–Frequency Features for the Detection and Severity Classification of Parkinson’s Disease." Sensors 21, no. 15 (July 31, 2021): 5207. http://dx.doi.org/10.3390/s21155207.
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Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.
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Gulai, A. V., and V. M. Zaitsev. "INTELLIGENT TECHNOLOGY OF WAVELET ANALYSIS OF VIBRATION SIGNALS." Doklady BGUIR, no. 7-8 (December 29, 2019): 101–8. http://dx.doi.org/10.35596/1729-7648-2019-126-8-101-108.
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During solution of engineering problems of machinery dynamics a need of revealing the harmonic components often arises in the narrow timing gate. This requires the use of wavelet-transformation oscillation methods and introduction of intelligent systems to hardware and software used in the experiment. The wavelet is considered as a short in time signal functional window, which has its internal structure in the form of a fading wavelike burst, and it is characterized by a scale of display of certain events in the field of the signal frequency spectrum, as well as and by time axis shifts. Complex-functioned continuous functions of real arguments (Daubechies wavelets, Gaussian wavelets, MHat-wavelets), complex-valued functions of real arguments (Morlet and Paul wavelets), as well as real discrete functions (HААRT- and FHat-wavelets) are used as wavelet functions. The wavelet analysis method of vibration signals is disclosed at acoustic diagnostics of machines and mechanisms. Digital implementation of discrete indications of wavelets with the subsequent visualization of results in the form of scalotons is the mathematical basis of the algorithm for procession of vibration signals. It has been suggested that engineering analysis and reconstruction of signals should be implemented by means of directed and reverse continuous wavelet conversions, which are discrete by arguments. The structural and functional scheme of the multichannel system of the intelligent wavelet analysis of vibration signals in machines has been considered. The intelligent system for study of vibration signals makes it possible to form the totality of photographic parameters, when scalotons are calculated by wavelet functions. An example of experimental implementation of the wavelet conversion method of vibration signals parameters is shown. Results of scalotons calculation are shown, when MHat-wavelet and DOG-wavelet are used.