Discrete wavelet analysis for multiparticle production experiments
Georgopoulos, G; Vassiliou, Maria
2000-01-01
In high energy nucleus-nucleus collisions (SPS, RHIC, LHC) and in cosmic ray interactions, many particles are produced in the available phase space. We make an attempt to apply the wavelets technique in order to classify such events according to the event pattern and also to locate the so-called "clustering" in a distribution. After describing the method, we demonstrate its power (a) to a single event, produced by a pion condensation theoretical model, (b) to a sample of Pb-Pb simulated data at 158 GeV/c per nucleon taking into account all the experimental uncertainties. (15 refs).
Cheng, Lizhi; Luo, Yong; Chen, Bo
2014-01-01
This book could be divided into two parts i.e. fundamental wavelet transform theory and method and some important applications of wavelet transform. In the first part, as preliminary knowledge, the Fourier analysis, inner product space, the characteristics of Haar functions, and concepts of multi-resolution analysis, are introduced followed by a description on how to construct wavelet functions both multi-band and multi wavelets, and finally introduces the design of integer wavelets via lifting schemes and its application to integer transform algorithm. In the second part, many applications are discussed in the field of image and signal processing by introducing other wavelet variants such as complex wavelets, ridgelets, and curvelets. Important application examples include image compression, image denoising/restoration, image enhancement, digital watermarking, numerical solution of partial differential equations, and solving ill-conditioned Toeplitz system. The book is intended for senior undergraduate stude...
Bizzarri, Anna Rita
2016-01-01
Force fluctuations recorded in an atomic force spectroscopy experiment, during the approach of a tip functionalized with biotin towards a substrate charged with avidin, have been analyzed by a wavelet transform. The observation of strong transient changes only when a specific biorecognition process between the partners takes place suggests a drastic modulation of the force fluctuations when biomolecules recognize each other. Such an analysis allows to investigate the peculiar features of a biorecognition process. These results are discussed in connection with the possible role of energy minima explored by biomolecules during the biorecognition process.
Wavelet analysis in neurodynamics
Pavlov, Aleksei N.; Hramov, Aleksandr E.; Koronovskii, Aleksei A.; Sitnikova, Evgenija Yu; Makarov, Valeri A.; Ovchinnikov, Alexey A.
2012-09-01
Results obtained using continuous and discrete wavelet transforms as applied to problems in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for decoding signal information from neural systems and networks. The following areas of application are considered: (1) the microscopic dynamics of single cells and intracellular processes, (2) sensory data processing, (3) the group dynamics of neuronal ensembles, and (4) the macrodynamics of rhythmical brain activity (using multichannel EEG recordings). The detection and classification of various oscillatory patterns of brain electrical activity and the development of continuous wavelet-based brain activity monitoring systems are also discussed as possibilities.
Affine density in wavelet analysis
Kutyniok, Gitta
2007-01-01
In wavelet analysis, irregular wavelet frames have recently come to the forefront of current research due to questions concerning the robustness and stability of wavelet algorithms. A major difficulty in the study of these systems is the highly sensitive interplay between geometric properties of a sequence of time-scale indices and frame properties of the associated wavelet systems. This volume provides the first thorough and comprehensive treatment of irregular wavelet frames by introducing and employing a new notion of affine density as a highly effective tool for examining the geometry of sequences of time-scale indices. Many of the results are new and published for the first time. Topics include: qualitative and quantitative density conditions for existence of irregular wavelet frames, non-existence of irregular co-affine frames, the Nyquist phenomenon for wavelet systems, and approximation properties of irregular wavelet frames.
WAVELET ANALYSIS OF ABNORMAL ECGS
Directory of Open Access Journals (Sweden)
Vasudha Nannaparaju
2014-02-01
Full Text Available Detection of the warning signals by the heart can be diagnosed from ECG. An accurate and reliable diagnosis of ECG is very important however which is cumbersome and at times ambiguous in time domain due to the presence of noise. Study of ECG in wavelet domain using both continuous Wavelet transform (CWT and discrete Wavelet transform (DWT, with well known wavelet as well as a wavelet proposed by the authors for this investigation is found to be useful and yields fairly reliable results. In this study, Wavelet analysis of ECGs of Normal, Hypertensive, Diabetic and Cardiac are carried out. The salient feature of the study is that detection of P and T phases in wavelet domain is feasible which are otherwise feeble or absent in raw ECGs.
An Introduction to Wavelet Theory and Analysis
Energy Technology Data Exchange (ETDEWEB)
Miner, N.E.
1998-10-01
This report reviews the history, theory and mathematics of wavelet analysis. Examination of the Fourier Transform and Short-time Fourier Transform methods provides tiormation about the evolution of the wavelet analysis technique. This overview is intended to provide readers with a basic understanding of wavelet analysis, define common wavelet terminology and describe wavelet amdysis algorithms. The most common algorithms for performing efficient, discrete wavelet transforms for signal analysis and inverse discrete wavelet transforms for signal reconstruction are presented. This report is intended to be approachable by non- mathematicians, although a basic understanding of engineering mathematics is necessary.
From Fourier analysis to wavelets
Gomes, Jonas
2015-01-01
This text introduces the basic concepts of function spaces and operators, both from the continuous and discrete viewpoints. Fourier and Window Fourier Transforms are introduced and used as a guide to arrive at the concept of Wavelet transform. The fundamental aspects of multiresolution representation, and its importance to function discretization and to the construction of wavelets is also discussed. Emphasis is given on ideas and intuition, avoiding the heavy computations which are usually involved in the study of wavelets. Readers should have a basic knowledge of linear algebra, calculus, and some familiarity with complex analysis. Basic knowledge of signal and image processing is desirable. This text originated from a set of notes in Portuguese that the authors wrote for a wavelet course on the Brazilian Mathematical Colloquium in 1997 at IMPA, Rio de Janeiro.
A Mellin transform approach to wavelet analysis
Alotta, Gioacchino; Di Paola, Mario; Failla, Giuseppe
2015-11-01
The paper proposes a fractional calculus approach to continuous wavelet analysis. Upon introducing a Mellin transform expression of the mother wavelet, it is shown that the wavelet transform of an arbitrary function f(t) can be given a fractional representation involving a suitable number of Riesz integrals of f(t), and corresponding fractional moments of the mother wavelet. This result serves as a basis for an original approach to wavelet analysis of linear systems under arbitrary excitations. In particular, using the proposed fractional representation for the wavelet transform of the excitation, it is found that the wavelet transform of the response can readily be computed by a Mellin transform expression, with fractional moments obtained from a set of algebraic equations whose coefficient matrix applies for any scale a of the wavelet transform. Robustness and computationally efficiency of the proposed approach are shown in the paper.
Wavelet analysis of epileptic spikes
Latka, M; Kozik, A; West, B J; Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.
2003-01-01
Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous, pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.
Wavelet analysis of epileptic spikes
Latka, Miroslaw; Was, Ziemowit; Kozik, Andrzej; West, Bruce J.
2003-05-01
Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition of a spike is based on its amplitude, duration, sharpness, and emergence from its background. However, spike detection systems built solely around this definition are not reliable due to the presence of numerous transients and artifacts. We use wavelet transform to analyze the properties of EEG manifestations of epilepsy. We demonstrate that the behavior of wavelet transform of epileptic spikes across scales can constitute the foundation of a relatively simple yet effective detection algorithm.
Denoising and robust nonlinear wavelet analysis
Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.
1994-03-01
In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transform, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the 'S+WAVELETS' object-oriented toolkit for wavelet analysis.
Wavelet analysis and its applications an introduction
Yajnik, Archit
2013-01-01
"Wavelet analysis and its applications: an introduction" demonstrates the consequences of Fourier analysis and introduces the concept of wavelet followed by applications lucidly. While dealing with one dimension signals, sometimes they are required to be oversampled. A novel technique of oversampling the digital signal is introduced in this book alongwith necessary illustrations. The technique of feature extraction in the development of optical character recognition software for any natural language alongwith wavelet based feature extraction technique is demonstrated using multiresolution analysis of wavelet in the book.
Power System Transients Analysis by Wavelet Transforms
Institute of Scientific and Technical Information of China (English)
陈维荣; 宋永华; 赵蔚
2002-01-01
In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time-frequency characteristics with automatically-adjusted window lengths. Research shows that wavelet transform is one of the most powerful tools for power system transient analysis. The basic ideas of wavelet transform are presented in the paper together with several power system applications. It is clear that wavelet transform has some clear advantages over other transforms in detecting, analyzing, and identifying various types of power system transients.
Cross wavelet analysis: significance testing and pitfalls
Directory of Open Access Journals (Sweden)
D. Maraun
2004-01-01
Full Text Available In this paper, we present a detailed evaluation of cross wavelet analysis of bivariate time series. We develop a statistical test for zero wavelet coherency based on Monte Carlo simulations. If at least one of the two processes considered is Gaussian white noise, an approximative formula for the critical value can be utilized. In a second part, typical pitfalls of wavelet cross spectra and wavelet coherency are discussed. The wavelet cross spectrum appears to be not suitable for significance testing the interrelation between two processes. Instead, one should rather apply wavelet coherency. Furthermore we investigate problems due to multiple testing. Based on these results, we show that coherency between ENSO and NAO is an artefact for most of the time from 1900 to 1995. However, during a distinct period from around 1920 to 1940, significant coherency between the two phenomena occurs.
Status of pattern recognition with wavelet analysis
Institute of Scientific and Technical Information of China (English)
Yuanyan TANG
2008-01-01
Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years.Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence,information security,biometrics,military science and technology,finance and economics,weather forecast,image processing,communication,biomedical engineering,document processing,robot vision,transportation,and endless other areas,with many encouraging results.The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis.This paper aims at a brief survey of pattern recognition with the wavelet theory.It contains the following respects:analysis and detection of singularities with wavelets;wavelet descriptors for shapes of the objects;invariant representation of patterns;handwritten and printed character recognition;texture analysis and classification;image indexing and retrieval;classification and clustering;document analysis with wavelets;iris pattern recognition;face recognition using wavelet transform;hand gestures classification;character processing with B-spline wavelet transform;wavelet-based image fusion,and others.
THEORY AND APPLICATION OF WAVELET ANALYSIS INSTRUMENT LIBRARY
Institute of Scientific and Technical Information of China (English)
BO Lin; QIN Shuren; LIU Xiaofeng
2006-01-01
Some new theory and algorithms on wavelet analysis are proposed, including continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet package transform (WPT),wavelet denosing and mother wavelet selection, etc. Using the component-based hierarchy mode, the platform for virtual instrument (Ⅵ) is constructed, and the functions such as data sampling, data analysis and data present, etc are provided. Subsequently, the wavelet analysis library is designed and developed. The library consists of expert system, experienced database, development platform and abundant wavelet analysis functional module, which together implement general and special wavelet analysis in the field of mechanical engineering, energy source, transportation and biomedicine, etc.Finally, the wavelet analysis virtual instrument library is applied to detect fault called engine knock.Experimental result indicates that the wavelet analysis virtual instrument library can efficiently solve the engineering problem such as detecting engine knock.
Adapted wavelet analysis from theory to software
Wickerhauser, Mladen Victor
1994-01-01
This detail-oriented text is intended for engineers and applied mathematicians who must write computer programs to perform wavelet and related analysis on real data. It contains an overview of mathematical prerequisites and proceeds to describe hands-on programming techniques to implement special programs for signal analysis and other applications. From the table of contents: - Mathematical Preliminaries - Programming Techniques - The Discrete Fourier Transform - Local Trigonometric Transforms - Quadrature Filters - The Discrete Wavelet Transform - Wavelet Packets - The Best Basis Algorithm - Multidimensional Library Trees - Time-Frequency Analysis - Some Applications - Solutions to Some of the Exercises - List of Symbols - Quadrature Filter Coefficients
Electric Equipment Diagnosis based on Wavelet Analysis
Directory of Open Access Journals (Sweden)
Stavitsky Sergey A.
2016-01-01
Full Text Available Due to electric equipment development and complication it is necessary to have a precise and intense diagnosis. Nowadays there are two basic ways of diagnosis: analog signal processing and digital signal processing. The latter is more preferable. The basic ways of digital signal processing (Fourier transform and Fast Fourier transform include one of the modern methods based on wavelet transform. This research is dedicated to analyzing characteristic features and advantages of wavelet transform. This article shows the ways of using wavelet analysis and the process of test signal converting. In order to carry out this analysis, computer software Mathcad was used and 2D wavelet spectrum for a complex function was created.
Wavelet Analysis for Acoustic Phased Array
Kozlov, Inna; Zlotnick, Zvi
2003-03-01
Wavelet spectrum analysis is known to be one of the most powerful tools for exploring quasistationary signals. In this paper we use wavelet technique to develop a new Direction Finding (DF) Algorithm for the Acoustic Phased Array (APA) systems. Utilising multi-scale analysis of libraries of wavelets allows us to work with frequency bands instead of individual frequency of an acoustic source. These frequency bands could be regarded as features extracted from quasistationary signals emitted by a noisy object. For detection, tracing and identification of a sound source in a noisy environment we develop smart algorithm. The essential part of this algorithm is a special interacting procedure of the above-mentioned DF-algorithm and the wavelet-based Identification (ID) algorithm developed in [4]. Significant improvement of the basic properties of a receiving APA pattern is achieved.
Li, Hong; Ding, Xue
2017-03-01
This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.
Complex Wavelet Based Modulation Analysis
DEFF Research Database (Denmark)
Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt
2008-01-01
because only the magnitudes are taken into account and the phase data is often neglected. We remedy this problem with the use of a complex wavelet transform as a more appropriate envelope and phase processing tool. Complex wavelets carry both magnitude and phase explicitly with great sparsity and preserve well...... polynomial trends. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in an alternative transform domain coined as “Modulation Subbands”, this transform shows very promising denoising capabilities and suggests new approaches for joint...
Significance-linked connected component analysis for wavelet image coding.
Chai, B B; Vass, J; Zhuang, X
1999-01-01
Recent success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256x256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.
Wavelet Analysis for Molecular Dynamics
2015-06-01
2480. 4. Ismail AE, Rutledge GC, Stephanopoulos G. Topological coarse graining of polymer chains using wavelet-accelerated Monte Carlo. I. Freely...accelerated Monte Carlo. II. Self-avoiding chains. J Chem Phys. 2005;122:234902. 6. Coifman R, Maggioni M. Diffusion wavelets. Appl Comput Harm Anal...INFORMATION CTR DTIC OCA 2 (PDF) DIRECTOR US ARMY RESEARCH LAB RDRL CIO LL IMAL HRA MAIL & RECORDS MGMT 1 (PDF) GOVT PRINTG OFC A MALHOTRA 1 (PDF) DIR USARL RDRL WML B B RICE 21 INTENTIONALLY LEFT BLANK. 22
WAVELET ANALYSIS OF MODULATED SIGNALS
Institute of Scientific and Technical Information of China (English)
Hu Jianwei; Yang Shaoquan
2006-01-01
The relationship between Haar wavelet decomposition coefficients and modulated signal parameters is discussed. A new modulation classification method is presented. The new method uses the amplitude,frequency and phase information derived from Haar wavelet decomposition as feature vectors to distinguish the modulation types of M-ary Frequency-Shift Keying (MFSK), M-ary Phase-Shift Keying (MPSK) and Quadrature Amplitude Modulation (QAM) modulation types. A parallel combined classifier is designed based on these feature vectors. The overall successful recognition rate of 92.4% can be achieved even at a low Signal-to-Noise Ratio (SNR) of 5dB.
Directory of Open Access Journals (Sweden)
Md. Rafiqul Islam
2012-05-01
Full Text Available Fingerprint analysis plays a crucial role in crucial legal matters such as investigation of crime and security system. Due to the large number and size of fingerprint images, data compression has to be applied to reduce the storage and communication bandwidth requirements of those images. To do this, there are many types of wavelet has been used for fingerprint image compression. In this paper we haveused Coiflet-Type wavelets and our aim is to determine the most appropriate Coiflet-Type wavelet for better compression of a digitized fingerprint image and to achieve our goal Retain Energy (RE and Number of Zeros (NZ in percentage is determined for different Coiflet-Type wavelets at different threshold values at the fixed decomposition level 3 using wavelet and wavelet packet transform. We have used 8-bit grayscale left thumb digitized fingerprint image of size 480×400 as a test image.
Wavelet analysis of MR functional data from the cerebellum
Energy Technology Data Exchange (ETDEWEB)
Karen, Romero Sánchez, E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Vásquez Reyes Marcos, A., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; González Gómez Dulce, I., E-mail: alphacentauri-hp@hotmail.com, E-mail: marcos-vaquezr@hotmail.com, E-mail: isabeldgg@hotmail.com; Hernández López, Javier M., E-mail: javierh@fcfm.buap.mx [Faculty of Physics and Mathematics, BUAP, Puebla, Pue (Mexico); Silvia, Hidalgo Tobón, E-mail: shidbon@gmail.com [Infant Hospital of Mexico, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Infant Hospital of Mexico, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Foundation for Development Carlos Sigüenza. Puebla, Pue. (Mexico)
2014-11-07
The main goal of this project was to create a computer algorithm based on wavelet analysis of BOLD signals, which automatically diagnosed ADHD using information from resting state MR experiments. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Wavelet analysis, which is a mathematical tool used to decompose time series into elementary constituents and detect hidden information, was applied here to the BOLD signal obtained from the cerebellum 8 region of all our volunteers. Statistical differences between the values of the a parameters of wavelet analysis was found and showed significant differences (p<0.02) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD.
Wavelet methods in mathematical analysis and engineering
Damlamian, Alain
2010-01-01
This book gives a comprehensive overview of both the fundamentals of wavelet analysis and related tools, and of the most active recent developments towards applications. It offers a stateoftheart in several active areas of research where wavelet ideas, or more generally multiresolution ideas have proved particularly effective. The main applications covered are in the numerical analysis of PDEs, and signal and image processing. Recently introduced techniques such as Empirical Mode Decomposition (EMD) and new trends in the recovery of missing data, such as compressed sensing, are also presented.
Wavelet Analysis of Space Solar Telescope Images
Institute of Scientific and Technical Information of China (English)
Xi-An Zhu; Sheng-Zhen Jin; Jing-Yu Wang; Shu-Nian Ning
2003-01-01
The scientific satellite SST (Space Solar Telescope) is an important research project strongly supported by the Chinese Academy of Sciences. Every day,SST acquires 50 GB of data (after processing) but only 10GB can be transmitted to the ground because of limited time of satellite passage and limited channel volume.Therefore, the data must be compressed before transmission. Wavelets analysis is a new technique developed over the last 10 years, with great potential of application.We start with a brief introduction to the essential principles of wavelet analysis,and then describe the main idea of embedded zerotree wavelet coding, used for compressing the SST images. The results show that this coding is adequate for the job.
Application of wavelet transform in runoff sequence analysis
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A wavelet transform is applied to runoff analysis to obtain the composition of the runoff sequence and to forecast future runoff. An observed runoff sequence is firstly decomposed and reconstructed by wavelet transform and its expanding tendency is derived. Then, the runoff sequence is forecasted by the back propagation artificial neural networks (BPANN) and by a wavelet transform combined with BPANN. The earlier researches seldom involve the problem of how to choose wavelet function, which is important and cannot be ignored when the wavelet transform is used. With application of the developed approach to the analysis of runoff sequence, several kinds of wavelet functions have been tested.
RESEARCH OF WAVELET TRANSFORM INSTRUMENT SYSTEM FOR SIGNAL ANALYSIS
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
After brief describing the principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instrument of wavelet transform are described. A number of practical uses of this system demonstrate that wavelet transform system is specially functional in identifying and processing impulse, singular and nonsmooth signals,so that it should be evaluated the most advanced signal analyzing system.
Wavelet Variance Analysis of EEG Based on Window Function
Institute of Scientific and Technical Information of China (English)
ZHENG Yuan-zhuang; YOU Rong-yi
2014-01-01
A new wavelet variance analysis method based on window function is proposed to investigate the dynamical features of electroencephalogram (EEG).The ex-prienmental results show that the wavelet energy of epileptic EEGs are more discrete than normal EEGs, and the variation of wavelet variance is different between epileptic and normal EEGs with the increase of time-window width. Furthermore, it is found that the wavelet subband entropy (WSE) of the epileptic EEGs are lower than the normal EEGs.
Energy Technology Data Exchange (ETDEWEB)
Antonopoulos-Domis, M.; Tambouratzis, T. [NCSR, Athens (Greece). Institute of Nuclear Technology-Radiation Protection
1997-12-22
A method is proposed for system identification during a transient, employing wavelet multiresolution analysis and denoising followed by classical (FFT) special analysis. The method has been tested on numerical experiments. (Author).
Energy Technology Data Exchange (ETDEWEB)
Antonopoulos-Domis, M.; Tambouratzis, T
1998-05-01
A method is proposed for system identification during a transient, employing wavelet multiresolution analysis and denoising followed by classical (FFT) special analysis. The method has been tested on numerical experiments.
WAVELET RATIONAL FILTERS AND REGULARITY ANALYSIS
Institute of Scientific and Technical Information of China (English)
Zheng Kuang; Ming-gen Cui
2000-01-01
In this paper, we choose the trigonometric rational functions as wavelet filters and use them to derive various wavelets. Especially for a certain family of wavelets generated by the rational filters, the better smoothness results than Daubechies' are obtained.
Wavelet Denoising and Surface Electromyography Analysis
Hussain, M.S.; Md. Mamun
2012-01-01
In this research, Surface Electromyography (SEMG) signal analysis from the right rectus femoris muscle is performed during walk. Wavelet Transform (WT) has been applied for removing noise from the surface SEMG. Gaussianity tests are conducted to understand changes in muscle contraction and to quantify the effectiveness of the noise removal process. Results show that the proposed method can effectively remove noise from the raw SEMG signals for further analysis.
Wavelet Analysis of Fractionally Integrated Processes
Mark J. Jensen
1994-01-01
In this paper we apply wavelet analysis to the class of fractionally integrated processes to show that this class is a member of the $1/f$ family of processes as defined by Wornell (1993) and to produce an alternative method of estimating the fractional differencing parameter. Currently the method by Geweke and Porter-Hudak (1983) is used most often to estimate and test the fractional differencing parameter. The GPH approach, however, has been shown to have poor statistical properties and suf...
Wavelet analysis of the impedance cardiogram waveforms
Podtaev, S.; Stepanov, R.; Dumler, A.; Chugainov, S.; Tziberkin, K.
2012-12-01
Impedance cardiography has been used for diagnosing atrial and ventricular dysfunctions, valve disorders, aortic stenosis, and vascular diseases. Almost all the applications of impedance cardiography require determination of some of the characteristic points of the ICG waveform. The ICG waveform has a set of characteristic points known as A, B, E ((dZ/dt)max) X, Y, O and Z. These points are related to distinct physiological events in the cardiac cycle. Objective of this work is an approbation of a new method of processing and interpretation of the impedance cardiogram waveforms using wavelet analysis. A method of computer thoracic tetrapolar polyrheocardiography is used for hemodynamic registrations. Use of original wavelet differentiation algorithm allows combining filtration and calculation of the derivatives of rheocardiogram. The proposed approach can be used in clinical practice for early diagnostics of cardiovascular system remodelling in the course of different pathologies.
Analysis of phonocardiogram signals using wavelet transform.
Meziani, F; Debbal, S M; Atbi, A
2012-08-01
Phonocardiograms (PCG) are recordings of the acoustic waves produced by the mechanical action of the heart. They generally consist of two kinds of acoustic vibrations: heart sounds and heart murmurs. Heart murmurs are often the first signs of pathological changes of the heart valves, and are usually found during auscultation in primary health care. Heart auscultation has been recognized for a long time as an important tool for the diagnosis of heart disease, although its accuracy is still insufficient to diagnose some heart diseases. It does not enable the analyst to obtain both qualitative and quantitative characteristics of the PCG signals. The efficiency of diagnosis can be improved considerably by using modern digital signal processing techniques. Therefore, these last can provide useful and valuable information on these signals. The aim of this study is to analyse PCG signals using wavelet transform. This analysis is based on an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs using the PCG signal as the only source. The segmentation algorithm, which separates the components of the heart signal, is based on denoising by wavelet transform (DWT). This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs. Thus, the analysis of various PCGs signals using wavelet transform can provide a wide range of statistical parameters related to the phonocardiogram signal.
Wavelet and statistical analysis for melanoma classification
Nimunkar, Amit; Dhawan, Atam P.; Relue, Patricia A.; Patwardhan, Sachin V.
2002-05-01
The present work focuses on spatial/frequency analysis of epiluminesence images of dysplastic nevus and melanoma. A three-level wavelet decomposition was performed on skin-lesion images to obtain coefficients in the wavelet domain. A total of 34 features were obtained by computing ratios of the mean, variance, energy and entropy of the wavelet coefficients along with the mean and standard deviation of image intensity. An unpaired t-test for a normal distribution based features and the Wilcoxon rank-sum test for non-normal distribution based features were performed for selecting statistically correlated features. For our data set, the statistical analysis of features reduced the feature set from 34 to 5 features. For classification, the discriminant functions were computed in the feature space using the Mahanalobis distance. ROC curves were generated and evaluated for false positive fraction from 0.1 to 0.4. Most of the discrimination functions provided a true positive rate for melanoma of 93% with a false positive rate up to 21%.
Denoising and robust non-linear wavelet analysis
Bruce, Andrew G.; Donoho, David L.; Gao, Hong-Ye; Martin, R. D.
1994-04-01
In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistance wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transforms, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the S+Wavelets object-oriented toolkit for wavelet analysis.
Abnormal traffic flow data detection based on wavelet analysis
Directory of Open Access Journals (Sweden)
Xiao Qian
2016-01-01
Full Text Available In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.
TRANSMISSION LINE FAULT ANALYSIS USING WAVELET THEORY
Directory of Open Access Journals (Sweden)
Ravindra Malkar
2012-06-01
Full Text Available This paper describes a Wavelet transform technique to analyze power system disturbance such as transmission line faults with Biorthogonal and Haar wavelets. In this work, wavelet transform based approach,which is used to detect transmission line faults, is proposed. The coefficient of discrete approximation of the dyadic wavelet transform with different wavelets are used to be an index for transmission line fault detection and faulted – phase selection and select which wavelet is suitable for this application. MATLAB/Simulation is used to generate fault signals. Simulation results reveal that the performance of the proposed fault detection indicator is promising and easy to implement for computer relaying application.
Signal Analysis by New Mother Wavelets
Institute of Scientific and Technical Information of China (English)
NIU Jin-Bo; FAN Hong-Yi; QI Kai-Guo
2009-01-01
Based on the general formula for finding qualified mother wavelets [Opt. Lett. 31 (2006) 407] we make wavelet transforms computed with the newly found mother wavelets (characteristic of the power 2n) for some optical Gaussian pulses, which exhibit the ability to measure frequency of the pulse more precisely and clearly. We also work with complex mother wavelets composed of new real mother wavelets, which offer the ability of obtaining phase information of the pulse as well as amplitude information. The analogy between the behavior of Hermite-Gauss beams and that of new wavelet transforms is noticed.
An introduction to random vibrations, spectral & wavelet analysis
Newland, D E
2005-01-01
One of the first engineering books to cover wavelet analysis, this classic text describes and illustrates basic theory, with a detailed explanation of the workings of discrete wavelet transforms. Computer algorithms are explained and supported by examples and a set of problems, and an appendix lists ten computer programs for calculating and displaying wavelet transforms.Starting with an introduction to probability distributions and averages, the text examines joint probability distributions, ensemble averages, and correlation; Fourier analysis; spectral density and excitation response relation
Understanding wavelet analysis and filters for engineering applications
Parameswariah, Chethan Bangalore
parameters---number of data points and the sampling frequency---and the selection of these is critical to qualitative analysis of signals. A wavelet seismic event detection method is presented which has been successfully applied to detect the P phase and the S phase waves of earthquakes. This method uses wavelets to classify the seismic signal to different frequency bands and then a simple threshold trigger method is applied to the rms values calculated on one of the wavelet bands. Further research on the understanding of wavelets is encouraged through this research to provide qualified and clearly understood wavelet solutions to real world problems. The wavelets are a promising tool that will complement the existing signal processing methods and are open for research and exploration.
Wavelet analysis of solar decimeter spikes
Fernandes, F. C. R.; Bolzan, M. J. A.; Rosa, R. R.; Prestes, A.; Cecatto, J. R.; Sawant, H. S.
2012-04-01
The Brazilian Solar Spectroscope (BSS), in operation at INPE, Brazil, have recorded a group of radio spikes observed on June 24, 1999 (16:53 - 16: 56 UT), in the decimeter frequency range of 1000-2500 MHz, with high temporal resolution of 100 ms and spectral resolution of 10 MHz. This event presents distinct clusters of spikes with intermittent behavior. The spectral and temporal behaviors of the observed groups of spikes of radio were investigated, applying wavelet techniques, which permits to determine the cadence of the clusters. From the dynamic spectra, the following observational parameters were determined: the total duration of the event and of each cluster, the total frequency band and cluster bands, in the case of the harmonic structures. The wavelet analysis shows an average periodicity of about 17 seconds (and harmonics) for the cadence of intermittent clusters of spikes. The analysis also suggested a semi-harmonic frequency ratio of 1:1.2. The same methodology of analysis is being applied to other selected groups of spikes recorded by BSS. These results will be presented and discussed.
Directory of Open Access Journals (Sweden)
Akimov Pavel Alekseevich
2012-10-01
Full Text Available Part 1 of this paper represents an introduction into the multi-resolution wavelet analysis. The wavelet-based analysis is an exciting new problem-solving tool used by mathematicians, scientists and engineers. In the paper, the authors try to present the fundamental elements of the multi-resolution wavelet analysis in a way that is accessible to an engineer, a scientist and an applied mathematician both as a theoretical approach and as a potential practical method of solving problems (particularly, boundary problems of structural mechanics and mathematical physics. The main goal of the contemporary wavelet research is to generate a set of basic functions (or general expansion functions and transformations that will provide an informative, efficient and useful description of a function or a signal. Another central idea is that of multi-resolution whereby decomposition of a signal represents the resolution of the detail. The multi-resolution decomposition seems to separate components of a signal in a way that is superior to most other methods of analysis, processing or compression. Due to the ability of the discrete wavelet transformation technique to decompose a signal at different independent scaling levels and to do it in a very flexible way, wavelets can be named "the microscopes of mathematics". Indeed, the use of the wavelet analysis and wavelet transformations requires a new point of view and a new method of interpreting representations.
[Wavelet entropy analysis of spontaneous EEG signals in Alzheimer's disease].
Zhang, Meiyun; Zhang, Benshu; Chen, Ying
2014-08-01
Wavelet entropy is a quantitative index to describe the complexity of signals. Continuous wavelet transform method was employed to analyze the spontaneous electroencephalogram (EEG) signals of mild, moderate and severe Alzheimer's disease (AD) patients and normal elderly control people in this study. Wavelet power spectrums of EEG signals were calculated based on wavelet coefficients. Wavelet entropies of mild, moderate and severe AD patients were compared with those of normal controls. The correlation analysis between wavelet entropy and MMSE score was carried out. There existed significant difference on wavelet entropy among mild, moderate, severe AD patients and normal controls (Pentropy for mild, moderate, severe AD patients was significantly lower than that for normal controls, which was related to the narrow distribution of their wavelet power spectrums. The statistical difference was significant (Pentropy of EEG and the MMSE score were significantly correlated (r= 0. 601-0. 799, Pentropy is a quantitative indicator describing the complexity of EEG signals. Wavelet entropy is likely to be an electrophysiological index for AD diagnosis and severity assessment.
Steerable wavelet analysis of CMB structures alignment
Vielva, P; Martínez-González, E; Vandergheynst, P
2006-01-01
This paper reviews the application of a novel methodology for analysing the isotropy of the universe by probing the alignment of local structures in the CMB. The strength of the proposed methodology relies on the steerable wavelet filtering of the CMB signal. One the one hand, the filter steerability renders the computation of the local orientation of the CMB features affordable in terms of computation time. On the other hand, the scale-space nature of the wavelet filtering allows to explore the alignment of the local structures at different scales, probing possible different phenomena. We present the WMAP first-year data analysis recently performed by the same authors (Wiaux et al.), where an extremely significant anisotropy was found. In particular, a preferred plane was detected, having a normal direction with a northern end position close to the northern end of the CMB dipole axis. In addition, a most preferred direction was found in that plane, with a northern end direction very close to the north eclipt...
EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform
2001-10-25
research and medical applications. Wavelet transform (WT) is a new multi-resolution time-frequency analysis method. WT possesses localization feature both... wavelet transform , the EEG signals are successfully decomposed and denoised. In this paper we also use a ’quasi-detrending’ method for classification of EEG
Aircraft target identification based on 2D ISAR images using multiresolution analysis wavelet
Fu, Qiang; Xiao, Huaitie; Hu, Xiangjiang
2001-09-01
The formation of 2D ISAR images for radar target identification hold much promise for additional distinguish- ability between targets. Since an image contains important information is a wide range of scales, and this information is often independent from one scale to another, wavelet analysis provides a method of identifying the spatial frequency content of an image and the local regions within the image where those spatial frequencies exist. In this paper, a multiresolution analysis wavelet method based on 2D ISAR images was proposed for use in aircraft radar target identification under the wide band high range resolution radar background. The proposed method was performed in three steps; first, radar backscatter signals were processed in the form of 2D ISAR images, then, Mallat's wavelet algorithm was used in the decomposition of images, finally, a three layer perceptron neural net was used as classifier. The result of experiments demonstrated that the feasibility of using multiresolution analysis wavelet for target identification.
Directory of Open Access Journals (Sweden)
Jikai Chen
2016-12-01
Full Text Available In a power system, the analysis of transient signals is the theoretical basis of fault diagnosis and transient protection theory. Shannon wavelet entropy (SWE and Shannon wavelet packet entropy (SWPE are powerful mathematics tools for transient signal analysis. Combined with the recent achievements regarding SWE and SWPE, their applications are summarized in feature extraction of transient signals and transient fault recognition. For wavelet aliasing at adjacent scale of wavelet decomposition, the impact of wavelet aliasing is analyzed for feature extraction accuracy of SWE and SWPE, and their differences are compared. Meanwhile, the analyses mentioned are verified by partial discharge (PD feature extraction of power cable. Finally, some new ideas and further researches are proposed in the wavelet entropy mechanism, operation speed and how to overcome wavelet aliasing.
Forecasting of PM10 time series using wavelet analysis and wavelet-ARMA model in Taiyuan, China.
Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng
2017-02-23
PM10 forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-time series of the PM10 concentrations. It was evaluated by experiments using a 10-year dataset of daily PM10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: 1) PM10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but it was increased in 2013. PM10 concentrations had an obvious seasonal fluctuation related with coal fired heating in winter and early spring. 2) Spatial difference among four stations showed that the PM10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. 3) Wavelet analysis revealed that the trend variation and the changes of the PM10 concentration of Taiyuan were complicated. 4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multi-time scale prediction.
Wavelet Based Fractal Analysis of Airborne Pollen
Degaudenzi, M E
1999-01-01
The most abundant biological particles in the atmosphere are pollen grains and spores. Self protection of pollen allergy is possible through the information of future pollen contents in the air. In spite of the importance of airborne pol len concentration forecasting, it has not been possible to predict the pollen concentrations with great accuracy, and about 25% of the daily pollen forecasts have resulted in failures. Previous analysis of the dynamic characteristics of atmospheric pollen time series indicate that the system can be described by a low dimensional chaotic map. We apply the wavelet transform to study the multifractal characteristics of an a irborne pollen time series. We find the persistence behaviour associated to low pollen concentration values and to the most rare events of highest pollen co ncentration values. The information and the correlation dimensions correspond to a chaotic system showing loss of information with time evolution.
Network Anomaly Detection Based on Wavelet Analysis
Directory of Open Access Journals (Sweden)
Ali A. Ghorbani
2008-11-01
Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.
A New Adaptive Mother Wavelet for Electromagnetic Transient Analysis
Guillén, Daniel; Idárraga-Ospina, Gina; Cortes, Camilo
2016-01-01
Wavelet Transform (WT) is a powerful technique of signal processing, its applications in power systems have been increasing to evaluate power system conditions, such as faults, switching transients, power quality issues, among others. Electromagnetic transients in power systems are due to changes in the network configuration, producing non-periodic signals, which have to be identified to avoid power outages in normal operation or transient conditions. In this paper a methodology to develop a new adaptive mother wavelet for electromagnetic transient analysis is proposed. Classification is carried out with an innovative technique based on adaptive wavelets, where filter bank coefficients will be adapted until a discriminant criterion is optimized. Then, its corresponding filter coefficients will be used to get the new mother wavelet, named wavelet ET, which allowed to identify and to distinguish the high frequency information produced by different electromagnetic transients.
Joint Time-Frequency And Wavelet Analysis - An Introduction
Directory of Open Access Journals (Sweden)
Majkowski Andrzej
2014-12-01
Full Text Available A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in this paper. Unfortunately, there is no correlation between the width of the time-frequency window and its frequency content in the t/f analysis. This property is not valid in the case of a wavelet transform. A wavelet is a wave-like oscillation, which forms its own “wavelet window”. Compression of the wavelet narrows the window, and vice versa. Individual wavelet functions are well localized in time and simultaneously in scale (the equivalent of frequency. The wavelet analysis owes its effectiveness to the pyramid algorithm described by Mallat, which enables fast decomposition of a signal into wavelet components.
WAVOS: a MATLAB toolkit for wavelet analysis and visualization of oscillatory systems
Directory of Open Access Journals (Sweden)
Harang Richard
2012-03-01
Full Text Available Abstract Background Wavelets have proven to be a powerful technique for the analysis of periodic data, such as those that arise in the analysis of circadian oscillators. While many implementations of both continuous and discrete wavelet transforms are available, we are aware of no software that has been designed with the nontechnical end-user in mind. By developing a toolkit that makes these analyses accessible to end users without significant programming experience, we hope to promote the more widespread use of wavelet analysis. Findings We have developed the WAVOS toolkit for wavelet analysis and visualization of oscillatory systems. WAVOS features both the continuous (Morlet and discrete (Daubechies wavelet transforms, with a simple, user-friendly graphical user interface within MATLAB. The interface allows for data to be imported from a number of standard file formats, visualized, processed and analyzed, and exported without use of the command line. Our work has been motivated by the challenges of circadian data, thus default settings appropriate to the analysis of such data have been pre-selected in order to minimize the need for fine-tuning. The toolkit is flexible enough to deal with a wide range of oscillatory signals, however, and may be used in more general contexts. Conclusions We have presented WAVOS: a comprehensive wavelet-based MATLAB toolkit that allows for easy visualization, exploration, and analysis of oscillatory data. WAVOS includes both the Morlet continuous wavelet transform and the Daubechies discrete wavelet transform. We have illustrated the use of WAVOS, and demonstrated its utility for the analysis of circadian data on both bioluminesence and wheel-running data. WAVOS is freely available at http://sourceforge.net/projects/wavos/files/
Variable mass pendulum behaviour processed by wavelet analysis
Caccamo, M. T.; Magazù, S.
2017-01-01
The present work highlights how, in order to characterize the motion of a variable mass pendulum, wavelet analysis can be an effective tool in furnishing information on the time evolution of the oscillation spectral content. In particular, the wavelet transform is applied to process the motion of a hung funnel that loses fine sand at an exponential rate; it is shown how, in contrast to the Fourier transform which furnishes only an average frequency value for the motion, the wavelet approach makes it possible to perform a joint time-frequency analysis. The work is addressed at undergraduate and graduate students.
A comparison of wavelet analysis techniques in digital holograms
Molony, Karen M.; Maycock, Jonathan; McDonald, John B.; Hennelly, Bryan M.; Naughton, Thomas J.
2008-04-01
This study explores the effectiveness of wavelet analysis techniques on digital holograms of real-world 3D objects. Stationary and discrete wavelet transform techniques have been applied for noise reduction and compared. Noise is a common problem in image analysis and successful reduction of noise without degradation of content is difficult to achieve. These wavelet transform denoising techniques are contrasted with traditional noise reduction techniques; mean filtering, median filtering, Fourier filtering. The different approaches are compared in terms of speckle reduction, edge preservation and resolution preservation.
THE WAVELET ANALYSIS METHOD ON THE TRANSIENT SIGNAL
Institute of Scientific and Technical Information of China (English)
吴淼
1996-01-01
Many dynamic signals of mining machines are transient, such as load signals when roadheader's cutting head being cut-in or cut-out and response signals produced by these loads. For these transient signals, the traditional Fourier analysis method is quite inadequate,The limitations of analysis, resolution by using Short-Time Fourier Transform (STFT) on them were discussed in this paper. Because of wavelet transform having the characteristics of flexible window and multiresolution analysis, we try to apply it to analyse these transientsignal. In order to give a pratical example,using D 18 wavelet and Mallat's tree algorithm with MATLAB, the discrete wavelet transform was calculated for the simulating response signals of a three-degree-of freedom vibration system when it was under impulse and random excitations. The results of the wavelet transform made clear its effectiveness and superiority in analysing transient signals of mining machines.
Wavelet analysis of acoustic emission signals from thermal barrier coatings
Institute of Scientific and Technical Information of China (English)
YANG Li; ZHOU Yi-chun
2006-01-01
The wavelet transform is applied to the analysis of acoustic emission signals collected during tensile test of the ZrO2-8% Y2O3 (YSZ) thermal barrier coatings (TBCs). The acoustic emission signals are de-noised using the Daubechies discrete wavelets,and then decomposed into different wavelet levels using the programs developed by the authors. Each level is examined for its specific frequency range. The ratio of energy in different levels to the total energy gives information on the failure modes (coating micro-failures and substrate micro-failures) associated with TBCs system.
ECG signals denoising using wavelet transform and independent component analysis
Liu, Manjin; Hui, Mei; Liu, Ming; Dong, Liquan; Zhao, Zhu; Zhao, Yuejin
2015-08-01
A method of two channel exercise electrocardiograms (ECG) signals denoising based on wavelet transform and independent component analysis is proposed in this paper. First of all, two channel exercise ECG signals are acquired. We decompose these two channel ECG signals into eight layers and add up the useful wavelet coefficients separately, getting two channel ECG signals with no baseline drift and other interference components. However, it still contains electrode movement noise, power frequency interference and other interferences. Secondly, we use these two channel ECG signals processed and one channel signal constructed manually to make further process with independent component analysis, getting the separated ECG signal. We can see the residual noises are removed effectively. Finally, comparative experiment is made with two same channel exercise ECG signals processed directly with independent component analysis and the method this paper proposed, which shows the indexes of signal to noise ratio (SNR) increases 21.916 and the root mean square error (MSE) decreases 2.522, proving the method this paper proposed has high reliability.
Image denoising based on wavelet cone of influence analysis
Pang, Wei; Li, Yufeng
2009-11-01
Donoho et al have proposed a method for denoising by thresholding based on wavelet transform, and indeed, the application of their method to image denoising has been extremely successful. But this method is based on the assumption that the type of noise is only additive Gaussian white noise, which is not efficient to impulse noise. In this paper, a new image denoising algorithm based on wavelet cone of influence (COI) analyzing is proposed, and which can effectively remove the impulse noise and preserve the image edges via undecimated discrete wavelet transform (UDWT). Furthermore, combining with the traditional wavelet thresholding denoising method, it can be also used to restrain more widely type of noise such as Gaussian noise, impulse noise, poisson noise and other mixed noise. Experiment results illustrate the advantages of this method.
Wavelet analysis for ground penetrating radar applications: a case study
Javadi, Mehdi; Ghasemzadeh, Hasan
2017-10-01
Noises may significantly disturb ground penetrating radar (GPR) signals, therefore, filtering undesired information using wavelet analysis would be challenging, despite the fact that several methods have been presented. Noises are gathered by probe, particularly from deep locations, and they may conceal reflections, suffering from small altitudes, because of signal attenuation. Multiple engineering fields need data analysis to distinguish valued material, based on information obtained by underground observations. Using wavelets as one of the useful methods for analyzing data is considered in this paper. However, optimal wavelet analysis would be challenging in the realm of exploring GPR signals. There is no doubt that accounting for wavelet function, decomposition level, threshold estimation method and threshold transformation, in the matter of de-noising and investigating signals, is of great importance; they must be chosen with judgment as they influence the results enormously if they are not carefully designated. Multiple wavelet functions are applied to perform de-noising and reconstruction on synthetic noisy signals generated by the finite-difference time-domain (FDTD) method to account for the most appropriate function for the purpose. In addition, various possible decomposition levels, threshold estimation methods and threshold transformations in the de-noising procedure are tested. The optimal wavelet analysis is also evaluated by examining real data acquired from several antenna frequencies which are common in engineering practice.
On robust kalman filtering with using wavelet analysis
Lobach, V. I.
2013-01-01
One presents a nonlinear filtering algorithm that propagates the entire condi- tional probability density functions. These functions are recursively computed in efficient manner using the discrete wavelet transform. With the multiresolution analysis we can speed up the computation by ignoring the high-frequency details of the probability density function up to a certain level. The level of the wavelet decomposition can be determined at each time step adaptively.
Wavelets Applied to CMB Maps a Multiresolution Analysis for Denoising
Sanz, J L; Cayon, L; Martínez-González, E; Barriero, R B; Toffolatti, L
1999-01-01
Analysis and denoising of Cosmic Microwave Background (CMB) maps are performed using wavelet multiresolution techniques. The method is tested on $12^{\\circ}.8\\times 12^{\\circ}.8$ maps with resolution resembling the experimental one expected for future high resolution space observations. Semianalytic formulae of the variance of wavelet coefficients are given for the Haar and Mexican Hat wavelet bases. Results are presented for the standard Cold Dark Matter (CDM) model. Denoising of simulated maps is carried out by removal of wavelet coefficients dominated by instrumental noise. CMB maps with a signal-to-noise, $S/N \\sim 1$, are denoised with an error improvement factor between 3 and 5. Moreover we have also tested how well the CMB temperature power spectrum is recovered after denoising. We are able to reconstruct the $C_{\\ell}$'s up to $l\\sim 1500$ with errors always below $20% $ in cases with $S/N \\ge 1$.
Analysis and removing noise from speech using wavelet transform
Tomala, Karel; Voznak, Miroslav; Partila, Pavol; Rezac, Filip; Safarik, Jakub
2013-05-01
The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.
Chai, Bing-Bing; Vass, Jozsef; Zhuang, Xinhua
1997-04-01
Recent success in wavelet coding is mainly attributed to the recognition of importance of data organization. There has been several very competitive wavelet codecs developed, namely, Shapiro's Embedded Zerotree Wavelets (EZW), Servetto et. al.'s Morphological Representation of Wavelet Data (MRWD), and Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT). In this paper, we propose a new image compression algorithm called Significant-Linked Connected Component Analysis (SLCCA) of wavelet coefficients. SLCCA exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. A so-called significant link between connected components is designed to reduce the positional overhead of MRWD. In addition, the significant coefficients' magnitude are encoded in bit plane order to match the probability model of the adaptive arithmetic coder. Experiments show that SLCCA outperforms both EZW and MRWD, and is tied with SPIHT. Furthermore, it is observed that SLCCA generally has the best performance on images with large portion of texture. When applied to fingerprint image compression, it outperforms FBI's wavelet scalar quantization by about 1 dB.
[Application of SVM and wavelet analysis in EEG classification].
Zhao, Jianlin; Zhou, Weidong; Liu, Kai; Cai, Dongmei
2011-04-01
We employed two methods of support vector machines (SVM) combined with two kinds of wavelet analysis to classify these EEG signals, on the basis of the different profiles, energy, and frequency characteristics of the EEG during the seizures. One method was to classify these signals using waveform characteristics of the EEG signal. The other was to classify these signals based on fluctuation index and variation coefficient of the EEG signal. We compared the classification accuracies of these two methods with the intermittent EEG and epileptic EEG. The results of the experiments showed that both the two methods for distinguishing epileptic EEG and interictal EEG can achieve an effective performance. It was also confirmed that the latter, the method based on the fluctuation index and variation coefficient, possesses a better effect of classification.
Wavelets in music analysis and synthesis: timbre analysis and perspectives
Alves Faria, Regis R.; Ruschioni, Ruggero A.; Zuffo, Joao A.
1996-10-01
Music is a vital element in the process of comprehending the world where we live and interact with. Frequency it exerts a subtle but expressive influence over a society's evolution line. Analysis and synthesis of music and musical instruments has always been associated with forefront technologies available at each period of human history, and there is no surprise in witnessing now the use of digital technologies and sophisticated mathematical tools supporting its development. Fourier techniques have been employed for years as a tool to analyze timbres' spectral characteristics, and re-synthesize them from these extracted parameters. Recently many modern implementations, based on spectral modeling techniques, have been leading to the development of new generations of music synthesizers, capable of reproducing natural sounds with high fidelity, and producing novel timbres as well. Wavelets are a promising tool on the development of new generations of music synthesizers, counting on its advantages over the Fourier techniques in representing non-periodic and transient signals, with complex fine textures, as found in music. In this paper we propose and introduce the use of wavelets addressing its perspectives towards musical applications. The central idea is to investigate the capacities of wavelets in analyzing, extracting features and altering fine timbre components in a multiresolution time- scale, so as to produce high quality synthesized musical sounds.
ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform
Kaur, Inderbir; Rajni, Rajni; Marwaha, Anupma
2016-12-01
Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.
Data Clustering Analysis Based on Wavelet Feature Extraction
Institute of Scientific and Technical Information of China (English)
QIANYuntao; TANGYuanyan
2003-01-01
A novel wavelet-based data clustering method is presented in this paper, which includes wavelet feature extraction and cluster growing algorithm. Wavelet transform can provide rich and diversified information for representing the global and local inherent structures of dataset. therefore, it is a very powerful tool for clustering feature extraction. As an unsupervised classification, the target of clustering analysis is dependent on the specific clustering criteria. Several criteria that should be con-sidered for general-purpose clustering algorithm are pro-posed. And the cluster growing algorithm is also con-structed to connect clustering criteria with wavelet fea-tures. Compared with other popular clustering methods,our clustering approach provides multi-resolution cluster-ing results,needs few prior parameters, correctly deals with irregularly shaped clusters, and is insensitive to noises and outliers. As this wavelet-based clustering method isaimed at solving two-dimensional data clustering prob-lem, for high-dimensional datasets, self-organizing mapand U-matrlx method are applied to transform them intotwo-dimensional Euclidean space, so that high-dimensional data clustering analysis,Results on some sim-ulated data and standard test data are reported to illus-trate the power of our method.
WAVELET ANALYSIS OF INTERANNUAL VARIATION OF TROPICAL CYCLONES IN GUANGDONG
Institute of Scientific and Technical Information of China (English)
刘春霞
2002-01-01
Climatological laws are studied for the annual frequency of tropical cyclone occurrence and the date of the yearly first landfall,which take place in the Guangdong province or pose serious threats on it from 1951 to 1999,using the data in the Yearly Book on Typhoons.A new method that has developed over recent years for the study of temporal sequences,the wavelet analysis,is used,in addition to more common statistical approaches.By analyzing two wavelet functions,MHAT and MORLET,we have compared the results of transformation of the wavelets provided that other conditions remain unchanged.It is discovered that the variance of MORLET wavelet has better indication of primary periods;period-time sequence charts can reflect major affecting periods for individual sections of time;when compared with the original sequence,the chart shows a little shift.On the other hand,such shift is absent in the MHAT wavelet,but its higher frequency part of variance covers up the primary periods to make its variance less predominant as compared to the MORLET wavelet.Besides,the work compares two different assumptions of an amplifying factor a.It is found that primary periods can be shown more clearly in the variance when a takes the exponential of 2 than it takes values continuously.Studying the annual frequency of tropical cyclones and the date of first appearance for periodic patterns,we have found that the primary periods extracted by this approach are similar to those obtained by wavelet transformation.
Detecting Inhomogeneity in Daily Climate Series Using Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
YAN Zhongwei; Phil D.JONES
2008-01-01
A wavelet method was applied to detect inhomogeneities in daily meteorological series,data which are being increasingly applied in studies of climate extremes.The wavelet method has been applied to a few well-established long-term daily temperature series back to the 18th century,which have been "homogenized" with conventional approaches.Various types of problems remaining in the series were revealed with the wavelet method.Their influences on analyses of change in climate extremes are discussed.The results have importance for understanding issues in conventional climate data processing and for development of improved methods of homogenization in order to improve analysis of climate extremes based on daily data.
Long memory analysis by using maximal overlapping discrete wavelet transform
Shafie, Nur Amalina binti; Ismail, Mohd Tahir; Isa, Zaidi
2015-05-01
Long memory process is the asymptotic decay of the autocorrelation or spectral density around zero. The main objective of this paper is to do a long memory analysis by using the Maximal Overlapping Discrete Wavelet Transform (MODWT) based on wavelet variance. In doing so, stock market of Malaysia, China, Singapore, Japan and United States of America are used. The risk of long term and short term investment are also being looked into. MODWT can be analyzed with time domain and frequency domain simultaneously and decomposing wavelet variance to different scales without loss any information. All countries under studied show that they have long memory. Subprime mortgage crisis in 2007 is occurred in the United States of America are possible affect to the major trading countries. Short term investment is more risky than long term investment.
Nonstationary Dynamics Data Analysis with Wavelet-SVD Filtering
Brenner, Marty; Groutage, Dale; Bessette, Denis (Technical Monitor)
2001-01-01
Nonstationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied.
Application of wavelet analysis to crustal deformation data processing
Institute of Scientific and Technical Information of China (English)
张燕; 吴云; 刘永启; 施顺英
2004-01-01
The time-frequency analysis and anomaly detection of wavelet transformation make the method irresistibly advantageous in non-stable signal processing. In the paper, the two characteristics are analyzed and demonstrated withsynthetic signal. By applying wavelet transformation to deformation data processing, we find that about 4 monthsbefore strong earthquakes, several deformation stations near the epicenter received at the same time the abnormalsignal with the same frequency and the period from several days to more than ten days. The GPS observation stations near the epicenter all received the abnormal signal whose period is from 3 months to half a year. These abnormal signals are possibly earthquake precursors.
Blind Component Separation in Wavelet Space: Application to CMB Analysis
Directory of Open Access Journals (Sweden)
J. Delabrouille
2005-09-01
Full Text Available It is a recurrent issue in astronomical data analysis that observations are incomplete maps with missing patches or intentionally masked parts. In addition, many astrophysical emissions are nonstationary processes over the sky. All these effects impair data processing techniques which work in the Fourier domain. Spectral matching ICA (SMICA is a source separation method based on spectral matching in Fourier space designed for the separation of diffuse astrophysical emissions in cosmic microwave background observations. This paper proposes an extension of SMICA to the wavelet domain and demonstrates the effectiveness of wavelet-based statistics for dealing with gaps in the data.
Wavelet-analysis for Laser Images of Blood Plasma
Directory of Open Access Journals (Sweden)
ANGELSKY, A.-P.
2011-05-01
Full Text Available The possibilities of the local wavelet-analysis of polarization-inhomogeneous laser image of human blood plasma were considered. The set of statistics, correlation and fractal parameters of the distributions of wavelet-coefficients that are characterize different scales of the polarization maps of polycrystalline networks of amino acids of blood plasma were defined. The criteria for the differentiation of the transformation of birefringence optical-anisotropic structures of blood plasma at different scales of their geometric dimensions were determined.
Abstract harmonic analysis of continuous wavelet transforms
Führ, Hartmut
2005-01-01
This volume contains a systematic discussion of wavelet-type inversion formulae based on group representations, and their close connection to the Plancherel formula for locally compact groups. The connection is demonstrated by the discussion of a toy example, and then employed for two purposes: Mathematically, it serves as a powerful tool, yielding existence results and criteria for inversion formulae which generalize many of the known results. Moreover, the connection provides the starting point for a – reasonably self-contained – exposition of Plancherel theory. Therefore, the book can also be read as a problem-driven introduction to the Plancherel formula.
Wavelet analysis of the nuclear phase space
Energy Technology Data Exchange (ETDEWEB)
Jouault, B.; Sebille, F.; Mota, V. de la
1997-12-31
The description of transport phenomena in nuclear matter is addressed in a new approach based on the mathematical theory of wavelets and the projection methods of statistical physics. The advantage of this framework is to offer the opportunity to use information concepts common to both the formulation of physical properties and the mathematical description. This paper focuses on two features, the extraction of relevant informations using the geometrical properties of the underlying phase space and the optimization of the theoretical and numerical treatments based on convenient choices of the representation spaces. (author). 34 refs.
A time-scale analysis of systematic risk: wavelet-based approach
Khalfaoui Rabeh, K; Boutahar Mohamed, B
2011-01-01
The paper studies the impact of different time-scales on the market risk of individual stock market returns and of a given portfolio in Paris Stock Market by applying the wavelet analysis. To investigate the scaling properties of stock market returns and the lead/lag relationship between them at different scales, wavelet variance and crosscorrelations analyses are used. According to wavelet variance, stock returns exhibit long memory dynamics. The wavelet cross-correlation analysis shows that...
Directory of Open Access Journals (Sweden)
Li Song
2010-04-01
Full Text Available Abstract Background Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results We developed a novel discrete wavelet transform (DWT and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP, a commonly used open source proteomics analysis pipeline. Conclusions We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.
Enhancement of damage indicators in wavelet and curvature analysis
Indian Academy of Sciences (India)
B K Raghu Prasad; N Lakshmanan; K Muthumani; N Gopalakrishnan
2006-08-01
Damage in a structural element induces a small perturbation in its static or dynamic displacement proﬁle which can be captured by wavelet analysis. The paper presents the wavelet analysis of damaged linear structural elements using DB4 or BIOR6·8 family of wavelets. An expression is developed for computing the natural frequencies of a damaged beam using ﬁrst order perturbation theory. Starting with a localized reduction of EI at the mid-span of a simply supported beam, damage modelling is done for a typical steel beam element. Wavelet analysis is performed for this damage model for displacement, rotation and curvature mode shapes as well as static displacement proﬁles. Damage indicators like displacement, slope and curvature are magniﬁed under higher modes. Instantaneous step-wise linearity is assumed for all the nonlinear elements. A localization scheme with arbitrararily located curvature nodes within a pseudo span is developed for steady state dynamic loads, such that curvature response and damages are maximized and the scheme is numerically tested and proved.
Avalanches in a granular stick-slip experiment: detection using wavelets
Abed Zadeh, Aghil; Barés, Jonathan; Behringer, Robert P.
2017-06-01
Avalanches have been experimentally investigated in a wide range of physical systems from granular physics to friction. Here, we measure and detect avalanches in a 2D granular stick-slip experiment. We discuss the conventional way of signal processing for avalanche extraction and how statistics depend on several parameters that are chosen in the analysis process. Then, we introduce another way of detecting avalanches using wavelet transformations that can be applied in many other systems. We show that by using this method and measuring Lipschitz exponents, we can intelligently detect noise in a signal, which leads to a better avalanche extraction and more reliable avalanche statistics.
Wavelet analysis of sunspot relative numbers
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The time series of the monthly smoothed sunspot numbers in 1749-2000 is analyzed with the wavelet.The result shows that besides the known time-variation of the period about 11 years, other main periods of the sunspot numbers, such as the periods of about 100 years and so on,vary with time. We suggest that the time-variation of the main periods is the manifestation of the complex variation of sunspot numbers. It is significant to make a thorough study of the character and mechanism of the time-variation of the periods for proving prediction of sunspot numbers, especially for understanding the variation process of sunspot numbers.
DEFF Research Database (Denmark)
Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Kirkegaard, Poul Henning;
2014-01-01
The presented study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to a SSP 34m blade mounted on a test rig. Operational modal analysis (OMA) was conducted to obtain....... Since only a limited number of measurement points were utilized in the experiments, valid damage identification can only be obtained when employing high-frequency modes....
Institute of Scientific and Technical Information of China (English)
DUAN Chen-dong; JIANG Hong-kai; HE Zheng-jia
2004-01-01
In order to make trend analysis and prediction to acquisition data in a mechanical equipment condition monitoring system, a new method of trend feature extraction and prediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisition data by means of second generation wavelet transform (SGWT). Firstly, taking the vanishing moment number of the predictor as a constraint, the linear predictor and updater are designed according to the acquisition data by using symmetrical interpolating scheme. Then the trend of the data is obtained through doing SGWT decomposition, threshold processing and SGWT reconstruction. Secondly, under the constraint of the vanishing moment number of the predictor, another predictor based on the acquisition data is devised to predict the future trend of the data using a non-symmetrical interpolating scheme. A one-step prediction algorithm is presented to predict the future evolution trend with historical data. The proposed method obtained a desirable effect in peak-to-peak value trend analysis for a machine set in an oil refinery.
Mathematical principles of signal processing Fourier and wavelet analysis
Brémaud, Pierre
2002-01-01
Fourier analysis is one of the most useful tools in many applied sciences. The recent developments of wavelet analysis indicates that in spite of its long history and well-established applications, the field is still one of active research. This text bridges the gap between engineering and mathematics, providing a rigorously mathematical introduction of Fourier analysis, wavelet analysis and related mathematical methods, while emphasizing their uses in signal processing and other applications in communications engineering. The interplay between Fourier series and Fourier transforms is at the heart of signal processing, which is couched most naturally in terms of the Dirac delta function and Lebesgue integrals. The exposition is organized into four parts. The first is a discussion of one-dimensional Fourier theory, including the classical results on convergence and the Poisson sum formula. The second part is devoted to the mathematical foundations of signal processing - sampling, filtering, digital signal proc...
Application of multifractal wavelet analysis to spontaneous fermentation processes
Ibarra-Junquera, V; Escalante-Minakata, P; Rosu, H C
2007-01-01
An algorithm is presented here to get more detailed information, of mixed culture type, based exclusively on the biomass concentrations data for fermentation processes. The analysis is performed having available only the on-line measurements of the redox potential. It is a two-step procedure which includes an Artificial Neural Network (ANN) that relates the redox potential to the biomass concentrations in the first step. Next, a multifractal wavelet analysis is performed using the biomass estimates of the process. In this context, our results show that the redox potential is a valuable indicator of microorganism metabolic activity during the spontaneous fermentation. In this paper, the detailed design of the multifractal wavelet analysis is presented, as well as its direct experimental application at the laboratory level
Optimizing cardiac resuscitation outcomes using wavelet analysis.
Umapathy, K; Krishnan, S; Masse, S; Hu, X; Dorian, P; Nanthakumar, K
2009-01-01
Ventricular fibrillation (VF) is the most lethal of cardiac arrhythmias that leads to sudden cardiac death if untreated within minutes of its occurrence. Defibrillation using electric shock resets the heart to return to spontaneous circulation (ROSC) state, however the success of which depends on various factors such as the viability of myocardium and the time lag between the onset of VF to defibrillation. Recent studies have reported that performing cardio pulmonary resuscitation (CPR) procedure prior to applying shock increases the survival rate especially when VF is untreated for more than 5 minutes. Considering the limited time within which the VF has to be treated for better survival rates, the choice of the right therapy (shock parameters, shock first or CPR first, drug administration) is vital. In aiding this choice, it would be of immense help for emergency medical staff (EMS) if an objective feedback could be provided at near real-time rate on the VF characteristics and its relation to the shock outcomes. Existing works in the literature have demonstrated correlation between the characteristics of the VF waveform and the outcome (ROSC) of the defibrillation. The proposed work improves on this by attempting to arrive at a near real-time monitoring tool in aiding the EMS staff. Using data collected from 16 pigs during VF, the proposed wavelet methodology achieved an overall accuracy of 94% in successfully predicting the shock outcomes.
Comparative Genomics via Wavelet Analysis for Closely Related Bacteria
Directory of Open Access Journals (Sweden)
Jiuzhou Song
2004-01-01
Full Text Available Comparative genomics has been a valuable method for extracting and extrapolating genome information among closely related bacteria. The efficiency of the traditional methods is extremely influenced by the software method used. To overcome the problem here, we propose using wavelet analysis to perform comparative genomics. First, global comparison using wavelet analysis gives the difference at a quantitative level. Then local comparison using keto-excess or purine-excess plots shows precise positions of inversions, translocations, and horizontally transferred DNA fragments. We firstly found that the level of energy spectra difference is related to the similarity of bacteria strains; it could be a quantitative index to describe the similarities of genomes. The strategy is described in detail by comparisons of closely related strains: S.typhi CT18, S.typhi Ty2, S.typhimurium LT2, H.pylori 26695, and H.pylori J99.
Comparative Genomics via Wavelet Analysis for Closely Related Bacteria
Song, Jiuzhou; Ware, Tony; Liu, Shu-Lin; Surette, M.
2004-12-01
Comparative genomics has been a valuable method for extracting and extrapolating genome information among closely related bacteria. The efficiency of the traditional methods is extremely influenced by the software method used. To overcome the problem here, we propose using wavelet analysis to perform comparative genomics. First, global comparison using wavelet analysis gives the difference at a quantitative level. Then local comparison using keto-excess or purine-excess plots shows precise positions of inversions, translocations, and horizontally transferred DNA fragments. We firstly found that the level of energy spectra difference is related to the similarity of bacteria strains; it could be a quantitative index to describe the similarities of genomes. The strategy is described in detail by comparisons of closely related strains: S.typhi CT18, S.typhi Ty2, S.typhimurium LT2, H.pylori 26695, and H.pylori J99.
Ground extraction from airborne laser data based on wavelet analysis
Xu, Liang; Yang, Yan; Jiang, Bowen; Li, Jia
2007-11-01
With the advantages of high resolution and accuracy, airborne laser scanning data are widely used in topographic mapping. In order to generate a DTM, measurements from object features such as buildings, vehicles and vegetation have to be classified and removed. However, the automatic extraction of bare earth from point clouds acquired by airborne laser scanning equipment remains a problem in LIDAR data filtering nowadays. In this paper, a filter algorithm based on wavelet analysis is proposed. Relying on the capability of detecting discontinuities of continuous wavelet transform and the feature of multi-resolution analysis, the object points can be removed, while ground data are preserved. In order to evaluate the performance of this approach, we applied it to the data set used in the ISPRS filter test in 2003. 15 samples have been tested by the proposed approach. Results showed that it filtered most of the objects like vegetation and buildings, and extracted a well defined ground model.
SAMPLING PRINCIPLE AND TECHNOLOGY IN WAVELET ANALYSIS FOR SIGNALS
Institute of Scientific and Technical Information of China (English)
1998-01-01
Sampling principle and characteristics and edge effect of orthogonal wavelet transform of signals are researched. Two samples of signals and wavelet bases must be taken in wavelet transform. In the second sample sampling interval or sampling length in different frequency range will be automatically adjusted. Wavelet transform can detect singular points. Both ends of signals are singular points. Edge effect is not avoidable.
Wavelet analysis on paleomagnetic (and computer simulated VGP time series
Directory of Open Access Journals (Sweden)
A. Siniscalchi
2003-06-01
Full Text Available We present Continuous Wavelet Transform (CWT data analysis of Virtual Geomagnetic Pole (VGP latitude time series. The analyzed time series are sedimentary paleomagnetic and geodynamo simulated data. Two mother wavelets (the Morlet function and the first derivative of a Gaussian function are used in order to detect features related to the spectral content as well as polarity excursions and reversals. By means of the Morlet wavelet, we estimate both the global spectrum and the time evolution of the spectral content of the paleomagnetic data series. Some peaks corresponding to the orbital components are revealed by the spectra and the local analysis helped disclose their statistical significance. Even if this feature could be an indication of orbital influence on geodynamo, other interpretations are possible. In particular, we note a correspondence of local spectral peaks with the appearance of the excursions in the series. The comparison among the paleomagnetic and simulated spectra shows a similarity in the high frequency region indicating that their degree of regularity is analogous. By means of Gaussian first derivative wavelet, reversals and excursions of polarity were sought. The analysis was performed first on the simulated data, to have a guide in understanding the features present in the more complex paleomagnetic data. Various excursions and reversals have been identified, despite of the prevalent normality of the series and its inherent noise. The found relative chronology of the paleomagnetic data reversals was compared with a coeval global polarity time scale (Channel et al., 1995. The relative lengths of polarity stability intervals are found similar, but a general shift appears between the two scales, that could be due to the datation uncertainties of the Hauterivian/Barremian boundary.
On The Fourier And Wavelet Analysis Of Coronal Time Series
Auchère, F; Bocchialini, K; Buchlin, E; Solomon, J
2016-01-01
Using Fourier and wavelet analysis, we critically re-assess the significance of our detection of periodic pulsations in coronal loops. We show that the proper identification of the frequency dependence and statistical properties of the different components of the power spectra provies a strong argument against the common practice of data detrending, which tends to produce spurious detections around the cut-off frequency of the filter. In addition, the white and red noise models built into the widely used wavelet code of Torrence & Compo cannot, in most cases, adequately represent the power spectra of coronal time series, thus also possibly causing false positives. Both effects suggest that several reports of periodic phenomena should be re-examined. The Torrence & Compo code nonetheless effectively computes rigorous confidence levels if provided with pertinent models of mean power spectra, and we describe the appropriate manner in which to call its core routines. We recall the meaning of the default c...
Blind component separation in wavelet space. Application to CMB analysis
Moudden, Y; Starck, J L; Delabrouille, J
2004-01-01
It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse astrophysical emissions in Cosmic Microwave Background observations. We show here that wavelets, which are standard tools in processing non stationary data, can profitably be used to extend SMICA. Among possible applications, it is shown that gaps in data are dealt with more conveniently and with better results using this extension, wSMICA, in place of the original SMICA. The performances of these two methods are compared on simulated CMB data sets, demonstrating the advantageous use of wavelets.
Wavelet analysis of the seismograms for tsunami warning
Directory of Open Access Journals (Sweden)
A. Chamoli
2010-10-01
Full Text Available The complexity in the tsunami phenomenon makes the available warning systems not much effective in the practical situations. The problem arises due to the time lapsed in the data transfer, processing and modeling. The modeling and simulation needs the input fault geometry and mechanism of the earthquake. The estimation of these parameters and other aprior information increases the utilized time for making any warning. Here, the wavelet analysis is used to identify the tsunamigenesis of an earthquake. The frequency content of the seismogram in time scale domain is examined using wavelet transform. The energy content in high frequencies is calculated and gives a threshold for tsunami warnings. Only first few minutes of the seismograms of the earthquake events are used for quick estimation. The results for the earthquake events of Andaman Sumatra region and other historic events are promising.
Wavelet analysis on adeles and pseudo-differential operators
Khrennikov, A Yu; Shelkovich, V M
2011-01-01
This paper is devoted to wavelet analysis on adele ring $\\bA$ and the theory of pseudo-differential operators. We develop the technique which gives the possibility to generalize finite-dimensional results of wavelet analysis to the case of adeles $\\bA$ by using infinite tensor products of Hilbert spaces. The adele ring is roughly speaking a subring of the direct product of all possible ($p$-adic and Archimedean) completions $\\bQ_p$ of the field of rational numbers $\\bQ$ with some conditions at infinity. Using our technique, we prove that $L^2(\\bA)=\\otimes_{e,p\\in\\{\\infty,2,3,5,...}}L^2({\\bQ}_{p})$ is the infinite tensor product of the spaces $L^2({\\bQ}_{p})$ with a stabilization $e=(e_p)_p$, where $e_p(x)=\\Omega(|x|_p)\\in L^2({\\bQ}_{p})$, and $\\Omega$ is a characteristic function of the unit interval $[0,\\,1]$, $\\bQ_p$ is the field of $p$-adic numbers, $p=2,3,5,...$; $\\bQ_\\infty=\\bR$. This description allows us to construct an infinite family of Haar wavelet bases on $L^2(\\bA)$ which can be obtained by shifts...
Wavelet analysis in ecology and epidemiology: impact of statistical tests.
Cazelles, Bernard; Cazelles, Kévin; Chavez, Mario
2014-02-06
Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.
Wavelet-based multifractal analysis of laser biopsy imagery
Jagtap, Jaidip; Panigrahi, Prasanta K; Pradhan, Asima
2011-01-01
In this work, we report a wavelet based multi-fractal study of images of dysplastic and neoplastic HE- stained human cervical tissues captured in the transmission mode when illuminated by a laser light (He-Ne 632.8nm laser). It is well known that the morphological changes occurring during the progression of diseases like cancer manifest in their optical properties which can be probed for differentiating the various stages of cancer. Here, we use the multi-resolution properties of the wavelet transform to analyze the optical changes. For this, we have used a novel laser imagery technique which provides us with a composite image of the absorption by the different cellular organelles. As the disease progresses, due to the growth of new cells, the ratio of the organelle to cellular volume changes manifesting in the laser imagery of such tissues. In order to develop a metric that can quantify the changes in such systems, we make use of the wavelet-based fluctuation analysis. The changing self- similarity during di...
RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM
Directory of Open Access Journals (Sweden)
Celso A. G. Santos
2016-01-01
Full Text Available The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009 from a raingauge and also using daily rainfall data (1998–2013 from the Tropical Rainfall Measuring Mission (TRMM. The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256– 512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.
On the Fourier and Wavelet Analysis of Coronal Time Series
Auchère, F.; Froment, C.; Bocchialini, K.; Buchlin, E.; Solomon, J.
2016-07-01
Using Fourier and wavelet analysis, we critically re-assess the significance of our detection of periodic pulsations in coronal loops. We show that the proper identification of the frequency dependence and statistical properties of the different components of the power spectra provides a strong argument against the common practice of data detrending, which tends to produce spurious detections around the cut-off frequency of the filter. In addition, the white and red noise models built into the widely used wavelet code of Torrence & Compo cannot, in most cases, adequately represent the power spectra of coronal time series, thus also possibly causing false positives. Both effects suggest that several reports of periodic phenomena should be re-examined. The Torrence & Compo code nonetheless effectively computes rigorous confidence levels if provided with pertinent models of mean power spectra, and we describe the appropriate manner in which to call its core routines. We recall the meaning of the default confidence levels output from the code, and we propose new Monte-Carlo-derived levels that take into account the total number of degrees of freedom in the wavelet spectra. These improvements allow us to confirm that the power peaks that we detected have a very low probability of being caused by noise.
Institute of Scientific and Technical Information of China (English)
KUMARAYAPA Ajith; ZHANG Ye
2007-01-01
In this paper, more efficient, low-complexity and reliable region of interest (ROI) image codec for compressing smooth low texture remote sensing images is proposed. We explore the efficiency of the modified ROI codec with respect to the selected set of convenient wavelet filters, which is a novel method. Such ROI coding experiment analysis representing low bit rate lossy to high quality lossless reconstruction with timing analysis is useful for improving remote sensing ground truth surveillance efficiency in terms of time and quality. The subjective [i.e. fair, five observer (HVS) evaluations using enhanced 3D picture view Hyper memory display technology] and the objective results revealed that for faster ground truth ROI coding applications, the Symlet-4 adaptation performs better than Biorthogonal 4.4 and Biorthogonal 6.8. However, the discrete Meyer wavelet adaptation is the best solution for delayed ROI image reconstructions.
Features of long-term health monitored strains of a bridge with wavelet analysis
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
This paper analyses the five years' monitored strains collected from a long-term health monitoring system installed on a bridge with wavelet transform.In the analysis,the monitored strains are pre-processed,features of the monitored data are summarized briefly.The influences of the base functions on the results of wavelet analysis are studied simultaneously.The results show that the db wavelet is a good mother wavelet function in the analysis,and the order N should be larger than 20,but less than 46 in deco...
A Remark on the Mallat Pyramidal Algorithm of Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
无
1997-01-01
The exact relationships between the lenthgs of scale sequences and wavelet sequences in the Mallat pyramidal algorithm for computing wavelet trans-form coefficients are obtained,and the maximum possible scale of arbitrary discrete signal is derived.
Constraints on CPT violation from WMAP three year polarization data: a wavelet analysis
Cabella, Paolo; Silk, Joseph
2007-01-01
We perform a wavelet analysis of the temperature and polarization maps of the Cosmic Microwave Background (CMB) delivered by the WMAP experiment in search for a parity violating signal. Such a signal could be seeded by new physics beyond the standard model, for which the Lorentz and CPT symmetries may not hold. Under these circumstances, the linear polarization direction of a CMB photon may get rotated during its cosmological journey, a phenomenon also called cosmological birefringence. Recently, Feng et al. have analyzed a subset the WMAP and BOOMERanG 2003 angular power spectra of the CMB, deriving a constraint that mildly favors a non zero rotation. By using wavelet transforms we set a tighter limit on the CMB photon rotation angle \\Delta\\alpha= -2.5 \\pm 3.0 (\\Delta\\alpha= -2.5 \\pm 6.0) at the one (two) \\sigma level, consistent with a null detection.
A NOVEL METHOD FOR NETWORK WORM DETECTION BASED ON WAVELET PACKET ANALYSIS
Institute of Scientific and Technical Information of China (English)
Liao Mingtao; Zhang Deyun; Hou Lin
2006-01-01
Objective To detect unknown network worm at its early propagation stage. Methods On the basis of characteristics of network worm attack, the concept of failed connection flow (FCT) was defined. Based on wavelet packet analysis of FCT time series, this method computed the energy associated with each wavelet packet of FCT time series, transformed the FCT time series into a series of energy distribution vector on frequency domain, then a trained K-nearest neighbor (KNN) classifier was applied to identify the worm. Results The experiment showed that the method could identify network worm when the worm started to scan. Compared to theoretic value, the identification error ratio was 5.69%. Conclusion The method can detect unknown network worm at its early propagation stage effectively.
Cheng, Tao; Rivard, Benoit; Sánchez-Azofeifa, Arturo G; Féret, Jean-Baptiste; Jacquemoud, Stephane; Ustin, Susan L
2012-08-15
Leaf water content is an important variable for understanding plant physiological properties. This study evaluates a spectral analysis approach, continuous wavelet analysis (CWA), for the spectroscopic estimation of leaf gravimetric water content (GWC, %) and determines robust spectral indicators of GWC across a wide range of plant species from different ecosystems. CWA is both applied to the Leaf Optical Properties Experiment (LOPEX) data set and a synthetic data set consisting of leaf reflectance spectra simulated using the leaf optical properties spectra (PROSPECT) model. The results for the two data sets, including wavelet feature selection and GWC prediction derived using those features, are compared to the results obtained from a previous study for leaf samples collected in the Republic of Panamá (PANAMA), to assess the predictive capabilities and robustness of CWA across species. Furthermore, predictive models of GWC using wavelet features derived from PROSPECT simulations are examined to assess their applicability to measured data. The two measured data sets (LOPEX and PANAMA) reveal five common wavelet feature regions that correlate well with leaf GWC. All three data sets display common wavelet features in three wavelength regions that span 1732-1736 nm at scale 4, 1874-1878 nm at scale 6, and 1338-1341 nm at scale 7 and produce accurate estimates of leaf GWC. This confirms the applicability of the wavelet-based methodology for estimating leaf GWC for leaves representative of various ecosystems. The PROSPECT-derived predictive models perform well on the LOPEX data set but are less successful on the PANAMA data set. The selection of high-scale and low-scale features emphasizes significant changes in both overall amplitude over broad spectral regions and local spectral shape over narrower regions in response to changes in leaf GWC. The wavelet-based spectral analysis tool adds a new dimension to the modeling of plant physiological properties with
Scale-Dependent Representations of Relief Based on Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Automatic generalization of geographic information is the core of multi-scale representation of spatial data,but the scale-dependent generalization methods are far from abundant because of its extreme complicacy.This paper puts forward a new consistency model about scale-dependent representations of relief based on wavelet analysis,and discusses the thresholds in the model so as to acquire the continual representations of relief with different details between scales.The model not only meets the need of automatic generalization but also is scale-dependent completely.Some practical examples are given.
Employing wavelet for neutron tracks distribution analysis in PADC detectors
Ferrari, Paolo; Campani, Lorenzo; Mariotti, Francesca
2017-08-01
PADC nuclear track dosemeters are used for fast neutron monitoring. A system, based on one-shot image acquisition is employed and a simple image analysis, based on the track counting, is performed in a series of image regions. When this procedure fails a different approach is needed. In the present paper we tested a wavelet transform based algorithm to detect possible ;patterns; in tracks distributions that could be associated to dosemeter anomalies, assuming that a neutron exposure should produce a homogenous distribution. The algorithm, tested with samples of our dosimetric service, showed its potential effectiveness and capabilities.
Application of the wavelets in multiparticle production experiments
Georgopoulos, G; Vassiliou, Maria
2000-01-01
In high energy nucleus-nucleus collisions (SPS, RHIC, LHC) and in cosmic rays interactions, many particles are produced in the available phase space. We make an attempt to apply the wavelets technique in order to classify such events according to the event pattern and also to locate the so-called `clustering' in a distribution. After describing the method, we demonstrate its power: (a) to a single event, produced by a pion condensation theoretical model; (b) to a sample of Pb-Pb experimental data at 158 GeV/c per nucleon; (c) to simulated events taking into account all the experimental uncertainties. (6 refs).
Wavelet Analysis for Investigation of Precise Gnss Solutions' Credibility
Bogusz, Janusz; Klos, Anna
2010-01-01
This publication presents the results of searching short-term oscillations of the ASG network sites using wavelet transform. Polish Active Geodetic Network (ASG-EUPOS) is the multifunctional precise satellite positioning system established by the Head Office of Geodesy and Cartography in 2008. The adjusted network consisted of over 130 stations from Poland and neighbouring countries. The period covered observations gathered from June 2008 to July 2010. The method of processing elaborated in the CAG (Centre of Applied Geomatics, Warsaw Military University of Technology), which is one of the 17 EPN LAC (EUREF Permanent Network Local Analysis Centre) acting now in Europe, established at the end of 2009, is similar with the official one used in EPN. It is based on the Bernese 5.0 software, but the difference to the EPN's solutions lies in the resolution of resulting coordinates. In the presented research the 1-hour sampling rate with 3-hour windowing (66% of correlation) is applied. This allows us to make the interpretations concerning short period information in GNSS (Global Navigation Satellite System) coordinates series. Analyses using FFT and least squares (tidal) gave very coherent results and confirmed several millimetres diurnal and sub-diurnal oscillations. Wavelet analysis is aimed at the investigation of credibility of the precise GNSS solutions in terms of changes of the amplitude of oscillations in time. As a result of this study the changes in the amplitude of oscillations at diurnal and sub-diurnal frequency bands were obtained. These could be caused by the artificial modulations of the near-by frequencies, but also some geophysical signals could be clearly distinguished. Additionally the comparison of Continuous Wavelet Transforms of near stations (three pairs from ASG-EUPOS network) was performed. This comparison showed different behaviour of oscillations of residual coordinates, mainly due to the different thermal response or artefacts related to the
Analysis of Wide-Band Signals Using Wavelet Array Processing
Nisii, V.; Saccorotti, G.
2005-12-01
Wavelets transforms allow for precise time-frequency localization in the analysis of non-stationary signals. In wavelet analysis the trade-off between frequency bandwidth and time duration, also known as Heisenberg inequality, is by-passed using a fully scalable modulated window which solves the signal-cutting problem of Windowed Fourier Transform. We propose a new seismic array data processing procedure capable of displaying the localized spatial coherence of the signal in both the time- and frequency-domain, in turn deriving the propagation parameters of the most coherent signals crossing the array. The procedure consists in: a) Wavelet coherence analysis for each station pair of the instruments array, aimed at retrieving the frequency- and time-localisation of coherent signals. To this purpose, we use the normalised wavelet cross- power spectrum, smoothed along the time and scale domains. We calculate different coherence spectra adopting smoothing windows of increasing lengths; a final, robust estimate of the time-frequency localisation of spatially-coherent signals is eventually retrieved from the stack of the individual coherence distribution. This step allows for a quick and reliable signal discrimination: wave groups propagating across the network will manifest as high-coherence patches spanning the corresponding time-scale region. b) Once the signals have been localised in the time and frequency domain,their propagation parameters are estimated using a modified MUSIC (MUltiple SIgnal Characterization) algorithm. We select the MUSIC approach as it demonstrated superior performances in the case of low SNR signals, more plane waves contemporaneously impinging at the array and closely separated sources. The narrow-band Coherent Signal Subspace technique is applied to the complex Continuous Wavelet Transform of multichannel data for improving the singularity of the estimated cross-covariance matrix and the accuracy of the estimated signal eigenvectors. Using
A Wavelet Analysis Approach for Categorizing Air Traffic Behavior
Drew, Michael; Sheth, Kapil
2015-01-01
In this paper two frequency domain techniques are applied to air traffic analysis. The Continuous Wavelet Transform (CWT), like the Fourier Transform, is shown to identify changes in historical traffic patterns caused by Traffic Management Initiatives (TMIs) and weather with the added benefit of detecting when in time those changes take place. Next, with the expectation that it could detect anomalies in the network and indicate the extent to which they affect traffic flows, the Spectral Graph Wavelet Transform (SGWT) is applied to a center based graph model of air traffic. When applied to simulations based on historical flight plans, it identified the traffic flows between centers that have the greatest impact on either neighboring flows, or flows between centers many centers away. Like the CWT, however, it can be difficult to interpret SGWT results and relate them to simulations where major TMIs are implemented, and more research may be warranted in this area. These frequency analysis techniques can detect off-nominal air traffic behavior, but due to the nature of air traffic time series data, so far they prove difficult to apply in a way that provides significant insight or specific identification of traffic patterns.
Directory of Open Access Journals (Sweden)
MIHAIL PRICOP
2016-06-01
Full Text Available Vulnerable and critical mechanical systems are bearings and drive belts. Signal analysis of vibration highlights the changes in root mean square, the frequency spectrum (frequencies and amplitudes in the time- frequency (Short Time Fourier Transform and Wavelet Transform, are the most used method for faults diagnosis and location of rotating machinery. This article presents the results of an experimental study applied on a di agnostic platform of rotating machinery through three Wavelet methods: (Discrete Wavelet Transform -DWT, Continuous Wavelet Transform -CWT, Wavelet Packet Transform -WPT with different mother wavelet. Wavelet Transform is used to decompose the original sig nal into sub -frequency band signals in order to obtain multiple data series at different resolutions and to identify faults appearing in the complex rotation systems. This paper investigates the use of different mother wavelet functions for drive belts and bearing fault diagnosis. The results demonstrate the possibility of using different mother wavelets in rotary systems diagnosis detecting and locating in this way the faults in bearings and drive belts.
Time-frequency analysis of spike-wave discharges using a modified wavelet transform
Bosnyakova, D.Y.; Gabova, A.; Kuznetsova, G.D.; Obukhov, Y.; Midzyanovskaya, I.S.; Salonin, D.V.; Rijn, C.M. van; Coenen, A.M.L.; Tuomisto, L.; Luijtelaar, E.L.J.M. van
2006-01-01
The continuous Morlet wavelet transform was used for the analysis of the time-frequency pattern of spike-wave discharges (SWD) as can be recorded in a genetic animal model of absence epilepsy (rats of the WAG/Rij strain). We developed a new wavelet transform that allows to obtain the time-frequency
Hilbert-Huang transform and wavelet analysis of time history signal
Institute of Scientific and Technical Information of China (English)
石春香; 罗奇峰
2003-01-01
The brief theories of wavelet analysis and Hilbert-Huang transform (HHT) are introduced firstly in the present paper. Then several signal data were analyzed by using wavelet and HHT methods, respectively. The comparison shows that HHT is not only an effective method for analyzing non-stationary data, but also is a useful tool for examining detailed characters of time history signal.
Dating the age of admixture via wavelet transform analysis of genome-wide data
I. Pugach (Irina); R. Matveyev (Rostislav); A. Wollstein (Andreas); M.H. Kayser (Manfred); M. Stoneking (Mark)
2011-01-01
textabstractWe describe a PCA-based genome scan approach to analyze genome-wide admixture structure, and introduce wavelet transform analysis as a method for estimating the time of admixture. We test the wavelet transform method with simulations and apply it to genome-wide SNP data from eight admixe
Institute of Scientific and Technical Information of China (English)
SUN Ji-ping; MA Feng-ying; WU Dong-xu; LIU Xiao-yang
2008-01-01
Underground Electro Magnetic Interference (EMI) has become so serious that there were false alarms in monitoring system, which induced troubles of coal mine safety in production. In order to overcome difficulties caused by the explosion-proof enclosure of the equipments and the limitation of multiple startup and stop in transient process during EMI measurement, a novel technique was proposed to measure underground EMI distribution indirectly and enhance Electromagnetic Campatibility(EMC) of the monitoring system. The wavelet time-frequency analysis was introduced to underground monitoring system. Therefore, the sources, the startup time, duration and waveform of EMI could be ascertained correctly based on running records of underground electric equipments. The electrical fast transient/burst (EFT/B) was studied to verify the validity of wavelet analysis.EMI filter was improved in accordance of the EMI distribution gotten from wavelet analysis.Power port immunity was developed obviously. In addition, the method of setting wavelet thresholds was amended based upon conventional thresholds in the wavelet filter design.Therefore the EFT/B of data port was restrained markedly with the wavelet filtering. Coordinative effect of EMI power and wavelet filter makes false alarms of monitoring system reduce evidently. It is concluded that wavelet analysis and the improved EMI filter have enhanced the EMC of monitoring system obviously.
Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
ZHANG Shanshan; SU Rongguo; DUAN Yali; ZHANG Cui; SONG Zhijie; WANG Xiulin
2012-01-01
The discrete excitation-emission-matrix fluorescence spectra(EEMS)at 12 excitation wavelengths (400,430,450,460,470,490,500,510,525,550,570,and 590 nm)and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species.A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed.For laboratory simulatively mixed samples,the samples mixed from 43 algal species(the algae of one division accounted for 25％,50％,75％,85％,and 100％ of the gross biomass,respectively),the average discrimination rates at the level of division were 65.0％,87.5％,98.6％,99.0％,and 99.1％,with average relative contents of 18.9％,44.5％,68.9％,73.4％,and 82.9％,respectively;the samples mixed from 32 red tide algal species(the dominant species accounted for 60％,70％,80％,90％,and 100％ of the gross biomass,respectively),the average correct discrimination rates of the dominant species at the level of genus were 63.3％,74.2％,78.8％,83.4％,and 79.4％,respectively.For the 81 laboratory mixed samples with the dominant species accounting for 75％ of the gross biomass(chlorophyll),the discrimination rates of the dominant species were 95.1％ and 72.8％ at the level of division and genus,respectively.For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007,the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80％ of the gross biomass were discriminated at the genus level;for the 12 samples obtained from Jiaozhou Bay in August 2007,the dominant species of all the 12 samples were recognized at the division level.The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for
Psychoacoustic Music Analysis Based on the Discrete Wavelet Packet Transform
Directory of Open Access Journals (Sweden)
Xing He
2008-01-01
Full Text Available Psychoacoustical computational models are necessary for the perceptual processing of acoustic signals and have contributed significantly in the development of highly efficient audio analysis and coding. In this paper, we present an approach for the psychoacoustic analysis of musical signals based on the discrete wavelet packet transform. The proposed method mimics the multiresolution properties of the human ear closer than other techniques and it includes simultaneous and temporal auditory masking. Experimental results show that this method provides better masking capabilities and it reduces the signal-to-masking ratio substantially more than other approaches, without introducing audible distortion. This model can lead to greater audio compression by permitting further bit rate reduction and more secure watermarking by providing greater signal space for information hiding.
Analysis of wheezes using wavelet higher order spectral features.
Taplidou, Styliani A; Hadjileontiadis, Leontios J
2010-07-01
Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively
Chan, Y T
1995-01-01
Since the study of wavelets is a relatively new area, much of the research coming from mathematicians, most of the literature uses terminology, concepts and proofs that may, at times, be difficult and intimidating for the engineer. Wavelet Basics has therefore been written as an introductory book for scientists and engineers. The mathematical presentation has been kept simple, the concepts being presented in elaborate detail in a terminology that engineers will find familiar. Difficult ideas are illustrated with examples which will also aid in the development of an intuitive insight. Chapter 1 reviews the basics of signal transformation and discusses the concepts of duals and frames. Chapter 2 introduces the wavelet transform, contrasts it with the short-time Fourier transform and clarifies the names of the different types of wavelet transforms. Chapter 3 links multiresolution analysis, orthonormal wavelets and the design of digital filters. Chapter 4 gives a tour d'horizon of topics of current interest: wave...
Discrete directional wavelet bases and frames: analysis and applications
Dragotti, Pier Luigi; Velisavljevic, Vladan; Vetterli, Martin; Beferull-Lozano, Baltasar
2003-11-01
The application of the wavelet transform in image processing is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions. Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.
Rotor Faults Detection in Induction Motor by Wavelet Analysis
Directory of Open Access Journals (Sweden)
Neelam Mehala
2009-12-01
Full Text Available Motor current signature analysis has been successfully used for fault diagnosis in induction motors. However, this method does not always achieve good results when the speed or the load torque is not constant, because this cause variation on the motor slip and fast Fourier transform problems appear due to non-stationary signal. This paper experimentally describes the effects of rotor broken bar fault in the stator current of induction motor operating under non-constant load conditions. To achieve this, broken rotor bar fault is eplicated in a laboratory and its effect on the motor current has been studied. To diagnose the broken rotor bar fault, a new approach based on wavelet transform is applied by using ‘Labview 8.2 software’ of National Instrument (NI. The diagnosis procedure was performed by using the virtual instruments. The theoretical basis of proposed method is proved by laboratory tests.
Energy Technology Data Exchange (ETDEWEB)
Matsushima, J.; Rokugawa, S.; Kato, Y. [The University of Tokyo, Tokyo (Japan). Faculty of Engineering; Yokota, T.; Miyazaki, T. [Geological Survey of Japan, Tsukuba (Japan); Ichie, Y. [The University of Tokyo, Tokyo (Japan)
1996-10-01
Data processing techniques have been investigated for clarifying structures and physical properties of geothermal reservoirs in the deep underground by seismic exploration using multiple wells. They include the initial motion time-distance tomography, amplitude tomography, diffracted wave tomography, and structure imaging using reflected wave or scattered wave. When applying these data processing methods to observed records, weak and minor signals essentially required are canceled due to averaging the analytical fields. In this study, influence of inhomogeneous media on the wavefield was evaluated. Data were analyzed considering frequency by using wavelet transform by which time-frequency can be easily analyzed. From the time-frequency analysis using wavelet transform, it was illustrated that high frequency scattered waves, generated by scatterer like cracks or by irregularity on the reflection surface, arrive behind direct P-wave and direct S-wave. 5 refs., 8 figs.
Heart Rate Variability Analysis Using Threshold of Wavelet Package Coefficients
Kheder, G; Massoued, M Ben; Samet, M
2009-01-01
In this paper, a new efficient feature extraction method based on the adaptive threshold of wavelet package coefficients is presented. This paper especially deals with the assessment of autonomic nervous system using the background variation of the signal Heart Rate Variability HRV extracted from the wavelet package coefficients. The application of a wavelet package transform allows us to obtain a time-frequency representation of the signal, which provides better insight in the frequency distribution of the signal with time. A 6 level decomposition of HRV was achieved with db4 as mother wavelet, and the above two bands LF and HF were combined in 12 specialized frequencies sub-bands obtained in wavelet package transform. Features extracted from these coefficients can efficiently represent the characteristics of the original signal. ANOVA statistical test is used for the evaluation of proposed algorithm.
An Investigation into the Potential Application of Wavelets to Modal Testing and Analysis
Gwinn, A. Fort, Jr.
2002-01-01
The analysis of transient data of the type found in vibrating mechanical systems has been greatly improved through the use of modern techniques such as Fourier analysis. This is especially true when considered in conjunction with the development of the so-called Fast Fourier Transform algorithm by Cooley and the tremendous strides in computational power of the last several decades. The usefulness of the discrete Fourier Transform is its ability to transform sampled data from the "time-domain" to the "frequency domain," thereby allowing the analyst to decompose a signal into its frequency content. More recent developments have led to the wavelet transform. The strength of wavelet analysis is its ability to maintain both time and frequency information, thus making it an attractive candidate for the analysis of non-stationary signals. This report is an overview of wavelet theory and the potential use of the wavelet transform as an alternative to Fourier analysis in modal identification.
Tide Forecasting of Tides Around Taiwan by Artificial Neural Network Method and Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In multiresolution analysis (MRA) by wavelet function Daubechies (db), we decompose the signal to two parts, the low and high frequency content. The high-frequency content of the data is removed first and a new "de-noise" signal is reconstructed by using inverse wavelet transform. The wavelet spectrum and harmonic analysis were used to analyze the characteristics of tidal data before constructing the input and output structure of ANN model. That is, the concept of tidal constituent phase-lags was introduced and the new "de-noise" signal was used as the input data set of ANN and the forecasting accuracy of ANN model is significantly improved.
Michel, V.
2005-12-01
A spherical wavelet analysis of monthly GRACE gravity data is presented. We observe strong correlations to gravity variations predicted by some common hydrology models, in particular in the Amazon, Zambezi and Ganges area. A time series analysis of the predicted gravity due to surface density changes in comparison to spherical wavelet coefficients of the GRACE potential demonstrates the advantages of spherical wavelets. Whereas a spherical harmonics expansion always implicitly includes a global averaging process, wavelets represent localizing basis functions that are much better able to analyze regional variations of a considered data set. Moreover, it is demonstrated that the spherical wavelet approach due to W. Freeden and U. Windheuser can be extended to a larger set of problems including the modelling of functions on balls, i.e. not only on the spherical surface. Examples of applications, such as the volume density recovery from simulated SGG gravity data (cf. planned satellite mission GOCE) are demonstrated. References: M.J. Fengler, W. Freeden, A. Kohlhaas, V. Michel, T. Peters: Wavelet Modelling of Regional and Temporal Variations of the Earth's Gravitational Potential Observed by GRACE, Schriften zur Funktionalanalysis und Geomathematik, 21 (2005), preprint, article submitted to Journal of Geodesy, 2005. V. Michel: Regularized Wavelet--based Multiresolution Recovery of the Harmonic Mass Density Distribution from Data of the Earth's Gravitational Field at Satellite Height, Inverse Problems, 21 (2005), 997-1025.
Investigating the Link Between Climate and Leptospirosis in the Caribbean Using Wavelet Analysis.
Batchelor, T. W.; Amarakoon, D.; Taylor, M. A.; Stephenson, T.
2009-05-01
The Caribbean has shown changes in its climate (temperature and rainfall) as a result of urbanisation, population growth and industrialisation. The climatic changes have implications for the emergence and re- emergence of rodent-borne diseases such as leptospirosis. In this paper wavelet analysis is used to investigate the relationship between the incidence of leptospirosis in the Caribbean and climate variables such as temperature and precipitation. Wavelet analysis takes into account characteristics unique to climate and epidemiological data which other spectral techniques failed to do. The analysis reveals 2-3 year periodic signals in both the wavelet power spectrum and wavelet coherency. There is also a correlation between incidence of leptospirosis and late season Caribbean rainfall.
The Research on Wavelet Audio Watermark Based on Independent Component Analysis
Energy Technology Data Exchange (ETDEWEB)
Ma, X F [Engineering Training Centre, Harbin Engineering University, Harbin, 150001 (China); Jiang, T [Info. and Comm. Engineering College, Harbin Engineering University, Harbin, 150001 (China)
2006-10-15
Along with the development of the watermark technique many new scheme were presented and most of them were proved efficient. Some researchers have presented extraction of audio watermark using ICA in spatial domain. In this paper, we present a wavelet audio watermark using ICA. We embedded the image watermark into the wavelet coefficient of the audio signal, and extracted the watermark image using ICA in wavelet domain. We added noise on the watermark audio for analysis and the simulation results show that this watermark scheme we present is efficient and robustness.
Application of wavelet transforms as a time-series analysis tool for nuclear thermalhydraulics
Energy Technology Data Exchange (ETDEWEB)
Pohl, D.J.; Pascoe, J.; Popescu, A.I., E-mail: daniel.pohl@amec.com, E-mail: jason.pascoe@amec.com, E-mail: adrian.popescu@amec.com [AMEC NSS Limited, Toronto, Ontario (Canada)
2011-07-01
Wavelet transforms can be a valuable time-series analysis tool in the field of nuclear thermalhydraulics. As an example, the Morlet wavelet transform can be used to reduce the aleatory (random) uncertainty of a voiding transient in a large loss of coolant accident (LOCA). The wavelet transform is used to determine the cutoff frequency for a low pass Butterworth filter in order to remove the noisy part of the signal without infringing upon the characteristic frequencies of the phenomenon. This technique successfully reduced the standard random uncertainty by 42.4%. (author)
monthly energy consumption forecasting using wavelet analysis and ...
African Journals Online (AJOL)
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paper, a wavelet transform and radial basis function neural network based energy forecast model is developed to .... These prop- erties lead to quicker learning in comparison to ..... Machine Learning and Cybernetics,. IEEE Transactions, 8: ...
Multi-resolution analysis for ear recognition using wavelet features
Shoaib, M.; Basit, A.; Faye, I.
2016-11-01
Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.
Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram
Energy Technology Data Exchange (ETDEWEB)
Anant, K.S.
1997-06-01
In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the P as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the
Institute of Scientific and Technical Information of China (English)
Zhao Bin; Quan Taifan; Wang Jinrong
2005-01-01
The imaging and target detection methods for stepped frequency signal based on the wavelet transform and its power spectrum are investigated. Not only an imaging and target detection algorithm for stepped frequency signal based on the wavelet transform, but also its respective power spectrum are proposed. The multisampling property of stepped frequency signal is studied and wavelet transform is well suited for analyzing the signal. After multisampling property of stepped frequency signal being studied, it is shown that the wavelet transform is appropriate to analyze the signal. Based on the theory, the wavelet power spectrum analysis is applied to obtain the target range profile and the binary wavelet transform is used to perform target detection. To determine a suitable wavelet scaling for imaging of range profile' s MMW radar, the distance resolution △R technique is proposed. The effectiveness of this new method is evaluated using simulated noisy radar signal. Results show that the proposed method can bring out the exactness and low computational complexity of this method.
Wavelet transform analysis of skin perfusion during thermal stimulation.
Bagno, Andrea; Martini, Romeo
2016-11-25
This work elucidates the mechanisms of skin microcirculation response to local heating at 44°C in vasculopathic patients. Laser Doppler and tcpO2 were simultaneously acquired. Patients were selected on the basis of tcpO2: Group A 50 mmHg. The wavelet analysis of signal oscillations displays six frequency intervals. Each interval is assigned to a specific cardiovascular activity. The contributions of cardiac, myogenic and neurogenic activities were selectively detected. Thermal stimulation increased relative amplitude in all patients: heart activity by +103.26% in A, +162.84% in B, +454.54% in C; myogenic activity by +52.45% in A, +38.51% in B, +156.19% in C; neurogenic activity +43.36% in A, +74.15% in B, +242.42% in C. Thermal stimulation increased relative power in all patients: heart activity by +365.30% in A, +473.72% in B, +1393.77% in C; myogenic activity by +106.92% in A, +66.03% in B, +380.18% in C; neurogenic activity by +77.00% in A, +162.65% in B, +771.93% in C.This work demonstrates that the spectral analysis allows extracting from Laser Doppler signals more information than that can be gained by solely investigating perfusion values over time.
A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients
Directory of Open Access Journals (Sweden)
Mariel Rosenblatt
2014-11-01
Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.
APPLICATION OF WAVELET TIME-FR EQUENCY ANALYSIS TO IDENTIFICATION OF CRACKED ROTOR
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
Based on the simple hinge crack model and the local flexibility theorem, the corresponding dynamic equation of the cracked rotor is modelled, the numerical simulation solutions of the cracked rotor and the uncracked rotor are obtained. By the continuous wavelet time-frequency transform, the wavelet time-frequency properties of the uncracked rotor and the cracked rotor are discussed. A new detection algorithm that uses the wavelet time-frequency transform to identify the crack is proposed. The influence of the sampling frequency on the wavelet time-frequency transform is analyzed by the numerical simulation research. The valid sampling frequency is suggested. Experiments demonstrate the validity and availability of the proposed algorithm in identification of the cracked rotor for engineering practices.
Analysis of Solar Magnetic Activity with the Wavelet Coherence Method
Velasco, V. M.; Perez-Peraza, J. A.; Mendoza, B. E.; Valdes-Galicia, J. F.; Sosa, O.; Alvarez-Madrigal, M.
2007-05-01
The origin, behavior and evolution of the solar magnetic field is one of the main challenges of observational and theoretical solar physics. Up to now the Dynamo theory gives us the best approach to the problem. However, it is not yet able to predict many features of the solar activity, which seems not to be strictly a periodical phenomenon. Among the indicators of solar magnetic variability there is the 11-years cycle of sunspots, as well as the solar magnetic cycle of 22 years (the Hale cycle). In order to provide more elements to the Dynamo theory that could help it in the predicting task, we analyze here the plausible existence of other periodicities associated with the solar magnetic field. In this preliminary work we use historical data (sunspots and aurora borealis), proxies (Be10 and C14) and modern instrumental data (Coronal Holes, Cosmic Rays, sunspots, flare indexes and solar radio flux at 10.7 cm). To find relationships between different time-frequency series we have employed the t Wavelet Coherence technique: this technique indicates if two time-series of solar activity have the same periodicities in a given time interval. If so, it determines whether such relation is a linear one or not. Such a powerful tool indicates that, if some periodicity at a given frequency has a confidence level below 95%, it appears very lessened or does not appear in the Wavelet Spectral Analysis, such periodicity does not exist . Our results show that the so called Glaisberg cycle of 80-90 years and the periodicity of 205 years (the Suess cycle) do not exist . It can be speculated that such fictitious periodicities hav been the result of using the Fourier transform with series with are not of stationary nature, as it is the case of the Be10 and C14 series. In contrast we confirm the presence of periodicities of 1.3, 1.7, 3.5, 5.5, 7, 60, 120 and 240 years. The concept of a Glaisberg cycle falls between those of 60 and 120 years. We conclude that the periodicity of 120 years
Results of wavelet processing of the 2K-capture Kr-78 experiment statistics
Gavrilyuk, Yu M; Kazalov, V V; Kuzminov, V V; Panasenko, S I; Ratkevich, S S
2010-01-01
Results of a search for Kr-78 double K-capture with the large low-background proportional counter (2005-2008 years) at the Baksan Neutrino Observatory are presented. An experimental method and characteristics of detectors are described. Basic features of the digitized pulses processing using wavelet transform are considered. With due account taken of the analysis of individual noise characteristic it has been shown that the appropriate choice of both wavelet characteristics and sequence of processing algorithms allows one to decrease the background in the energy region of useful events with a unique set of characteristics by ~2000 times. New limit on the half-life of Kr-78 with regard to 2K-capture has been found: T_{1/2} >= 2.4E21 yrs (90% C.L.).
WAVELET TRANSFORM ANALYSIS OF ELECTROMYOGRAPHY KUNG FU STRIKES DATA
Directory of Open Access Journals (Sweden)
Ana Carolina de Miranda Marzullo
2009-11-01
Full Text Available In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP values instead of root mean square (rms values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF. EMG data of the deltoid anterior (DA, triceps brachii (TB and brachioradialis (BR muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023 for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007 for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners
Problems in wavelet analysis of hydrologic series and some suggestions on improvement
Institute of Scientific and Technical Information of China (English)
WANG Hongrui; YE Letian; LIU Changming; YANG Chi; LIU Peng
2007-01-01
Applying the wavelet theory and methods to investigate the hydrologic processes such as precipitation and runoff is a hot field. However, several aspects in research are usually ignored: the effect of admissible condition of wavelet functions and the disturbance of noises for the detection of periods, the effect of the length of a hydrologic time-series on the final result, and the choice between the anomaly and the original time series for wavelet analysis. In this paper, these issues are fully discussed. Precipitation data from Lanzhou Precipitation Station are taken for case study. The result indicates that in the wavelet analysis of hydrologic series, denoise methods should be used to eliminate the influence of noises. The MexHat wavelet function satisfies the admissible condition, which ensures that the periodic properties of hydrologic processes can be well represented by using the MexHat wavelet for decomposition. The affected range of hydrologic series which should be discarded before analysis is given. It is also suggested that the anomaly series should be used to highlight the actual undulation of the hydrologic series.
Luo, Xiaodong; Jakobsen, Morten; Nævdal, Geir
2016-01-01
In this work we propose an ensemble 4D seismic history matching framework for reservoir characterization. Compared to similar existing frameworks in reservoir engineering community, the proposed one consists of some relatively new ingredients, in terms of the type of seismic data in choice, wavelet multiresolution analysis for the chosen seismic data and related data noise estimation, and the use of recently developed iterative ensemble history matching algorithms. Typical seismic data used for history matching, such as acoustic impedance, are inverted quantities, whereas extra uncertainties may arise during the inversion processes. In the proposed framework we avoid such intermediate inversion processes. In addition, we also adopt wavelet-based sparse representation to reduce data size. Concretely, we use intercept and gradient attributes derived from amplitude versus angle (AVA) data, apply multilevel discrete wavelet transforms (DWT) to attribute data, and estimate noise level of resulting wavelet coeffici...
A multiresolution analysis for tensor-product splines using weighted spline wavelets
Kapl, Mario; Jüttler, Bert
2009-09-01
We construct biorthogonal spline wavelets for periodic splines which extend the notion of "lazy" wavelets for linear functions (where the wavelets are simply a subset of the scaling functions) to splines of higher degree. We then use the lifting scheme in order to improve the approximation properties with respect to a norm induced by a weighted inner product with a piecewise constant weight function. Using the lifted wavelets we define a multiresolution analysis of tensor-product spline functions and apply it to image compression of black-and-white images. By performing-as a model problem-image compression with black-and-white images, we demonstrate that the use of a weight function allows to adapt the norm to the specific problem.
ECG Analysis based on Wavelet Transform and Modulus Maxima
Directory of Open Access Journals (Sweden)
Mourad Talbi
2012-01-01
Full Text Available In this paper, we have developed a new technique of P, Q, R, S and T Peaks detection using Wavelet Transform (WT and Modulus maxima. One of the commonest problems in electrocardiogram (ECG signal processing, is baseline wander removal suppression. Therefore we have removed the baseline wander in order to make easier the detection of the peaks P and T. Those peaks are detected after the QRS detection. The proposed method is based on the application of the discritized continuous wavelet transform (Mycwt used for the Bionic wavelet transform, to the ECG signal in order to detect R-peaks in the first stage and in the second stage, the Q and S peaks are detected using the R-peaks localization. Finally the Modulus maxima are used in the undecimated wavelet transform (UDWT domain in order to detect the others peaks (P, T. This detection is performed by using a varying-length window that is moving along the whole signal. For evaluating the proposed method, we have compared it to others techniques based on wavelets. In this evaluation, we have used many ECG signals taken from MIT-BIH database. The obtained results show that the proposed method outperforms a number of conventional techniques used for our evaluation.
Chowdhury, Suman Kanti; Nimbarte, Ashish D; Jaridi, Majid; Creese, Robert C
2013-10-01
Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue under dynamic repetitive conditions. Ten human participants performed 40min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12-23Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the "Rbio3.1" wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets.
Wavelet and Spectral Analysis of Some Selected Problems in Reactor Diagnostics
Energy Technology Data Exchange (ETDEWEB)
Sunde, Carl
2004-12-01
Both spectral and wavelet analysis were successfully used in various diagnostic problems involving non-stationary core processes in nuclear power reactors. Three different problems were treated: two-phase flow identification, detector tube impacting and core-barrel vibrations. The first two problems are of non-stationary nature, whereas the last one is not. In the first problem, neutron radiographic and visible light images of four different vertical two-phase flow regimes, bubbly, slug, chum and annular flow, were analysed and classified with a neuro-wavelet algorithm. The algorithm consists of a wavelet part, using the 2-D discrete wavelet transform and of an artificial neural network. It classifies the different flow regimes with up to 99% efficiency. Detector tubes in a Boiling Water Reactor may execute vibrations and may also impact on nearby fuel-assemblies. Signals from in-core neutron detectors in Ringhals-1 were analysed, for detection of impacting, with both a classical spectral method and wavelet-based methods. The wavelet methods include both the discrete and the continuous 1-D wavelet transform. It was found that there is agreement between the different methods as well as with visual inspections made during the outage at the plant. However, the wavelet technique has the advantage that it does not require expert judgement for the interpretation of the analysis. In the last part two analytical calculations of the neutron noise, induced by shell-mode core-barrel vibrations, were carried out. The results are in good agreement with calculations from a numerical simulator. An out-of-phase behaviour between in-core and ex-core positions was found, which is in agreement with earlier measurements from the Pressurised Water Reactor Ringhals-3. The results from these calculations are planned to be used when diagnosing the shell-mode core-barrel vibrations in an operating plant.
Phase characteristic analysis of continuous depth air-gun source wavelet
Xing, Lei; Liu, Huaishan; Zheng, Xilai; Liu, Xueqin; Zhang, Jin; Wang, Linfei; Zou, Zhihui; Xu, Yiming
2016-10-01
Air guns are important sources for marine seismic exploration. Far-field wavelet of air gun arrays, as a necessary parameter for pre-stack processing and source models, plays an important role during marine seismic data processing and interpretation. When an air gun fires, it generates a series of air bubbles. Similar to onshore seismic exploration, the water forms a plastic fluid near the bubble; the farther the air gun is located from the measurement, the more steady and more accurately represented the wavelet will be. In practice, hydrophones should be placed more than 100 m from the air gun; however, traditional seismic cables cannot meet this requirement. On the other hand, vertical cables provide a viable solution to this problem. This study uses a vertical cable to receive wavelets from 38 air guns and data are collected offshore Southeast Qiong, where the water depth is over 1000 m. In this study, the wavelets measured using this technique coincide very well with the simulated wavelets and can therefore represent the real shape of the wavelets. This experiment fills a technology gap in China.
Efficient hemodynamic event detection utilizing relational databases and wavelet analysis
Saeed, M.; Mark, R. G.
2001-01-01
Development of a temporal query framework for time-oriented medical databases has hitherto been a challenging problem. We describe a novel method for the detection of hemodynamic events in multiparameter trends utilizing wavelet coefficients in a MySQL relational database. Storage of the wavelet coefficients allowed for a compact representation of the trends, and provided robust descriptors for the dynamics of the parameter time series. A data model was developed to allow for simplified queries along several dimensions and time scales. Of particular importance, the data model and wavelet framework allowed for queries to be processed with minimal table-join operations. A web-based search engine was developed to allow for user-defined queries. Typical queries required between 0.01 and 0.02 seconds, with at least two orders of magnitude improvement in speed over conventional queries. This powerful and innovative structure will facilitate research on large-scale time-oriented medical databases.
Galiana-Merino, J. J.; Rosa-Herranz, J. L.; Rosa-Cintas, S.; Martinez-Espla, J. J.
2013-01-01
A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of multichannel seismic data. The considered time-frequency transforms include the continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform. The developed approaches provide a fast and precise time-frequency examination of the seismograms at different frequency bands. Moreover, filtering methods for noise, transients or even baseline removal, are implemented. The primary motivation is to support seismologists with a user-friendly and fast program for the wavelet analysis, providing practical and understandable results. Program summaryProgram title: SeismicWaveTool Catalogue identifier: AENG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AENG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 611072 No. of bytes in distributed program, including test data, etc.: 14688355 Distribution format: tar.gz Programming language: MATLAB (MathWorks Inc.) version 7.8.0.347 (R2009a) or higher. Wavelet Toolbox is required. Computer: Developed on a MacBook Pro. Tested on Mac and PC. No computer-specific optimization was performed. Operating system: Any supporting MATLAB (MathWorks Inc.) v7.8.0.347 (R2009a) or higher. Tested on Mac OS X 10.6.8, Windows XP and Vista. Classification: 13. Nature of problem: Numerous research works have developed a great number of free or commercial wavelet based software, which provide specific solutions for the analysis of seismic data. On the other hand, standard toolboxes, packages or libraries, such as the MathWorks' Wavelet Toolbox for MATLAB, offer command line functions and interfaces for the wavelet analysis of one-component signals. Thus, software usually is focused on very specific problems
Improved wavelet analysis for induction motors mixed-fault diagnosis
Institute of Scientific and Technical Information of China (English)
ZHANG Hanlei; ZHOU Jiemin; LI Gang
2007-01-01
Eccentricity is one of the frequent faults of induction motors,and it may cause rub between the rotor and the stator.Early detection of significant rub from pure eccentricity can prolong the lifespan of induction motors.This paper is devoted to such mixed-fault diagnosis:eccentricity plus rub fault.The continuous wavelet transform(CWT)is employed to analyze vibration signals obtained from the motor body.An improved continuous wavelet trartsform was proposed to alleviate the frequency aliasing.Experimental results show that the proposed method can effectively distinguish two types of faults,single-fault of eccentricity and mixed-fault of eccentricity plus rub.
Analysis of Linear Time-varying Systems via Haar Wavelet
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
In this paper Haar wavelet integral operational matrices are introduced and the n applied to analyse linear time-varying systems. The method converts the origi nal problem to solving linear algebraic equations. Hence, computational difficulties are considerably reduced. Based on the property of time-frequency localization of Haar wavelet bases, the solution of a system includes both the frequency information and the time information. Other orthogonal functions do not have this property. An example is given, and the results are shown to be ver y accurate.
Performance Analysis of Texture Image Classification Using Wavelet Feature
Directory of Open Access Journals (Sweden)
Dolly Choudhary
2013-01-01
Full Text Available This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.
Spatial wavelet analysis of calcium oscillations in developing neurons.
Directory of Open Access Journals (Sweden)
Federico Alessandro Ruffinatti
Full Text Available Calcium signals play a major role in the control of all key stages of neuronal development, and in particular in the growth and orientation of neuritic processes. These signals are characterized by high spatial compartmentalization, a property which has a strong relevance in the different roles of specific neuronal regions in information coding. In this context it is therefore important to understand the structural and functional basis of this spatial compartmentalization, and in particular whether the behavior at each compartment is merely a consequence of its specific geometry or the result of the spatial segregation of specific calcium influx/efflux mechanisms. Here we have developed a novel approach to separate geometrical from functional differences, regardless on the assumptions on the actual mechanisms involved in the generation of calcium signals. First, spatial indices are derived with a wavelet-theoretic approach which define a measure of the oscillations of cytosolic calcium concentration in specific regions of interests (ROIs along a cell, in our case developing chick ciliary ganglion neurons. The resulting spatial profile demonstrates clearly that different ROIs along the neuron are characterized by specific patterns of calcium oscillations. Next we have investigated whether this inhomogeneity is due just to geometrical factors, namely the surface to volume ratio in the different subcompartments (e.g. soma vs. growth cone or it depends on their specific biophysical properties. To this aim correlation functions are computed between the activity indices and the surface/volume ratio along the cell: the data thus obtained are validated by a statistical analysis on a dataset of [Formula: see text] different cells. This analysis shows that whereas in the soma calcium dynamics is highly correlated to the surface/volume ratio, correlations drop in the growth cone-neurite region, suggesting that in this latter case the key factor is the
Energy Technology Data Exchange (ETDEWEB)
Magazù, S.; Migliardo, F. [Dipartimento di Fisica e di Scienze della Terra dell’, Università degli Studi di Messina, Viale F. S. D’Alcontres 31, 98166 Messina (Italy); Vertessy, B.G. [Institute of Enzymology, Hungarian Academy of Science, Budapest (Hungary); Caccamo, M.T., E-mail: maccamo@unime.it [Dipartimento di Fisica e di Scienze della Terra dell’, Università degli Studi di Messina, Viale F. S. D’Alcontres 31, 98166 Messina (Italy)
2013-10-16
Highlights: • Innovative multiresolution wavelet analysis of elastic incoherent neutron scattering. • Elastic Incoherent Neutron Scattering measurements on homologues disaccharides. • EINS wavevector analysis. • EINS temperature analysis. - Abstract: In the present paper the results of a wavevector and thermal analysis of Elastic Incoherent Neutron Scattering (EINS) data collected on water mixtures of three homologous disaccharides through a wavelet approach are reported. The wavelet analysis allows to compare both the spatial properties of the three systems in the wavevector range of Q = 0.27 Å{sup −1} ÷ 4.27 Å{sup −1}. It emerges that, differently from previous analyses, for trehalose the scalograms are constantly lower and sharper in respect to maltose and sucrose, giving rise to a global spectral density along the wavevector range markedly less extended. As far as the thermal analysis is concerned, the global scattered intensity profiles suggest a higher thermal restrain of trehalose in respect to the other two homologous disaccharides.
Roll Eccentricity Compensation Based on Anti-Alias-sing Wavelet Analysis Method
Institute of Scientific and Technical Information of China (English)
CHEN Zhi-ming; LUO Fei; XU Yu-ge; YU Wei
2009-01-01
Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi-resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency-aliasing problem owing to the less than ideal cut-off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi-resolution wavelet analyzer was designed. Experimental results showed that the anti-aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was im-proved from ±40 μm to ±15 μm.
Directory of Open Access Journals (Sweden)
Kohei Arai
2011-09-01
Full Text Available A method for embedded object detection with radar echo data by means of wavelet analysis of MRA: Multi-Resolution Analysis, in particular, three dimensional wavelet transformations is proposed. In order to improve embedded object detecting capability, not only one dimensional radar echo data but also three dimensional data are used. Through a comparison between one dimensional edge detection with Sobel operator and three dimensional wavelet transformation based edge detection, it is found that the proposed method is superior to the Sobel operator based method.
Wavelet transform analysis of electromyography kung fu strikes data.
Neto, Osmar Pinto; Marzullo, Ana Carolina de Miranda
2009-11-01
In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG) analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP) values instead of root mean square (rms) values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF). EMG data of the deltoid anterior (DA), triceps brachii (TB) and brachioradialis (BR) muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023) for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007) for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners. Key PointsThe results show higher muscle activity and lower electromyography median frequencies for strikes with impact compared to strikes without.SSP results presented higher sensitivity and lower inter-subject coefficient of variations than rms results.Kung Fu palm strikes with impact may present better motor units' synchronization than strikes without.
Bedform evolution in a tidal inlet referred from wavelet analysis
DEFF Research Database (Denmark)
Fraccascia, Serena; Winter, Christian; Ernstsen, Verner Brandbyge;
2011-01-01
inlet and evaluate how they changed over consecutive years, when morphology was modified and bedforms migrated. High resolution bathymetric data from the Grådyb tidal inlet channel (Danish Wadden Sea) from seven years from 2002 to 2009 (not in 2004) were analyzed. Continuous wavelet transform of bed...
Kang, Shanlin; Kang, Yuzhe; Chen, Jingwei
2008-10-01
A novel approach combining wavelet transform with neural network is proposed for vibration fault diagnosis of turbo-generator set in power system. The multi-resolution analysis technology is used to acquire the feature vectors which are applied to train and test the neural network. Feature extraction involves preliminary processing of measurements to obtain suitable parameters which reveal weather an interesting pattern is emerging. The feature extraction technique is needed for preliminary processing of recorded time-series vibrations over a long period of time to obtain suitable parameters. The neural network parameters are determined by means of the recursive orthogonal least squares algorithm. In network training procedure, much simulation and practical samples are utilized to verify and test the network performance. And according to the output result, the fault pattern can be recognized. The actual applications show that the method is effective for detection and diagnosis of rotating machine fault and the experiment result is correct.
Application of fractal-wavelet analysis for separation of geochemical anomalies
Afzal, Peyman; Ahmadi, Kamyar; Rahbar, Kambiz
2017-04-01
The purpose of this paper is separation and detection of different geochemical populations and anomalies from background utilizing fractal-wavelet analysis. Daubechies2 and Morlet wavelets were used for transformation of the Cu estimated data to spatial frequency based on lithogeochemical data in Bardaskan area (SE Iran) by a MATLAB code. Wavelet is a significant tool for transformation of exploratory data because the noise data are removed from results and also, accuracy for determination of thresholds can be higher than other conventional methods. The Cu threshold values for extremely, highly and moderately anomalies are 1.4%, 0.66% and 0.4%, respectively, according to the fractal-wavelet analysis based on the Daubichies2 transformation. Moreover, the fractal-wavelet analysis by the Morlet wavelet shows that the Cu threshold values are 2%, 0.75% and 0.46% for extremely, highly and moderately anomalies and populations, respectively. The results obtained by the both WT methods indicate that the main Cu enriched anomalies and populations were situated in the central parts of the Bardaskan district which are associated with surface mineralization and ancient mining digs. Furthermore, results derived via the Morlet WT is better than Daubichies2 WT according to the correlation with geological characteristics by logratio matrix. The results obtained by the fractal-wavelet method have a good correlation with geological particulars including alteration zones and surface Cu mineralization which reveals the proposed technique is an applicable approach for identification of various geochemical anomalies and zones from background. However, the main targets for detailed exploration is located in the central part of the studied area.
Data-Adaptive Wavelets and Multi-Scale Singular Spectrum Analysis
Yiou, P; Ghil, M
1998-01-01
Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series of length $N$ whose intermittency can give rise to the divergence of their variance. SSA relies on the construction of the lag-covariance matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M Dt, with Dt the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M 3/4 W 3/4 N. Multi-scale SSA varies W, while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lag-covariance matrix C_M as a data-adaptive wavelets; successive eigenvectors of C_M correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency do...
Melodic pattern discovery by structural analysis via wavelets and clustering techniques
DEFF Research Database (Denmark)
Velarde, Gissel; Meredith, David
We present an automatic method to support melodic pattern discovery by structural analysis of symbolic representations by means of wavelet analysis and clustering techniques. In previous work, we used the method to recognize the parent works of melodic segments, or to classify tunes into tune...... to support human or computer assisted music analysis and teaching....
Directory of Open Access Journals (Sweden)
Carsten Proppe
2012-01-01
Full Text Available Multiresolution analysis for problems involving random parameter fields is considered. The random field is discretized by a Karhunen-Loève expansion. The eigenfunctions involved in this representation are computed by a wavelet expansion. The wavelet expansion allows to control the spatial resolution of the problem. Fine and coarse scales are defined, and the fine scales are taken into account by projection operators. The influence of the truncation level for the wavelet expansion on the computed reliability is documented.
Directory of Open Access Journals (Sweden)
Jaafar Khalaf Ali, Qusai Talib Abdulwahab, Sajjad Nayyef Abdul kareem
2016-01-01
Full Text Available Vibration monitoring and analysis techniques are the key features of successful predictive and proactive maintenance programs. In this work, advanced vibration analysis techniques like Wavelet transform, Principle Component Analysis (PCA and Squared Prediction Error (SPE have been used to detect the faults in bearing. Discrete Wavelet Transforms (DWT decomposes signal to high and low frequencies. PCA is employed to extract important feature and reduce dimension. SPE is used to detect the bearing faults. The experimental data is collected from SpectraQuest's Machine Fault Simulator (MFS-4 apparatus. In this study, four rollers were bearing defects (ball defect, outer race defect, inner race defect and combined defect for 1" and 3/4" bearing. From the results, the suggestion techniques can be used to detect multi-faults in the bearings. The results show that the best wavelet function is Coiflets4 in this method.
Institute of Scientific and Technical Information of China (English)
CHENMing; TANGTiantong; ZHANGXiaolin
2003-01-01
In this paper, an effective numerical method based on wavelet moment method is presented to enhance the analysis of interdigital transducer (IDT)for the excitation of surface acoustic waves (SAW) on the piezoelectric substrate of acoustic-optical devices. This problem is formulated in terms of an integral equa-tion, and its electric charge matrix equations obtained by the method of moment (MoM) are effectively solved by Daubechies discrete wavelet transform. One of the mosts triking advantage of this method is that it can greatly ac-celerate the computing with the help of conjugate gradient methods because the wavelet transform make the moment matrices sparse. As a result of the use of this method, the transducer input power coupling factors to both surface and bulk waves are computed. Analysis results show this method is a powerful numerical technique in analysis of IDT for acousto-optical devices.
Description of shape characteristics through Fourier and wavelet analysis
Directory of Open Access Journals (Sweden)
Yuan Zhanwei
2014-02-01
Full Text Available In this paper, Fourier and Wavelet transformation were adopted to analyze shape characteristics, with twelve simple shapes and two types of second phases from real microstructure morphology. According to the results of Fast Fourier transformation (FFT, the Fourier descriptors can be used to characterize the shape from the aspects of the first eight Normalization amplitudes, the number of the largest amplitudes to inverse reconstruction, similarity of shapes and profile roughness. And the Diepenbroek Roughness was rewritten by Normalization amplitudes of FFT results. Moreover, Sum Square of Relative Errors (SSRE of Wavelet transformation (WT signal sequence, including approximation signals and detail signals, was introduced to evaluate the similarity and relative orientation among shapes. As a complement to FFT results, the WT results can retain more detailed information of shapes including their orientations. Besides, the geometric signatures of the second phases were extracted by image processing and then were analyzed by means of FFT and WT.
Doubly Fed Induction Generator Analysis Through Wavelet Technique
Directory of Open Access Journals (Sweden)
K.Ram Mohan Rao
2009-01-01
Full Text Available Because of the intermittent nature of wind, its integration to the power system is still promising with respect to power qualityand stability. For the large penetration of wind energy, this paper using an embedded time-frequency localization features inwavelet, provides deep insight to the character of transient signals for a proposed test system comprising one thermal plantand three DFIG-based wind plants. The test system is first simulated and the results are mapped onto the wavelet formatfor accurate detection & better resolution of the characters of transients. This is found that the presence of lower frequencybandwidth signals accompanies relatively more energy and larger magnitude wavelet coefficients are the root cause for thestability and quality
Description of shape characteristics through Fourier and wavelet analysis
Institute of Scientific and Technical Information of China (English)
Yuan Zhanwei; Li Fuguo; Zhang Peng; Chen Bo
2014-01-01
In this paper, Fourier and Wavelet transformation were adopted to analyze shape char-acteristics, with twelve simple shapes and two types of second phases from real microstructure mor-phology. According to the results of Fast Fourier transformation (FFT), the Fourier descriptors can be used to characterize the shape from the aspects of the first eight Normalization amplitudes, the number of the largest amplitudes to inverse reconstruction, similarity of shapes and profile roughness. And the Diepenbroek Roughness was rewritten by Normalization amplitudes of FFT results. Moreover, Sum Square of Relative Errors (SSRE) of Wavelet transformation (WT) signal sequence, including approximation signals and detail signals, was introduced to evaluate the simi-larity and relative orientation among shapes. As a complement to FFT results, the WT results can retain more detailed information of shapes including their orientations. Besides, the geometric sig-natures of the second phases were extracted by image processing and then were analyzed by means of FFT and WT.
Singh, Ram Chandra; Bhatla, Rajeev
2012-07-01
This paper deals with the meteorological applications of wavelets and fuzzy logics and a hybrid of wavelets and fuzzy logics. The wavelet transform has emerged over recent years as a powerful time-frequency analysis and signal coding tool favoured for the interrogation of complex non-stationary signals. It has been shown that the wavelet transform is a flexible time-frequency decomposition tool which can form the basis of useful time series analysis. It is expected to see an increased amount of research and technology development work in the coming years employing wavelets for various scientific and engineering applications.
Response of Autonomic Nervous System to Body Positions: Fourier and Wavelet Analysis
Xu, A; Federici, A; Stramaglia, S; Simone, F; Zenzola, A; Santostasi, R; Xu, Aiguo
2003-01-01
Two mathematical methods, the Fourier and wavelet transforms, were used to study the short term cardiovascular control system. Time series, picked from electrocardiogram and arterial blood pressure lasting 6 minutes, were analyzed in supine position (SUP), during the first (HD1), and the second half (HD2) of $90^{\\circ}$ head down tilt and during recovery (REC). The wavelet transform was performed using the Haar function of period $T=2^j$ ($% j=1$,2,$... $,6) to obtain wavelet coefficients. Power spectra components were analyzed within three bands, VLF (0.003-0.04), LF (0.04-0.15) and HF (0.15-0.4) with the frquency unit cycle/interval. Wavelet transform demonstrated a higher discrimination among all analyzed periods than the Fourier transform. For the Fourier analysis, the LF of R-R intervals and VLF of systolic blood pressure show more evident difference for different body positions. For the wavelet analysis, the systolic blood pressures show much more evident difference than the R-R intervals. This study s...
Liu, Lin; Shen, Songhua; Liu, Qiang
2006-11-01
A novel method to detect power quality disturbance of distribution power system combing complex wavelet transform (WT) with radial basis function (RBF) neural network is presented. The paper tries to explain to design complex supported orthogonal wavelets by Morlet compactly supported orthogonal real wavelets, and then explore the extraction of disturbance signal to obtain the feature information, and finally propose several novel wavelet combined information (CI) to analyze the disturbance signal, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into RBF network for power quality disturbance pattern recognition. The power quality disturbance recognition model is established and the synthesized method of recursive orthogonal least squares algorithm (ROLSA) with improved Givens transform is used to fulfill the network structure and parameter identification. By means of choosing enough samples to train the recognition model, the type of disturbance can be obtained when signal representing fault is inputted to the trained network. The results of simulation analysis show that the complex WT combined with RBF network are more sensitive to signal singularity, and found to be significant improvement over current methods in real-time detection and better noise proof ability.
Wavelet Approach to Data Analysis, Manipulation, Compression, and Communication
2007-08-07
applications to animation movie production. According to our colleague Tony DeRose of Pixar Animation Studios, recently acquired by Walt Disney ...rendering and animation , as well as wavelet-based digital image restoration. (a) Papers published in peer-reviewed journals (N/A for none) List of...Accepted for publication. (18) Coherent line drawing (with H. Kang and S. Lee), ACM SIGGRAPH on Non-photorealistic Animation and Rendering
Analysis of a wavelet-based robust hash algorithm
Meixner, Albert; Uhl, Andreas
2004-06-01
This paper paper is a quantitative evaluation of a wavelet-based, robust authentication hashing algorithm. Based on the results of a series of robustness and tampering sensitivity tests, we describepossible shortcomings and propose variousmodifications to the algorithm to improve its performance. The second part of the paper describes and attack against the scheme. It allows an attacker to modify a tampered image, such that it's hash value closely matches the hash value of the original.
Seismic signal analysis based on the dual-tree complex wavelet packet transform
Institute of Scientific and Technical Information of China (English)
谢周敏; 王恩福; 张国宏; 赵国存; 陈旭庚
2004-01-01
We tried to apply the dual-tree complex wavelet packet transform in seismic signal analysis. The complex waveletpacket transform (CWPT) combine the merits of real wavelet packet transform with that of complex continuouswavelet transform (CCWT). It can not only pick up the phase information of signal, but also produce better "focalizing" function if it matches the phase spectrum of signals analyzed. We here described the dual-tree CWPT algorithm, and gave the examples of simulation and actual seismic signals analysis. As shown by our results, thedual-tree CWPT is a very efecfive method in analyzing seismic signals with non-linear phase.
Wavelet-based texture analysis of EEG signal for prediction of epileptic seizure
Petrosian, Arthur A.; Homan, Richard; Pemmaraju, Suryalakshmi; Mitra, Sunanda
1995-09-01
Electroencephalographic (EEG) signal texture content analysis has been proposed for early warning of an epileptic seizure. This approach was evaluated by investigating the interrelationship between texture features and basic signal informational characteristics, such as Kolmogorov complexity and fractal dimension. The comparison of several traditional techniques, including higher-order FIR digital filtering, chaos, autoregressive and FFT time- frequency analysis was also carried out on the same epileptic EEG recording. The purpose of this study is to investigate whether wavelet transform can be used to further enhance the developed methods for prediction of epileptic seizures. The combined consideration of texture and entropy characteristics extracted from subsignals decomposed by wavelet transform are explored for that purpose. Yet, the novel neuro-fuzzy clustering algorithm is performed on wavelet coefficients to segment given EEG recording into different stages prior to an actual seizure onset.
Indian Academy of Sciences (India)
Anita Gharekhan; Ashok N Oza; M B Sureshkumar; Asima Pradhan; Prasanta K Panigrahi
2010-12-01
Fluorescence characteristics of human breast tissues are investigated through wavelet transform and principal component analysis (PCA). Wavelet transform of polarized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate different tissue types. The emission range in the visible wavelength regime of 500–700 nm is analysed, with the excitation wavelength at 488 nm using laser as an excitation source, where flavin and porphyrin are some of the active fluorophores. A number of global and local parameters from principal component analysis of both high- and low-pass coefficients extracted in the wavelet domain, capturing spectral variations and subtle changes in the diseased tissues are clearly identifiable.
Analysis of corrosion behavior of LY12 in sodium chloride solution with wavelet transform technique
Institute of Scientific and Technical Information of China (English)
张昭; 曹发和; 程英亮; 张鉴清; 王建明; 曹楚南
2002-01-01
Wavelet transforms(WT) are proposed as an alternative tool to overcome the limitations of fast Fourier transforms(FFT) in the analysis of electrochemical noise(EN) data. The most relevant feature of this method of analysis is its capability of decomposing electrochemical noise records into different sets of wavelet coefficients(distinct type of events), which contains information about the time scale characteristic of the associated corrosion event. In this context, the potential noise fluctuations during the free corrosion of commercial aluminum alloy LY12 in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results show that the EN signal is composed of distinct type of events, which can be classified according to their scales, i.e. their time constants. Meanwhile, the energy distribution plot(EDP) can be used as "fingerprints" of EN signals and can be very useful for analyzing EN data in the future.
Institute of Scientific and Technical Information of China (English)
Z. Zhang; Q.D. Zhong; J.Q. Zhang; Y.L. Cheng; F.H. Cao; J.M. Wang; C.N. Cao
2002-01-01
Wavelet transforms (WT) are proposed as an alternative tool to overcome the limita-tions of Fourier transforms (FFT) in the analysis of electrochemical noise (EN) data.The most relevant feature of this method of analysis is its capability of decomposingelectrochemical noise records into different sets of wavelet coefficients, which containinformation about the time scale characteristic of the associated corrosion event. Inthis context, the potential noise fluctuations during the free corrosion of pure alu-minum in sodium chloride solution was recorded and analyzed with wavelet transformtechnique. The typical results showed that the EN signal is composed of distinct typeof events, which can be classified according to their scales, i.e. their time constants.Meanwhile, the energy distribution plot (EDP) can be used as "fingerprints" of ENsignals and can be very useful for analyzing EN data in the future.
DEFF Research Database (Denmark)
Dalgaard Ulriksen, Martin; Tcherniak, Dmitri; Kirkegaard, Poul Henning
2016-01-01
This study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to an SSP 34-m blade mounted on a test rig. Operational modal analysis was conducted to obtain mode shapes...
The methodology of wavelet analysis as a tool for cytology preparations image processing
Directory of Open Access Journals (Sweden)
Vyacheslav V. Lyashenko
2016-09-01
Conclusion: Consider the possibility and feasibility issues of applying wavelet analysis for processing cytology preparations images. This improves the quality of the analysis of cytology preparations images. This allows the to properly diagnose. [Cukurova Med J 2016; 41(3.000: 453-463
DEFF Research Database (Denmark)
Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Kirkegaard, Poul Henning
2014-01-01
The presented study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to a SSP 34m blade mounted on a test rig. Operational modal analysis (OMA) was conducted to obtain...
A Comparative Study of Wavelet Thresholding for Image Denoising
Directory of Open Access Journals (Sweden)
Arun Dixit
2014-11-01
Full Text Available Image denoising using wavelet transform has been successful as wavelet transform generates a large number of small coefficients and a small number of large coefficients. Basic denoising algorithm that using the wavelet transform consists of three steps – first computing the wavelet transform of the noisy image, thresholding is performed on the detail coefficients in order to remove noise and finally inverse wavelet transform of the modified coefficients is taken. This paper reviews the state of art methods of image denoising using wavelet thresholding. An Experimental analysis of wavelet based methods Visu Shrink, Sure Shrink, Bayes Shrink, Prob Shrink, Block Shrink and Neigh Shrink Sure is performed. These wavelet based methods are also compared with spatial domain methods like median filter and wiener filter. Results are evaluated on the basis of Peak Signal to Noise Ratio and visual quality of images. In the experiment, wavelet based methods perform better than spatial domain methods. In wavelet domain, recent methods like prob shrink, block shrink and neigh shrink sure performed better as compared to other wavelet based methods.
Fontana, Juan M; Chiu, Alan W L
2014-01-01
Myoelectric pattern recognition systems can translate muscle contractions into prosthesis commands; however, the lack of long-term robustness of such systems has resulted in low acceptability. Specifically, socket misalignment may cause disturbances related to electrodes shifting from their original recording location, which affects the myoelectric signals (MES) and produce degradation of the classification performance. In this work, the impact of such disturbances on wavelet features extracted from MES was evaluated in terms of classification accuracy. Additionally, two principal component analysis frameworks were studied to reduce the wavelet feature set. MES from seven able-body subjects and one subject with congenital transradial limb loss were studied. The electrode shifts were artificially introduced by recording signals during six sessions for each subject. A small drop in classification accuracy from 93.8% (no disturbances) to 88.3% (with disturbances) indicated that wavelet features were able to adapt to the variability introduced by electrode shift disturbances. The classification performance of the reduced feature set was significantly lower than the performance of the full wavelet feature set. The results observed in this study suggest that the effect of electrode shift disturbances on the MES can potentially be mitigated by using wavelet features embedded in a pattern recognition system.
Application of the wavelet image analysis technique to monitor cell concentration in bioprocesses
Directory of Open Access Journals (Sweden)
G. J. R. Garófano
2005-12-01
Full Text Available The growth of cells of great practical interest, such as, the filamentous cells of bacterium Streptomyces clavuligerus, the yeast Saccharomyces cerevisiae and the insect Spodoptera frugiperda (Sf9 cell, cultivated in shaking flasks with complex media at appropriate temperatures and pHs, was quantified by the new wavelet transform technique. This image analysis tool was implemented using Matlab 5.2 software to process digital images acquired of samples taken of these three types of cells throughoot their cultivation. The values of the average wavelet coefficients (AWCs of simplified images were compared with experimental measurements of cell concentration and with computer-based densitometric measurements. AWCs were shown to be directly proportional to measurements of cell concentration and to densitometric measurements, making evident the great potential of the wavelet transform technique to quantitatively estimate the growth of several types of cells.
Afeyan, Bedros; Starck, Jean Luc; Cuneo, Michael
2012-01-01
We introduce wavelets, curvelets and multiresolution analysis techniques to assess the symmetry of X ray driven imploding shells in ICF targets. After denoising X ray backlighting produced images, we determine the Shell Thickness Averaged Radius (STAR) of maximum density, r*(N, {\\theta}), where N is the percentage of the shell thickness over which to average. The non-uniformities of r*(N, {\\theta}) are quantified by a Legendre polynomial decomposition in angle, {\\theta}. Undecimated wavelet decompositions outperform decimated ones in denoising and both are surpassed by the curvelet transform. In each case, hard thresholding based on noise modeling is used. We have also applied combined wavelet and curvelet filter techniques with variational minimization as a way to select the significant coefficients. Gains are minimal over curvelets alone in the images we have analyzed.
Improving Resolution in k and r Space: A FEFF-based Wavelet for EXAFS Data Analysis
Funke, H.; Chukalina, M.; Voegelin, A.; Scheinost, A. C.
2007-02-01
Applying a wavelet analysis based on the Morlet mother function, we previously demonstrated the presence of both Al and Zn atoms in the first metal shell (r ≈ 3 Å from the central Zn atom) of Zn-Al layered double hydroxide (LDH). However, this approach was not suited to resolve the second and third metal shells (r ≈ 5 - 6 Å) in r and k space independently. Therefore, we developed a new FEFF-Morlet wavelet, where the EXAFS function itself, extracted from the FEFF model, is combined with the complex Morlet wavelet. With this method, we were able to distinguish the second metal shell (Zn atoms only) from the third metal shell (Zn and Al atoms), thereby proving a regular, dioctahedral distribution of Zn atoms in the hydroxide layers.
Value at risk estimation with entropy-based wavelet analysis in exchange markets
He, Kaijian; Wang, Lijun; Zou, Yingchao; Lai, Kin Keung
2014-08-01
In recent years, exchange markets are increasingly integrated together. Fluctuations and risks across different exchange markets exhibit co-moving and complex dynamics. In this paper we propose the entropy-based multivariate wavelet based approaches to analyze the multiscale characteristic in the multidimensional domain and improve further the Value at Risk estimation reliability. Wavelet analysis has been introduced to construct the entropy-based Multiscale Portfolio Value at Risk estimation algorithm to account for the multiscale dynamic correlation. The entropy measure has been proposed as the more effective measure with the error minimization principle to select the best basis when determining the wavelet families and the decomposition level to use. The empirical studies conducted in this paper have provided positive evidence as to the superior performance of the proposed approach, using the closely related Chinese Renminbi and European Euro exchange market.
Source location in plates based on the multiple sensors array method and wavelet analysis
Energy Technology Data Exchange (ETDEWEB)
Yang, Hong Jun; Shin, Tae Jin; Lee, Sang Kwon [Inha University, Incheon (Korea, Republic of)
2014-01-15
A new method for impact source localization in a plate is proposed based on the multiple signal classification (MUSIC) and wavelet analysis. For source localization, the direction of arrival of the wave caused by an impact on a plate and the distance between impact position and sensor should be estimated. The direction of arrival can be estimated accurately using MUSIC method. The distance can be obtained by using the time delay of arrival and the group velocity of the Lamb wave in a plate. Time delay is experimentally estimated using the continuous wavelet transform for the wave. The elasto dynamic theory is used for the group velocity estimation.
Thurner, S; Teich, M C; Thurner, Stefan; Feurstein, Markus C.; Teich, Malvin C.
1998-01-01
We applied multiresolution wavelet analysis to the sequence of times between human heartbeats (R-R intervals) and have found a scale window, between 16 and 32 heartbeats, over which the widths of the R-R wavelet coefficients fall into disjoint sets for normal and heart-failure patients. This has enabled us to correctly classify every patient in a standard data set as either belonging to the heart-failure or normal group with 100% accuracy, thereby providing a clinically significant measure of the presence of heart-failure from the R-R intervals alone. Comparison is made with previous approaches, which have provided only statistically significant measures.
Climatic drivers of vegetation based on wavelet analysis
Claessen, Jeroen; Martens, Brecht; Verhoest, Niko E. C.; Molini, Annalisa; Miralles, Diego
2017-04-01
Vegetation dynamics are driven by climate, and at the same time they play a key role in forcing the different bio-geochemical cycles. As climate change leads to an increase in frequency and intensity of hydro-meteorological extremes, vegetation is expected to respond to these changes, and subsequently feed back on their occurrence. This response can be analysed using time series of different vegetation diagnostics observed from space, in the optical (e.g. Normalised Difference Vegetation Index (NDVI), Solar Induced Fluorescence (SIF)) and microwave (Vegetation Optical Depth (VOD)) domains. In this contribution, we compare the climatic drivers of different vegetation diagnostics, based on a monthly global data-cube of 24 years at a 0.25° resolution. To do so, we calculate the wavelet coherence between each vegetation-related observation and observations of air temperature, precipitation and incoming radiation. The use of wavelet coherence allows unveiling the scale-by-scale response and sensitivity of the diverse vegetation indices to their climatic drivers. Our preliminary results show that the wavelet-based statistics prove to be a suitable tool for extracting information from different vegetation indices. Going beyond traditional methods based on linear correlations, the application of wavelet coherence provides information about: (a) the specific periods at which the correspondence between climate and vegetation dynamics is larger, (b) the frequencies at which this correspondence occurs (e.g. monthly or seasonal scales), and (c) the time lag in the response of vegetation to their climate drivers, and vice versa. As expected, areas of high rainfall volumes are characterised by a strong control of radiation and temperature over vegetation. Furthermore, precipitation is the most important driver of vegetation variability over short terms in most regions of the world - which can be explained by the rapid response of leaf development towards available water content
Søgaard, Andreas
For the LHC Run 2 and beyond, experiments are pushing both the energy and the intensity frontier so the need for robust and efficient pile-up mitigation tools becomes ever more pressing. Several methods exist, relying on uniformity of pile-up, local correlations of charged to neutral particles, and parton shower shapes, all in $y − \\phi$ space. Wavelets are presented as tools for pile-up removal, utilising their ability to encode position and frequency information simultaneously. This allows for the separation of individual hadron collision events by angular scale and thus for subtracting of soft, diffuse/wide-angle contributions while retaining the hard, small-angle components from the hard event. Wavelet methods may utilise the same assumptions as existing methods, the difference being the underlying, novel representation. Several wavelet methods are proposed and their effect studied in simple toy simulation under conditions relevant for the LHC Run 2. One full pile-up mitigation tool (‘wavelet analysis...
van den Berg, J. C.
2004-03-01
A guided tour J. C. van den Berg; 1. Wavelet analysis, a new tool in physics J.-P. Antoine; 2. The 2-D wavelet transform, physical applications J.-P. Antoine; 3. Wavelets and astrophysical applications A. Bijaoui; 4. Turbulence analysis, modelling and computing using wavelets M. Farge, N. K.-R. Kevlahan, V. Perrier and K. Schneider; 5. Wavelets and detection of coherent structures in fluid turbulence L. Hudgins and J. H. Kaspersen; 6. Wavelets, non-linearity and turbulence in fusion plasmas B. Ph. van Milligen; 7. Transfers and fluxes of wind kinetic energy between orthogonal wavelet components during atmospheric blocking A. Fournier; 8. Wavelets in atomic physics and in solid state physics J.-P. Antoine, Ph. Antoine and B. Piraux; 9. The thermodynamics of fractals revisited with wavelets A. Arneodo, E. Bacry and J. F. Muzy; 10. Wavelets in medicine and physiology P. Ch. Ivanov, A. L. Goldberger, S. Havlin, C.-K. Peng, M. G. Rosenblum and H. E. Stanley; 11. Wavelet dimension and time evolution Ch.-A. Guérin and M. Holschneider.
Directory of Open Access Journals (Sweden)
Andrzej Katunin
2015-01-01
Full Text Available The application of composite structures as elements of machines and vehicles working under various operational conditions causes degradation and occurrence of damage. Considering that composites are often used for responsible elements, for example, parts of aircrafts and other vehicles, it is extremely important to maintain them properly and detect, localize, and identify the damage occurring during their operation in possible early stage of its development. From a great variety of nondestructive testing methods developed to date, the vibration-based methods seem to be ones of the least expensive and simultaneously effective with appropriate processing of measurement data. Over the last decades a great popularity of vibration-based structural testing has been gained by wavelet analysis due to its high sensitivity to a damage. This paper presents an overview of results of numerous researchers working in the area of vibration-based damage assessment supported by the wavelet analysis and the detailed description of the Wavelet-based Structural Damage Assessment (WavStructDamAs Benchmark, which summarizes the author’s 5-year research in this area. The benchmark covers example problems of damage identification in various composite structures with various damage types using numerous wavelet transforms and supporting tools. The benchmark is openly available and allows performing the analysis on the example problems as well as on its own problems using available analysis tools.
The Research of Method of Malignant Load Identification Based on Wavelet Analysis
Wei, Shaoliang; Qin, Shiqun; Gao, Wenchang; Cheng, Fengyu; Cao, Zhongyue
Using wavelet analysis to analyze waveforms of various loads, analytical processing with Matlab, to get difference between various capacitive, inductive, and resistive load's waveforms, just to distinguish the malignant power load and general load. It is of great importance to campus apartment for automatic recognition of malignant load, and then to prevent campus fires.
MULTIRESOLUTION ANALYSIS, SELF-SIMILAR TILINGS AND HAAR WAVELETS ON THE HEISENBERG GROUP
Institute of Scientific and Technical Information of China (English)
Liu Heping; Liu Yu; Wang Haihui
2009-01-01
In this article, the properties of multiresolution analysis and self-similar tilings on the Heisenberg group are studied. Moreover, we establish a theory to construct an orthonormal Haar wavelet base in L~2(H~d) by using self-similar tilings for the acceptable dilations on the Heisenberg group.
Directory of Open Access Journals (Sweden)
Stefania Salvatore
2016-07-01
Full Text Available Abstract Background Wastewater-based epidemiology (WBE is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA and to wavelet principal component analysis (WPCA which is more flexible temporally. Methods We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. Results The first three principal components (PCs, functional principal components (FPCs and wavelet principal components (WPCs explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. Conclusion FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.
Analysis of Acoustic Emission Signals using WaveletTransformation Technique
Directory of Open Access Journals (Sweden)
S.V. Subba Rao
2008-07-01
Full Text Available Acoustic emission (AE monitoring is carried out during proof pressure testing of pressurevessels to find the occurrence of any crack growth-related phenomenon. While carrying out AEmonitoring, it is often found that the background noise is very high. Along with the noise, thesignal includes various phenomena related to crack growth, rubbing of fasteners, leaks, etc. Dueto the presence of noise, it becomes difficult to identify signature of the original signals related to the above phenomenon. Through various filtering/ thresholding techniques, it was found that the original signals were getting filtered out along with noise. Wavelet transformation technique is found to be more appropriate to analyse the AE signals under such situations. Wavelet transformation technique is used to de-noise the AE data. The de-noised signal is classified to identify a signature based on the type of phenomena.Defence Science Journal, 2008, 58(4, pp.559-564, DOI:http://dx.doi.org/10.14429/dsj.58.1677
Directory of Open Access Journals (Sweden)
Avdakovic Samir
2014-08-01
Full Text Available Analysis of power consumption presents a very important issue for power distribution system operators. Some power system processes such as planning, demand forecasting, development, etc.., require a complete understanding of behaviour of power consumption for observed area, which requires appropriate techniques for analysis of available data. In this paper, two different time-frequency techniques are applied for analysis of hourly values of active and reactive power consumption from one real power distribution transformer substation in urban part of Sarajevo city. Using the continuous wavelet transform (CWT with wavelet power spectrum and global wavelet spectrum some properties of analysed time series are determined. Then, empirical mode decomposition (EMD and Hilbert-Huang Transform (HHT are applied for the analyses of the same time series and the results showed that both applied approaches can provide very useful information about the behaviour of power consumption for observed time interval and different period (frequency bands. Also it can be noticed that the results obtained by global wavelet spectrum and marginal Hilbert spectrum are very similar, thus confirming that both approaches could be used for identification of main properties of active and reactive power consumption time series.
Energy Technology Data Exchange (ETDEWEB)
Espada, L.; Sanjurjo, M.; Urrejola, S.; Bouzada, F.; Rey, G.; Sanchez, A.
2003-07-01
Given its simplicity and low cost compared to other types of methodologies, the measurement and interpretation of Electrochemical Noise, is consolidating itself as one of the analysis methods most frequently used for the interpretation of corrosion. As the technique is still evolving, standard treatment methodologies for data retrieved in experiments do not exist yet. To date, statistical analysis and the Fourier analysis are commonly used in order to establish the parameters that may characterize the recording of potential and current electrochemical noise. This study introduces a new methodology based on wavelet analysis and presents its advantages with regards to the Fourier analysis in distinguishes periodical and non-periodical variations in the signal power in time and frequency, as opposed to the Fourier analysis that only considers the frequency. (Author) 15 refs.
Izmaylov, R.; Lebedev, A.
2015-08-01
Centrifugal compressors are complex energy equipment. Automotive control and protection system should meet the requirements: of operation reliability and durability. In turbocompressors there are at least two dangerous areas: surge and rotating stall. Antisurge protecting systems usually use parametric or feature methods. As a rule industrial system are parametric. The main disadvantages of anti-surge parametric systems are difficulties in mass flow measurements in natural gas pipeline compressor. The principal idea of feature method is based on the experimental fact: as a rule just before the onset of surge rotating or precursor stall established in compressor. In this case the problem consists in detecting of unsteady pressure or velocity fluctuations characteristic signals. Wavelet analysis is the best method for detecting onset of rotating stall in spite of high level of spurious signals (rotating wakes, turbulence, etc.). This method is compatible with state of the art DSP systems of industrial control. Examples of wavelet analysis application for detecting onset of rotating stall in typical stages centrifugal compressor are presented. Experimental investigations include unsteady pressure measurement and sophisticated data acquisition system. Wavelet transforms used biorthogonal wavelets in Mathlab systems.
Analysis of the Emitted Wavelet of High-Resolution Bowtie GPR Antennas
Directory of Open Access Journals (Sweden)
Manuel Pereira
2009-06-01
Full Text Available Most Ground Penetrating Radars (GPR cover a wide frequency range by emitting very short time wavelets. In this work, we study in detail the wavelet emitted by two bowtie GPR antennas with nominal frequencies of 800 MHz and 1 GHz. Knowledge of this emitted wavelet allows us to extract as much information as possible from recorded signals, using advanced processing techniques and computer simulations. Following previously published methodology used by Rial et al. [1], which ensures system stability and reliability in data acquisition, a thorough analysis of the wavelet in both time and frequency domain is performed. Most of tests were carried out with air as propagation medium, allowing a proper analysis of the geometrical attenuation factor. Furthermore, we attempt to determine, for each antenna, a time zero in the records to allow us to correctly assign a position to the reflectors detected by the radar. Obtained results indicate that the time zero is not a constant value for the evaluated antennas, but instead depends on the characteristics of the material in contact with the antenna.
The analysis of VF and VT with wavelet-based Tsallis information measure
Energy Technology Data Exchange (ETDEWEB)
Huang Hai [Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai (China)]. E-mail: hai_h@sjtu.edu.cn; Xie Hongbo [Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai (China); Wang Zhizhong [Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai (China)
2005-03-07
We undertake the study of ventricular fibrillation and ventricular tachycardia by recourse to wavelet-based multiresolution analysis. Comparing with conventional Shannon entropy analysis of signal, we proposed a new application of Tsallis entropy analysis. It is shown that, as a criteria for detecting between ventricular fibrillation and ventricular tachycardia, Tsallis' multiresolution entropy (MRET) provides one with better discrimination power than the Shannon's multiresolution entropy (MRE)
Winklewski, P J; Gruszecki, M; Wolf, J; Swierblewska, E; Kunicka, K; Wszedybyl-Winklewska, M; Guminski, W; Zabulewicz, J; Frydrychowski, A F; Bieniaszewski, L; Narkiewicz, K
2015-05-01
Pial artery adjustments to changes in blood pressure (BP) may last only seconds in humans. Using a novel method called near-infrared transillumination backscattering sounding (NIR-T/BSS) that allows for the non-invasive measurement of pial artery pulsation (cc-TQ) in humans, we aimed to assess the relationship between spontaneous oscillations in BP and cc-TQ at frequencies between 0.5 Hz and 5 Hz. We hypothesized that analysis of very short data segments would enable the estimation of changes in the cardiac contribution to the BP vs. cc-TQ relationship during very rapid pial artery adjustments to external stimuli. BP and pial artery oscillations during baseline (70s and 10s signals) and the response to maximal breath-hold apnea were studied in eighteen healthy subjects. The cc-TQ was measured using NIR-T/BSS; cerebral blood flow velocity, the pulsatility index and the resistive index were measured using Doppler ultrasound of the left internal carotid artery; heart rate and beat-to-beat systolic and diastolic blood pressure were recorded using a Finometer; end-tidal CO2 was measured using a medical gas analyzer. Wavelet transform analysis was used to assess the relationship between BP and cc-TQ oscillations. The recordings lasting 10s and representing 10 cycles with a frequency of ~1 Hz provided sufficient accuracy with respect to wavelet coherence and wavelet phase coherence values and yielded similar results to those obtained from approximately 70cycles (70s). A slight but significant decrease in wavelet coherence between augmented BP and cc-TQ oscillations was observed by the end of apnea. Wavelet transform analysis can be used to assess the relationship between BP and cc-TQ oscillations at cardiac frequency using signals intervals as short as 10s. Apnea slightly decreases the contribution of cardiac activity to BP and cc-TQ oscillations.
Liu, Yao; Wang, Xiufeng; Lin, Jing; Zhao, Wei
2016-11-01
Motor current is an emerging and popular signal which can be used to detect machining chatter with its multiple advantages. To achieve accurate and reliable chatter detection using motor current, it is important to make clear the quantitative relationship between motor current and chatter vibration, which has not yet been studied clearly. In this study, complex continuous wavelet coherence, including cross wavelet transform and wavelet coherence, is applied to the correlation analysis of motor current and chatter vibration in grinding. Experimental results show that complex continuous wavelet coherence performs very well in demonstrating and quantifying the intense correlation between these two signals in frequency, amplitude and phase. When chatter occurs, clear correlations in frequency and amplitude in the chatter frequency band appear and the phase difference of current signal to vibration signal turns from random to stable. The phase lead of the most correlated chatter frequency is the largest. With the further development of chatter, the correlation grows up in intensity and expands to higher order chatter frequency band. The analyzing results confirm that there is a consistent correlation between motor current and vibration signals in the grinding chatter process. However, to achieve accurate and reliable chatter detection using motor current, the frequency response bandwidth of current loop of the feed drive system must be wide enough to response chatter effectively.
Application of Wavelet Analysis to Interference Elimination for Geochemical Hydrocarbon Exploration
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multi-resolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.
Quantitative Multiscale Analysis using Different Wavelets in 1D Voice Signal and 2D Image
Shakhakarmi, Niraj
2012-01-01
Mutiscale analysis represents multiresolution scrutiny of a signal to improve its signal quality. Multiresolution analysis of 1D voice signal and 2D image is conducted using DCT, FFT and different wavelets such as Haar, Deubachies, Morlet, Cauchy, Shannon, Biorthogonal, Symmlet and Coiflet deploying the cascaded filter banks based decomposition and reconstruction. The outstanding quantitative analysis of the specified wavelets is done to investigate the signal quality, mean square error, entropy and peak-to-peak SNR at multiscale stage-4 for both 1D voice signal and 2D image. In addition, the 2D image compression performance is significantly found 93.00% in DB-4, 93.68% in bior-4.4, 93.18% in Sym-4 and 92.20% in Coif-2 during the multiscale analysis.
Dantas, José L; Camata, Thiago V; Brunetto, Maria A C; Moraes, Antonio C; Abrão, Taufik; Altimari, Leandro R
2010-01-01
Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in isometric and dynamic exercise. The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in isometric and dynamic exercise (P>0.05). However, the results of the variance was lower for both types of exercise in CWT compared to STFT (P signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.
Wavelet analysis method for detection of DDoS attack on the basis of Self-similarity
Institute of Scientific and Technical Information of China (English)
REN Xunyi; WANG Ruchuan; WANG Haiyan
2007-01-01
As the waditional methods were not suitable for the detection of small distribute denial of service(DDoS)attack and identification of busy traffc.on the basis of the influence of DDoS attack,one wavelet analysis method was proposed.Wavelet method of toefficient variance analysis was deduced and a software model for the method was designed.In addition.key issues of the choice of wavelet and calculation of Hurst were resolved.The experimental results show that the proposed method has more advantages in accurately identilying busy traffic and detection of small DDoS attack.
Wavelet Analyses and Applications
Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.
2009-01-01
It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…
Analysis of Energy Overshoot of High Frequency Waves with Wavelet Transform
Institute of Scientific and Technical Information of China (English)
WEN Fan
2000-01-01
A study is made on the overshoot phenomena in wind-generated waves. The surface displace ments of time-growing waves are measured at four fetches in a wind wave channel. The evolution of high frequency waves is displayed with wavelet transform. The results are compared with Sutherland＇s. It is found that high frequency wave components experience much stronger energy overshoot in the evolution.The energy of high frequency waves decreases greatly after overshoot
Wavelets, vibrations and scalings
Meyer, Yves
1997-01-01
Physicists and mathematicians are intensely studying fractal sets of fractal curves. Mandelbrot advocated modeling of real-life signals by fractal or multifractal functions. One example is fractional Brownian motion, where large-scale behavior is related to a corresponding infrared divergence. Self-similarities and scaling laws play a key role in this new area. There is a widely accepted belief that wavelet analysis should provide the best available tool to unveil such scaling laws. And orthonormal wavelet bases are the only existing bases which are structurally invariant through dyadic dilations. This book discusses the relevance of wavelet analysis to problems in which self-similarities are important. Among the conclusions drawn are the following: 1) A weak form of self-similarity can be given a simple characterization through size estimates on wavelet coefficients, and 2) Wavelet bases can be tuned in order to provide a sharper characterization of this self-similarity. A pioneer of the wavelet "saga", Meye...
A DNA Structure-Based Bionic Wavelet Transform and Its Application to DNA Sequence Analysis
Directory of Open Access Journals (Sweden)
Fei Chen
2003-01-01
Full Text Available DNA sequence analysis is of great significance for increasing our understanding of genomic functions. An important task facing us is the exploration of hidden structural information stored in the DNA sequence. This paper introduces a DNA structure-based adaptive wavelet transform (WT – the bionic wavelet transform (BWT – for DNA sequence analysis. The symbolic DNA sequence can be separated into four channels of indicator sequences. An adaptive symbol-to-number mapping, determined from the structural feature of the DNA sequence, was introduced into WT. It can adjust the weight value of each channel to maximise the useful energy distribution of the whole BWT output. The performance of the proposed BWT was examined by analysing synthetic and real DNA sequences. Results show that BWT performs better than traditional WT in presenting greater energy distribution. This new BWT method should be useful for the detection of the latent structural features in future DNA sequence analysis.
Wavelet multiscale analysis for Hedge Funds: Scaling and strategies
Conlon, T.; Crane, M.; Ruskin, H. J.
2008-09-01
The wide acceptance of Hedge Funds by Institutional Investors and Pension Funds has led to an explosive growth in assets under management. These investors are drawn to Hedge Funds due to the seemingly low correlation with traditional investments and the attractive returns. The correlations and market risk (the Beta in the Capital Asset Pricing Model) of Hedge Funds are generally calculated using monthly returns data, which may produce misleading results as Hedge Funds often hold illiquid exchange-traded securities or difficult to price over-the-counter securities. In this paper, the Maximum Overlap Discrete Wavelet Transform (MODWT) is applied to measure the scaling properties of Hedge Fund correlation and market risk with respect to the S&P 500. It is found that the level of correlation and market risk varies greatly according to the strategy studied and the time scale examined. Finally, the effects of scaling properties on the risk profile of a portfolio made up of Hedge Funds is studied using correlation matrices calculated over different time horizons.
Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria
Energy Technology Data Exchange (ETDEWEB)
Chellali, F. [Unite de Recherche Appliquee en Energies Renouvelables, Ghardaia (Algeria); Ecole Nationale Polytechnique, ENP, El-Harrach (Algeria); Khellaf, A. [Centre de Recherche et Developpement des Energies Renouvelables, CDER, Bouzeriah (Algeria); Belouchrani, A. [Ecole Nationale Polytechnique, ENP, El-Harrach (Algeria)
2010-06-15
Spectra of many meteorological data such as wind speed and temperature are time variable. Thus a Fourier analysis is not sufficient. In the present work, the wavelet transform is applied as a time-frequency analysis to the meteorological data for the region of Adrar (27.9 N, 0.3 W, 263 m), Algeria. This analysis is carried out in order to investigate the power spectra behaviors of both temperature and wind speed and their variations with time. To determine the relationship between these two meteorological parameters, the cross wavelet analysis is also applied. The study is carried out using data extending over a period of four years. The analysis is applied over a frequency range from 0.002 to 0.5 cycles per day. The results show that significant synoptic oscillations of periods between 2 and 16 days occur mainly in the cold season in both wind and temperature time series. Those oscillations are characterized by short life durations of one to few weeks. Wavelet power spectrum has also revealed the presence of intra-seasonal oscillations of periods between 30 and 60 days. These intra-seasonal oscillations have been observed mainly in the warm seasons. This study reveals also that temperature and wind speed co-vary especially at the synoptic and the intra-seasonal frequencies. (author)
Li, Xiaoli; Xie, Chuanqi; He, Yong; Qiu, Zhengjun; Zhang, Yanchao
2012-01-01
Effects of the moisture content (MC) of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325-1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT) and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034). Moreover, the effective wavelengths for MC measurement were detected at range of 888-1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.
Directory of Open Access Journals (Sweden)
Chuanqi Xie
2012-07-01
Full Text Available Effects of the moisture content (MC of tea on diffuse reflectance spectroscopy were investigated by integrated wavelet transform and multivariate analysis. A total of 738 representative samples, including fresh tea leaves, manufactured tea and partially processed tea were collected for spectral measurement in the 325–1,075 nm range with a field portable spectroradiometer. Then wavelet transform (WT and multivariate analysis were adopted for quantitative determination of the relationship between MC and spectral data. Three feature extraction methods including WT, principal component analysis (PCA and kernel principal component analysis (KPCA were used to explore the internal structure of spectral data. Comparison of those three methods indicated that the variables generated by WT could efficiently discover structural information of spectral data. Calibration involving seeking the relationship between MC and spectral data was executed by using regression analysis, including partial least squares regression, multiple linear regression and least square support vector machine. Results showed that there was a significant correlation between MC and spectral data (r = 0.991, RMSEP = 0.034. Moreover, the effective wavelengths for MC measurement were detected at range of 888–1,007 nm by wavelet transform. The results indicated that the diffuse reflectance spectroscopy of tea is highly correlated with MC.
Data-adaptive wavelets and multi-scale singular-spectrum analysis
Yiou, Pascal; Sornette, Didier; Ghil, Michael
2000-08-01
Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series, including the case where intermittency gives rise to the divergence of their variance. The wavelet transform resembles a local Fourier transform within a finite moving window whose width W, proportional to the major period of interest, is varied to explore a broad range of such periods. SSA, on the other hand, relies on the construction of the lag-correlation matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W= MΔ t with Δ t as the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M≤ W≤ N, where N is the length of the time series. Multi-scale SSA varies W, while keeping a fixed W/ M ratio, and uses the eigenvectors of the corresponding lag-correlation matrix C(M) as data-adaptive wavelets; successive eigenvectors of C(M) correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency domain by a suitable localization of the signal’s correlation matrix. We present several examples of application to synthetic signals with fractal or power-law behavior which mimic selected features of certain climatic or geophysical time series. The method is applied next to the monthly values of the Southern Oscillation Index (SOI) for 1933-1996; the SOI time series is widely believed to capture major features of the El Niño/Southern Oscillation (ENSO) in the Tropical Pacific. Our methodology highlights an abrupt periodicity shift in the SOI near 1960. This abrupt shift between 5 and 3 years supports the Devil’s staircase scenario for the ENSO phenomenon (preliminary results of this study
Wavelets theory, algorithms, and applications
Montefusco, Laura
2014-01-01
Wavelets: Theory, Algorithms, and Applications is the fifth volume in the highly respected series, WAVELET ANALYSIS AND ITS APPLICATIONS. This volume shows why wavelet analysis has become a tool of choice infields ranging from image compression, to signal detection and analysis in electrical engineering and geophysics, to analysis of turbulent or intermittent processes. The 28 papers comprising this volume are organized into seven subject areas: multiresolution analysis, wavelet transforms, tools for time-frequency analysis, wavelets and fractals, numerical methods and algorithms, and applicat
Research on Fault Diagnosis System of a Diesel Engine Based on Wavelet Analysis and LabVIEW Software
Directory of Open Access Journals (Sweden)
Eidam Ahmed Hebiel
2014-05-01
Full Text Available Experiment presented in this study, used vibration data obtained from a four-stroke, 295 diesel engine. Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head. The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on discrete wavelet analysis and LabVIEW software. The cylinder-head vibration signals were captured through a piezoelectric acceleration sensor, that was attached to a surface of the cylinder head of the engine, while the engine was running at two speeds (620 and 1300 rpm and two loads (15 and 45 N•m. Data was gathered from five different conditions, associated with the cylinder head such as single cylinder shortage, double cylinders shortage, intake manifold obstruction, exhaust manifold obstruction and normal condition. After decomposing the vibration signals into some of the details and approximations coefficients with db5 mother wavelet and decomposition level 5, the energies were extracted from each frequency sub-band of healthy and unhealthy conditions as a feature of engine fault diagnosis. By doing so, normal and abnormal conditions behavior could be effectively distinguished by comparing the energy accumulations of each sub-band. The results showed that detection of fault by discrete wavelet analysis is practicable. Finally, two techniques, Back-Propagation Neural Network (BPNN and Support Victor Machine (SVM were applied to the signal that was collected from the diesel engine head. The experimental results showed that BPNN was more effective in fault diagnosis of the internal-combustion engine, with various fault conditions, than SVM.
Dual wavelet energy approach-regression analysis for exploring steel micro structural behavior
Bettayeb, Fairouz
2012-05-01
Ultrasonic Ndt data are time series data decomposed in signal plus noise obtained from traveling ultrasonic waves inside a material and captured by piezoelectric sensors. The natural inhomogeneous and anisotropy character of steel made material causes high acoustic attenuation and scattering effect. This makes data interpretation highly complex for most of qualified Ndt operators. In this paper we address the non linear features of back scattered ultrasonic waves from steel plates. The structural noise data captured from the specimens, and processed by an algorithm based on wavelet energy approach, show significant insights into the relationship between backscattered noise and material microstructures. This algorithm along with correlation coefficients, residuals and interpolations calculations of processed ultrasonic data seems to be a well-adapted signal analysis tool for viewing material micro structural dimension scales. Experiments show interesting 3D interface and indicate a quasi linear signal energy distribution at micro structural level. It suggests probable incidence of microstructure acoustic signatures at different energy scales of the material phases. In conclusion multi polynomial interpolations of processed noise data exhibit an attractor shape which should involves chaos theory noise data modeling.
Cumulative areawise testing in wavelet analysis and its application to geophysical time series
Directory of Open Access Journals (Sweden)
J. A. Schulte
2015-07-01
Full Text Available Statistical significance testing in wavelet analysis was improved through the development of a cumulative areawise test. The test was developed to eliminate the selection of two significance levels that an existing geometric test requires for implementation. The selection of two significance levels was found to make the test sensitive to the chosen pointwise significance level, which may preclude further scientific investigation. A set of experiments determined that the cumulative areawise test has greater statistical power than the geometric test in most cases, especially when the signal-to-noise ratio is high. The number of false positives identified by the tests was found to be similar if the respective significance levels were set to 0.05. The new testing procedure was applied to the time series of the Atlantic Multi-decadal Oscillation (AMO, North Atlantic Oscillation (NAO, Pacific Decadal Oscillation (PDO, and Niño 3.4 index. The testing procedure determined that the NAO, PDO, and AMO are consistent with red-noise processes, whereas significant power was found in the 2–7 year period band for the Niño 3.4 index.
Directory of Open Access Journals (Sweden)
Imaouchen Yacine
2015-01-01
Full Text Available To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.
Cao, Guangxi; Xu, Wei
2016-02-01
This paper investigates the nonlinear structure between carbon and energy markets by employing the maximum overlap wavelet transform (MODWT) as well as the multifractal detrended cross-correlation analysis based on maximum overlap wavelet transform (MFDCCA-MODWT). Based on the MODWT multiresolution analysis and the statistic Qcc(m) significance, relatively significant cross-correlations are obtained between carbon and energy future markets either on different time scales or on the whole. The result of the Granger causality test indicates bidirectional Granger causality between carbon and electricity future markets, although the Granger causality relationship between the carbon and oil price is not evident. The existence of multifractality for the returns between carbon and energy markets is proven with the MFDCCA-MODWT algorithm. In addition, results of investigating the origin of multifractality demonstrate that both long-range correlations and fat-tailed distributions play important roles in the contributions of multifractality.
Detecting emboli from Doppler ultrasound signals with the wavelet packet analysis method
Institute of Scientific and Technical Information of China (English)
CHEN Xi; SUN Zhimin; WANG Yuanyuan; WANG Weiqi
2004-01-01
A Doppler ultrasound analysis method based on wavelet package transform was proposed for embolic detection. The embolic Doppler signal was firstly decomposed using the wavelet packet. Then the sensitive characteristics were calculated from each sub-band signal and used in the emboli classification. This method was applied to analyze 300 cases simulated and 163 cases clinical Doppler signals. The minimum error ratio of embolic detection using this method was 13 percents lower than that using the traditional spectrogram analysis method.It was shown that this method overcame the limit between the time and frequency resolution in the short time Fourier transform, improved the accuracy of embolic detection greatly and extracted more reliable parameters for the clinical diagnosis.
Institute of Scientific and Technical Information of China (English)
ZHOU Fu-chang; CHEN Jin; HE Jun; BI Guo; LI Fu-cai; ZHANG Gui-cai
2005-01-01
The vibration signals of rolling element bearing are produced by a combination of periodic and random processes due to the machine's rotation cycle and interaction with the real world. The combination of such components can give rise to signals, which have periodically time-varying ensemble statistical and are best considered as cyclostationary. When the early fault occurs, the background noise is very heavy, it is difficult to disclose the latent periodic components successfully using cyclostationary analysis alone. In this paper the degree of cyclostationarity is combined with wavelet filtering for detection of rolling element bearing early faults. Using the proposed entropy minimization rule. The parameters of the wavelet filter are optimized. This method is shown to be effective in detecting rolling element bearing early fault when cyclostationary analysis by itself fails.
Institute of Scientific and Technical Information of China (English)
DING YouLiang; LI AiQun; LIU Tao
2008-01-01
The structural damage alarming method based on wavelet packet energy spectrum (WPES) for long-span cable-stayed bridges is presented through combination of ambient vibration test and wavelet packet analysis.The environmental variability in the measured WPES and damage alarming indices ERVD of the Runyang Ca-ble-stayed Bridge are discussed in detail using the wavelet packet analysis of the measured acceleration responses of the bridge under daily environmental condi-tions.The analysis results reveal that the actual environmental conditions includ-ing traffic Ioadings,environmental temperature and typhoon Ioadings have re-markable correlations with the measured WPES.The changes of environmental temperature have a long-term trend influence on the WPES,while the influences of traffic and typhoon Ioadings on the measured WPES of the bridge present instan-taneous changes because of the nonstationary properties of the Ioadings.The analysis results of the measured responses further reveal that the damage alarm-ing indices ERVD can sensitively reflect the influences of environmental tempera-ture and typhoon Ioadings on the dynamic properties of Runyang Cable-stayed Bridge.Therefore,the proposed structural damage alarming indices ERVD under ambient vibrations are suitable for real-time damage alarming for long-span ca-ble-stayed bridges.
Institute of Scientific and Technical Information of China (English)
2008-01-01
The structural damage alarming method based on wavelet packet energy spectrum (WPES) for long-span cable-stayed bridges is presented through combination of ambient vibration test and wavelet packet analysis. The environmental variability in the measured WPES and damage alarming indices ERVD of the Runyang Cable-stayed Bridge are discussed in detail using the wavelet packet analysis of the measured acceleration responses of the bridge under daily environmental conditions. The analysis results reveal that the actual environmental conditions including traffic loadings, environmental temperature and typhoon loadings have remarkable correlations with the measured WPES. The changes of environmental temperature have a long-term trend influence on the WPES, while the influences of traffic and typhoon loadings on the measured WPES of the bridge present instantaneous changes because of the nonstationary properties of the loadings. The analysis results of the measured responses further reveal that the damage alarming indices ERVD can sensitively reflect the influences of environmental temperature and typhoon loadings on the dynamic properties of Runyang Cable-stayed Bridge. Therefore, the proposed structural damage alarming indices ERVD under ambient vibrations are suitable for real-time damage alarming for long-span cable-stayed bridges.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
Variations of land surface fluxes of sensible heat (H), latent heat ( LE ), and CO2(F-CO2) obtained from the eddy-covariance measurements above a winter wheat field from March 30 to April 24, 2001 have been studied at scales ranging from 10 minutes to days. Wavelet transform is used in the analysis of land surface fluxes and atmospheric stability (ζ) calculated from the measurements to reveal the changes in land surface fluxes in hours to days scales. The main results are: (1) Concise and compact information about the fluxes, net radiation (Rn), temperature (T) and ζ in the scale-time domain are extracted from the data by continuous wavelet analysis,and 1 day, 0.5 day and short-period (shorter than 0.5 day) components are revealed. Continuous wavelet coefficients can be used to characterize periodic components of changes in fluxes and ζ. (2) Discrete-time multi-resolution analysis can be used to concentrate total energy variance of time series of the measurements to a small number of coefficients, plotting the relative energy distribution to get several meaningful characteristics of the data. (3) Under neutral atmospheric conditions, the relative energy distributions of the Haar multi-resolution analysis of the three non-dimensional coefficients (T/T* , q/q * and c/c * ) display clear similarities.
Hramov, Alexander E; Makarov, Valeri A; Pavlov, Alexey N; Sitnikova, Evgenia
2015-01-01
This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural ...
Application of Holter ECG Signal Analysis Based on Wavelet and Data Mining Technique
Institute of Scientific and Technical Information of China (English)
余辉; 谢远国; 周仲兴; 吕扬生
2004-01-01
A new model based on dyadic differential wavelet was developed for detecting the R peak in Holter ECG signal according to the design of data mining. The Mallat recursive filter algorithm was introduced to calculate wavelet and optimize the detection algorithm which is based on the equivalent filter technique. The detection algorithm has been verified by MIT arrhythmia database with a high efficiency of 99%. After optimization,the algorithm was put into clinical experiment and tested in the Air Force Hospital in Tianjin for about two months. After about 108 hearts beating test of more than 100 patients, the total efficient detection rate has reached 97%. Now this algorithm module has been applied in business software and shows perfect performance under the complex conditions such as the inversion of heart beating, the falling off of the electrodes, the excursion of base line and so on.
Aboufadel, Edward
1999-01-01
An accessible and practical introduction to wavelets. With applications in image processing, audio restoration, seismology, and elsewhere, wavelets have been the subject of growing excitement and interest over the past several years. Unfortunately, most books on wavelets are accessible primarily to research mathematicians. Discovering Wavelets presents basic and advanced concepts of wavelets in a way that is accessible to anyone with only a fundamental knowledge of linear algebra. The basic concepts of wavelet theory are introduced in the context of an explanation of how the FBI uses wavelets
Application of Wavelet Packet Energy Spectrum to Extract the Feature of the Pulse Signal
Institute of Scientific and Technical Information of China (English)
Dian-guo CAO; Yu-qiang WU; Xue-wen SHI; Peng WANG
2010-01-01
The wavelet packet is presented as a new kind of multi-scale analysis technique followed by Wavelet analysis. The fundamental and realization arithmetic of the wavelet packet analysis method are described in this paper. A new application approach of the wavelet packed method to extract the feature of the pulse signal from energy distributing angle is expatiated. It is convenient for the microchip to process and judge by using the wavelet packet analysis method to make the pulse signals quantized and analyzed. Kinds of experiments are simulated in the lab, and the experiments prove that it is a convenient and accurate method to extract the feature of the pulse signal based on wavelet packed-energy spectrum analysis.
Some Problems on the Global Wavelet Spectrum
Institute of Scientific and Technical Information of China (English)
WU Shu; LIU Qinyu
2005-01-01
In order to test the validity of the global wavelet spectrum - a new period analysis method based on wavelet analysis, we carried out some simple experiments. In our experiments we used idealized time series and real Ni(~n)o 3 sea surface temperature (SST) for testing purposes. First we combined different signals which have the same power but different periods into some new time series. Then we calculated the global wavelet spectra and Fourier power spectra for the testing time series. The testing results revealed that on some occasions the global wavelet spectrum tends to amplify the relative power of longer periods. By making comparisons with the results obtained by the traditional Fourier power spectrum, we demonstrated that on an occasion when the global wavelet spectrum does not work the Fourier power spectrum can be used to achieve the right results. Hence it is recommended that when making period analysis with the global wavelet spectrum one needs to do further tests to confirm their results.
Brault, Patrice
2012-01-01
The construction of a spatio-temporal wavelet and its tuning to speed was first realized in the 90s on the Morlet wavelet by M. Duval-Destin \\cite{Duval-Destin91a,Duval-Destin92}. This enabled to demonstrate the capacities of the speed-tuned Morlet for psychovisual analysis. This construction was also used very efficiently in a powerful aerial target tracking algorithm by Mujica et al.\\cite{Mujica99,Mujica2000}. In the last decade, this tool was proposed as an elegant and efficient alternative framework to the Optical Flow (OF), the Block Matching (BM) or the phase difference, for the study of motion estimation in image sequences. Nevertheless, the aperture selectivity of the 2D+T Morlet wavelet presents some difficulties. Here we propose to replace the 2D Morlet wavelet by a Gaussian-Conical (GC) wavelet for the spatial part of the spatio-temporal wavelet, since the GC wavelet has a better aperture selectivity and allows a very simple adjustment of the aperture. Therefore we build a new, highly directional, ...
Wavelet analysis of location and intensity of spatial rhythms in hippocampus
Lavrova, Anastasia I.; Postnikov, Eugene B.
2013-10-01
Hippocampal formation is responsible for the memory processes and spatial navigation; however, underlaying mechanisms and firing location of specific neuronal cells are still poorly investigated. We propose the wavelet analysis for the description of generation of polyrhythmic signals in the human hippocampal system. We analyze experimental data obtained earlier in hippocampal shearers. This method allows comparing with the simple Fourier method to investigate firing patterns in details, namely, to characterize their location and firing intensity.
Wavelet-based analysis of gastric microcirculation in rats with ulcer bleedings
Pavlov, A. N.; Rodionov, M. A.; Pavlova, O. N.; Semyachkina-Glushkovskaya, O. V.; Berdnikova, V. A.; Kuznetsova, Ya. V.; Semyachkin-Glushkovskij, I. A.
2012-03-01
Studying of nitric oxide (NO) dependent mechanisms of regulation of microcirculation in a stomach can provide important diagnostic markers of the development of stress-induced ulcer bleedings. In this work we use a multiscale analysis based on the discrete wavelet-transform to characterize a latent stage of illness formation in rats. A higher sensitivity of stomach vessels to the NO-level in ill rats is discussed.
Allen, John; Di Maria, Costanzo; Mizeva, Irina; Podtaev, Sergey
2013-07-01
The physiological changes following a deep inspiratory gasp (DIG) manoeuvre have been described in the literature. However, the lack of a reliable signal processing technique to visualize and quantify these physiological changes has so far limited the applicability of the test to the clinical setting. The main aim of this study was to assess the feasibility of using wavelet analysis to quantify the pulse arrival time (PAT) and its changes during the DIG manoeuvre. Vascular responses were extracted from cardiac (electrocardiogram, ECG) and peripheral pulse (photoplethysmography, PPG) waveforms. Wavelet analysis characterized their cardiovascular responses in healthy adult subjects in the time-frequency space, and for the ECG-PPG inter-relationship in terms of the PAT. PAT showed a characteristic biphasic response to gasp, with an increase of 59 ± 59 ms (p = 0.001) compared to the maximum value reached during quiet breathing, and a decrease of -38 ± 55 ms (p < 0.01) compared to the minimum value during quiet breathing. The response measures were repeatable. This pilot study has successfully shown the feasibility of using wavelet analysis to characterize the cardiovascular waveforms and quantify their changes with DIG.
Improving resolution of gravity data with wavelet analysis and spectral method
Institute of Scientific and Technical Information of China (English)
QIU Ning; HE Zhanxiang; CHANG Yanjun
2007-01-01
Gravity data are the results of gravity force field interaction from all the underground sources. The objects of detection are always submerged in the background field, and thus one of the crucial problems for gravity data interpretation is how to improve the resolution of observed information.The wavelet transform operator has recently been introduced into the domain fields both as a filter and as a powerful source analysis tool. This paper studied the effects of improving resolution of gravity data with wavelet analysis and spectral method, and revealed the geometric characteristics of density heterogeneities described by simple shaped sources. First, the basic theory of the multiscale wavelet analysis and its lifting scheme and spectral method were introduced. With the exper-imental study on forward simulation of anomalies given by the superposition of six objects and measured data in Songliao plain, Northeast China, the shape, size and depth of the buried objects were estimated in the study. Also, the results were compared with those obtained by conventional techniques,which demonstrated that this method greatly improves the resolution of gravity anomalies.
Wavelet multiple correlation and cross-correlation: A multiscale analysis of Eurozone stock markets
Fernández-Macho, Javier
2012-02-01
Statistical studies that consider multiscale relationships among several variables use wavelet correlations and cross-correlations between pairs of variables. This procedure needs to calculate and compare a large number of wavelet statistics. The analysis can then be rather confusing and even frustrating since it may fail to indicate clearly the multiscale overall relationship that might exist among the variables. This paper presents two new statistical tools that help to determine the overall correlation for the whole multivariate set on a scale-by-scale basis. This is illustrated in the analysis of a multivariate set of daily Eurozone stock market returns during a recent period. Wavelet multiple correlation analysis reveals the existence of a nearly exact linear relationship for periods longer than the year, which can be interpreted as perfect integration of these Euro stock markets at the longest time scales. It also shows that small inconsistencies between Euro markets seem to be just short within-year discrepancies possibly due to the interaction of different agents with different trading horizons.
Mapping of normal fault scarps in airborne laser swath mapping data using wavelet analysis
Sare, R.; Hilley, G. E.
2015-12-01
Wavelet analysis of Digital Elevation Models (DEMs) successfully identifies degraded fault scarps where earthquakes produce topographic steps and provides an estimate of their morphologic age. However, these methods may fail to detect relatively young, sloping scarps created by more gently-dipping normal faults, misidentifying them as mature, highly-degraded vertical scarps if they are detected at all. We present new wavelet templates incorporating initial scarp slope and above- and below-scarp surface angles to better describe the curvature of observed fault scarps. These templates are based on an analytic solution for scarp curvature, allowing for more accurate estimation of the relative age of the scarp. Synthetic tests show that scarp-like landforms that went largely undetected by a vertical-scarp template are more clearly detected using profile geometries that reflect subtle changes in curvature due to scarp and far-field slope angles. Analysis of DEMs from sites in Surprise Valley in the northwestern Basin and Range and near Jenny Lake on the Teton rangefront illustrates the effects of along-strike variability in scarp morphology on best-fit template parameters. Where normal fault scarps have high slopes, they are identified by filters designed to detect topographic step functions. Scarps with finite initial slopes, as well as those that cut surfaces with different angles above and below the scarp, can be resolved with higher signal-to-noise ratios using more sophisticated template functions. Adaptive use of different wavelet templates could reduce the number of false negatives in wavelet analysis of data from complex faulting regimes, improving the robustness of these methods and enabling automated fault mapping of large areas.
A Stochastic Wavelet Finite Element Method for 1D and 2D Structures Analysis
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Xingwu Zhang
2014-01-01
Full Text Available A stochastic finite element method based on B-spline wavelet on the interval (BSWI-SFEM is presented for static analysis of 1D and 2D structures in this paper. Instead of conventional polynomial interpolation, the scaling functions of BSWI are employed to construct the displacement field. By means of virtual work principle and BSWI, the wavelet finite elements of beam, plate, and plane rigid frame are obtained. Combining the Monte Carlo method and the constructed BSWI elements together, the BSWI-SFEM is formulated. The constructed BSWI-SFEM can deal with the problems of structural response uncertainty caused by the variability of the material properties, static load amplitudes, and so on. Taking the widely used Timoshenko beam, the Mindlin plate, and the plane rigid frame as examples, numerical results have demonstrated that the proposed method can give a higher accuracy and a better constringency than the conventional stochastic finite element methods.
Wavelets, Curvelets and Multiresolution Analysis Techniques in Fast Z Pinch Research
Afeyan, Bedros; Starck, Jean Luc; Cuneo, Michael
2012-01-01
Z pinches produce an X ray rich plasma environment where backlighting imaging of imploding targets can be quite challenging to analyze. What is required is a detailed understanding of the implosion dynamics by studying snapshot images of its in flight deformations away from a spherical shell. We have used wavelets, curvelets and multiresolution analysis techniques to address some of these difficulties and to establish the Shell Thickness Averaged Radius (STAR) of maximum density, r*(N, {\\theta}), where N is the percentage of the shell thickness over which we average. The non-uniformities of r*(N, {\\theta}) are quantified by a Legendre polynomial decomposition in angle, {\\theta}, and the identification of its largest coefficients. Undecimated wavelet decompositions outperform decimated ones in denoising and both are surpassed by the curvelet transform. In each case, hard thresholding based on noise modeling is used.
Image analysis using a dual-tree M-band wavelet transform.
Chaux, Caroline; Duval, Laurent; Pesquet, Jean-Christophe
2006-08-01
We propose a two-dimensional generalization to the M-band case of the dual-tree decomposition structure (initially proposed by Kingsbury and further investigated by Selesnick) based on a Hilbert pair of wavelets. We particularly address: 1) the construction of the dual basis and 2) the resulting directional analysis. We also revisit the necessary pre-processing stage in the M-band case. While several reconstructions are possible because of the redundancy of the representation, we propose a new optimal signal reconstruction technique, which minimizes potential estimation errors. The effectiveness of the proposed M-band decomposition is demonstrated via denoising comparisons on several image types (natural, texture, seismics), with various M-band wavelets and thresholding strategies. Significant improvements in terms of both overall noise reduction and direction preservation are observed.
Ultrasonic C-scan Detection for Stainless Steel Spot Welding Based on Wavelet Package Analysis
Institute of Scientific and Technical Information of China (English)
LIU Jing; XU Guocheng; XU Desheng; ZHOU Guanghao; FAN Qiuyue
2015-01-01
An ultrasonic test of spot welding for stainless steel is conducted. Based on wavelet packet decomposition, the ultrasonic echo signal has been analyzed deeply in time - frequency domain, which can easily distinguish the nugget from the corona bond. The 2D C-scan images produced by ultrasonic C scan which contribute to quantitatively calculate the nugget diameter for the computer are further analyzed. The spot welding nugget diameter can be automatically obtained by image enhancement, edge detection and equivalent diameter algorithm procedure. The ultrasonic detection values in this paper show good agreement with the metallographic measured values. The mean value of normal distribution curve is 0.006 67, and the standard deviation is 0.087 11. Ultrasonic C-scan test based on wavelet packet signal analysis is of high accuracy and stability.
González Gómez, Dulce I.; Moreno Barbosa, E.; Martínez Hernández, Mario Iván; Ramos Méndez, José; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito
2014-11-01
The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (pADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.
An empirical analysis to study the cyclical trends on stock exchange using wavelet methods
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Shapour Mohammadi
2011-01-01
Full Text Available During the past few decades, there have been many evidences to believe that the stock markets around the world follow cyclical trends. In this paper, we study the cyclical trends using wavelet function based on various time windows on some major stock market indices. We use two methods of Daubechies and reverse bi-orthogonal wavelet methods and determine the optimal values of both methods. The results are used for Tehran stock exchange using the most recent ten years daily information as an empirical study. The details of our analysis on TEDPIX index for the last decade indicate that there are, at least, four trends of weekly, monthly, quarterly and yearly and the cycles would be expected to be repeated in future.
Characteristic analysis of underwater acoustic scattering echoes in the wavelet transform domain
Yang, Mei; Li, Xiukun; Yang, Yang; Meng, Xiangxia
2017-01-01
Underwater acoustic scattering echoes have time-space structures and are aliasing in time and frequency domains. Different series of echoes properties are not identified when incident angle is unknown. This article investigates variations in target echoes of monostatic sonar to address this problem. The mother wavelet with similar structures has been proposed on the basis of preprocessing signal waveform using matched filter, and the theoretical expressions between delay factor and incident angle are derived in the wavelet domain. Analysis of simulation data and experimental results in free-field pool show that this method can effectively separate geometrical scattering components of target echoes. The time delay estimation obtained from geometrical echoes at a single angle is consistent with target geometrical features, which provides a basis for object recognition without angle information. The findings provide valuable insights for analyzing elastic scattering echoes in actual ocean environment.
Energy Technology Data Exchange (ETDEWEB)
González Gómez Dulce, I., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Moreno Barbosa, E., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Hernández, Mario Iván Martínez, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Méndez, José Ramos, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. (Mexico); Silvia, Hidalgo Tobón [Hospital Infantil de México, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Hospital Infantil de México, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Fundación para el Desarrollo Carlos Sigüenza. Puebla, Pue (Mexico)
2014-11-07
The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.
Lecture notes on wavelet transforms
Debnath, Lokenath
2017-01-01
This book provides a systematic exposition of the basic ideas and results of wavelet analysis suitable for mathematicians, scientists, and engineers alike. The primary goal of this text is to show how different types of wavelets can be constructed, illustrate why they are such powerful tools in mathematical analysis, and demonstrate their use in applications. It also develops the required analytical knowledge and skills on the part of the reader, rather than focus on the importance of more abstract formulation with full mathematical rigor. These notes differs from many textbooks with similar titles in that a major emphasis is placed on the thorough development of the underlying theory before introducing applications and modern topics such as fractional Fourier transforms, windowed canonical transforms, fractional wavelet transforms, fast wavelet transforms, spline wavelets, Daubechies wavelets, harmonic wavelets and non-uniform wavelets. The selection, arrangement, and presentation of the material in these ...
Directory of Open Access Journals (Sweden)
Fenghua Tian
2016-01-01
Full Text Available Cerebral autoregulation represents the physiological mechanisms that keep brain perfusion relatively constant in the face of changes in blood pressure and thus plays an essential role in normal brain function. This study assessed cerebral autoregulation in nine newborns with moderate-to-severe hypoxic–ischemic encephalopathy (HIE. These neonates received hypothermic therapy during the first 72 h of life while mean arterial pressure (MAP and cerebral tissue oxygenation saturation (SctO2 were continuously recorded. Wavelet coherence analysis, which is a time-frequency domain approach, was used to characterize the dynamic relationship between spontaneous oscillations in MAP and SctO2. Wavelet-based metrics of phase, coherence and gain were derived for quantitative evaluation of cerebral autoregulation. We found cerebral autoregulation in neonates with HIE was time-scale-dependent in nature. Specifically, the spontaneous changes in MAP and SctO2 had in-phase coherence at time scales of less than 80 min (<0.0002 Hz in frequency, whereas they showed anti-phase coherence at time scales of around 2.5 h (~0.0001 Hz in frequency. Both the in-phase and anti-phase coherence appeared to be related to worse clinical outcomes. These findings suggest the potential clinical use of wavelet coherence analysis to assess dynamic cerebral autoregulation in neonatal HIE during hypothermia.
Wavelet-based neural network analysis of internal carotid arterial Doppler signals.
Ubeyli, Elif Derya; Güler, Inan
2006-06-01
In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.
Wavelet-based neural network analysis of ophthalmic artery Doppler signals.
Güler, Nihal Fatma; Ubeyli, Elif Derya
2004-10-01
In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.
A wavelet-based analysis of surface mechanomyographic signals from the quadriceps femoris.
Beck, Travis W; Housh, Terry J; Fry, Andrew C; Cramer, Joel T; Weir, Joseph P; Schilling, Brian K; Falvo, Michael J; Moore, Christopher A
2009-03-01
The purpose of this study was to use a wavelet analysis designed specifically for surface mechanomyographic (MMG) signals to examine the MMG responses of the vastus lateralis (VL), rectus femoris (RF), and vastus medialis (VM) muscles. Fifteen healthy men [age (mean +/- SD): 26.4 +/- 6.1 years] volunteered to perform isometric muscle actions of the dominant leg extensors at 20%, 40%, 60%, 80%, and 100% of the maximum voluntary contraction (MVC). During each muscle action, surface MMG signals were detected from the VL, RF, and VM and processed with the MMG wavelet analysis. The results show that, for the VL and VM muscles, there was compression of the total MMG intensity spectra toward low frequencies for most force levels above 20% MVC. For the RF, however, the peak of the total MMG intensity spectrum occurred at approximately 30-40 HZ for all force levels. Because the VL, RF, and VM are all innervated by the femoral nerve, the discrepancies among the three muscles for total MMG intensity in each wavelet band may have been due to differences in architecture, muscle stiffness, and/or intramuscular pressure.
Joshi, Nitin; Gupta, Divya; Suryavanshi, Shakti; Adamowski, Jan; Madramootoo, Chandra A.
2016-12-01
In this study, seasonal trends as well as dominant and significant periods of variability of drought variables were analyzed for 30 rainfall subdivisions in India over 141 years (1871-2012). Standardized precipitation index (SPI) was used as a meteorological drought indicator, and various drought variables (monsoon SPI, non-monsoon SPI, yearly SPI, annual drought duration, annual drought severity and annual drought peak) were analyzed. Discrete wavelet transform was used in conjunction with the Mann-Kendall test to analyze trends and dominant periodicities associated with the drought variables. Furthermore, continuous wavelet transform (CWT) based global wavelet spectrum was used to analyze significant periods of variability associated with the drought variables. From the trend analysis, we observed that over the second half of the 20th century, drought occurrences increased significantly in subdivisions of Northeast and Central India. In both short-term (2-8 years) and decadal (16-32 years) periodicities, the drought variables were found to influence the trend. However, CWT analysis indicated that the dominant periodic components were not significant for most of the geographical subdivisions. Although inter-annual and inter-decadal periodic components play an important role, they may not completely explain the variability associated with the drought variables across the country.
Export-led growth in Tunisia: A wavelet filtering based analysis
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Hamrita Mohamed Essaied
2013-10-01
Full Text Available In this paper, we use a wavelet filtering based approach to study the econometric relationship between exports, imports, and economic growth for Tunisia, using quarterly data for the period 1961:1-2007:4. GDP is used as a proxy for economic growth. We explore the interactions between these primary macroeconomic inputs in a co-integrating framework. We also study the direction of causality between the three variables, based on the more robust Toda-Yamamoto modified Wald (MWALD test. The much-studied relationship between these three primary indicators of the economy is explored with the help of the wavelet multi-resolution filtering technique. Instead of an analysis at the original series level, as is usually done, we first decompose the variables using wavelet decomposition technique at various scales of resolution and obtain relationship among components of the decomposed series matched to its scale. The analysis reveals interesting aspects of the interrelationship among the three fundamental macroeconomic variables.
ANN-based wavelet analysis for predicting electrical signal from photovoltaic power supply system
Energy Technology Data Exchange (ETDEWEB)
Mellit, A. [Medea Univ., Medea (Algeria). Inst. of Science Engineering, Dept. of Electronics
2007-07-01
This study was conducted to predict different electrical signals from a photovoltaic power supply system (PVPS) using an artificial neural networks (ANN) with wavelet analysis. It involved the creation of a database of electrical signals (PV-generator current, voltage, battery current voltage, regulator current and voltage) obtained from an experimental PVPS system installed in the south of Algeria. The potential applications were for sizing and analyzing the performance of PVPS systems; control of maximum power point tracker (MPPT) in order to deliver the maximum energy from the PV-array; prediction of the optimal configuration (PV-array and battery sizing) of PVPS systems; expert configuration of PV-systems; faults diagnosis; supervision; and, control and monitoring. First, based on the wavelet analysis each electrical signal was mapped in several time frequency domains. The PV-system was then divided into 3-subsystems corresponding to ANN-PV generator model, ANN-battery model, and ANN-regulator model. An example of day-by-day prediction for each electrical signal was presented. The results of the proposed approach were in good agreement with experimental results. In addition, the accuracy of the proposed approach was more satisfactory when only ANN was used. It was concluded that this methodology offers the possibility of developing a new expert configuration of PVPS by implementing the soft computing ANN-wavelet program with a digital signal processing (DSP) circuit. 26 refs., 1 tab., 5 figs.
WAVELET ANALYSIS OF COHERENT STRUCTURES IN A THREE-DIMENSIONAL MIXING LAYER
Institute of Scientific and Technical Information of China (English)
林建忠; 邵雪明; 倪利民
2002-01-01
Wavelet analysis is applied to the results obtained by the direct nu-merical simulation of a three-dimensional (3D) mixing layer in order to investigatecoherent structures in dimension of scale. First, 3D orthonormal wavelet bases areconstructed, and the corresponding decomposition algorithm is developed. Then theNavier-Stokes equations are transformed into the wavelet space and the architecturefor multi-scale analysis is established. From this architecture, the coarse field imagesin different scales are obtained and some local statistical quantities are calculated.The results show that, with the development of a mixing layer, the energy spectrumdensities for different wavenumbers increase and the energy is transferred from theaverage flow to vortex structures in different scales. Due to the non-linear interactionsbetween different scales, cascade processes of energy are very complex. Because vor-tices always roll and pair at special areas, for a definite scale, the energy is obtainedfrom other scales at some areas while it is transferred to other scales at other areas.In addition, energy dissipation and transfer always occur where an intense interactionbetween vortices exists.
Structural health monitoring of long-span suspension bridges using wavelet packet analysis
Institute of Scientific and Technical Information of China (English)
Ding Youliang; Li Aiqun
2007-01-01
During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage. This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform (WPT). The WPTbased method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum (WPES) has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices VD reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index VD is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.
Structural health monitoring of long-span suspension bridges using wavelet packet analysis
Ding, Youliang; Li, Aiqun
2007-09-01
During the service life of civil engineering structures such as long-span bridges, local damage at key positions may continually accumulate, and may finally result in their sudden failure. One core issue of global vibration-based health monitoring methods is to seek some damage indices that are sensitive to structural damage. This paper proposes an online structural health monitoring method for long-span suspension bridges using wavelet packet transform (WPT). The WPT-based method is based on the energy variations of structural ambient vibration responses decomposed using wavelet packet analysis. The main feature of this method is that the proposed wavelet packet energy spectrum (WPES) has the ability to detect structural damage from ambient vibration tests of a long-span suspension bridge. As an example application, the WPES-based health monitoring system is used on the Runyang Suspension Bridge under daily environmental conditions. The analysis reveals that changes in environmental temperature have a long-term influence on the WPES, while the effect of traffic loadings on the measured WPES of the bridge presents instantaneous changes because of the nonstationary properties of the loadings. The condition indication indices V D reflect the influences of environmental temperature on the dynamic properties of the Runyang Suspension Bridge. The field tests demonstrate that the proposed WPES-based condition indication index V D is a good candidate index for health monitoring of long-span suspension bridges under ambient excitations.
Directory of Open Access Journals (Sweden)
K. Jayakumar
2015-02-01
Full Text Available A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on industrial drives leads to higher current flow rate and the broken bearing detected system determined the number of unhealthy bearings but need to develop a faster system with constant frequency domain. Vibration data acquisition was used in our proposed work to detect broken bearing faults in induction machine. To generate an effective fault detection of industrial drives, Biorthogonal Posterior Vibration Signal-Data Probabilistic Wavelet Neural Network (BPPVS-WNN system was proposed in this paper. This system was focused to reducing the current flow and to identify faults with lesser execution time with harmonic values obtained through fifth derivative. Initially, the construction of Biorthogonal vibration signal-data based wavelet transform in BPPVS-WNN system localizes the time and frequency domain. The Biorthogonal wavelet approximates the broken bearing using double scaling and factor, identifies the transient disturbance due to fault on induction motor through approximate coefficients and detailed coefficient. Posterior Probabilistic Neural Network detects the final level of faults using the detailed coefficient till fifth derivative and the results obtained through it at a faster rate at constant frequency signal on the industrial drive. Experiment through the Simulink tool detects the healthy and unhealthy motor on measuring parametric factors such as fault detection rate based on time, current flow rate and execution time.
Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation
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Jérôme Boudy
2007-01-01
Full Text Available This work aims at providing new insights on the electrocardiogram (ECG segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.
Kulesh, M.; Holschneider, M.; Shardakov, I.
2005-12-01
The problem of the surface elastic wave propagation in the half-space within the framework of the Cosserat continuum has been considered. Medium deformation in this model is described not only by the displacement vector, but also by kinematically independent rotation vector. This model can be used for the description of the media with microstructure, for example concrete, sand, sandy-gravel mixture etc. At the same time the applications of these models almost do not exist in praxis, since there are no reliable data about the material properties in nonsymmetrical elasticity theory and in fact there are no experiments which can demonstrate the effects of couple-stress behavior in solid under deformation. The main result of presented work consist in fact, that within the framework of the Cosserat continuum in half-space besides elliptical Rayleigh wave can be in existence the surface shear wave with only transversal component. Geometrically such wave is equal to Love wave, but in classical elasticity theory existence of the Love wave as surface elastic wave is defined by presence of a layer on a half-space, and while a layer thickness vanishing the Love wave proceeds to a plane wave. Thus, in Cosserat medium the new wave mode is found out, and there is no analogue of it in classical elasticity theory. As a second result of presented work the method of the displacement seismogram inversion has been proposed. This method is based on continues wavelet transform and allows to restore the wave number, phase and group velocities. These results can be effectively used in possible experiments which are aimed at the detection of couple-stress effects in medium and further at the identification of material constants of nonsymmetrical elasticity theory. This work was supported by Russian Foundation of Fundamental Research under project 03-01-00561 and by the Deutsche Forschungsgemeinschaft (DFG) within the framework of the priority program SPP 1114, Mathematical methods for time
Institute of Scientific and Technical Information of China (English)
WANG Jian; GAO Jingxiang; XU Changhui
2006-01-01
Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.
Energy Technology Data Exchange (ETDEWEB)
Suda, Y.; Okumura, M.; Komine, H.; Iwasa, T.; Terumichi, Y. [The University of Tokyo, Tokyo (Japan). Institute of Industrial Science; Qian, B. [The University of Tokyo, Tokyo (Japan)
1998-11-01
This paper reports the development of a method that detects the corrugation of a rail. A measurement test in which the track inspection car provided with an axle box accelerometer and irregular rail top face measurement equipment runs over the range of a specific section at the setting rate was conducted. A wavelet analysis is applied to the obtained acceleration data of vertical axle box vibration. The position of the generated corrugation was detected in a time base as the high position of a wavelet coefficient. Moreover, the dominant frequency range of corrugation was detected for each frequency by adding the wavelet coefficients in the whole position. This result was verified using the measurement data of an irregular rail top face. The wave height of corrugation can be estimated from the amplitude of the vertical vibration acceleration measured during low-speed traveling when the calculation result of a frequency response using the vertical vibration model in simplified wheel and rail systems is compared with the test result described above. 2 refs., 8 figs., 1 tab.
Wavelet Analysis of Earthquake Activity in the West of the Chinese Mainland and Its Adjacent Area
Institute of Scientific and Technical Information of China (English)
Shao Huicheng; Fu Zhengxiang; Wang Xiaoqing; Jiang Zaisen
2005-01-01
Wavelets are a useful tool for analyzing the time-frequency of a non-stable series and are widely applied in many fields. The process of earthquake preparation and occurrence is a non-linear process. In the paper, the wavelet method is used to analyze the series of earthquake data for the time period from 1900 to 2003 in the west of the Chinese mainland and its adjacent area (WCMAA), and to obtain the characteristic information for different time scales. In the past 103 years, there were four primary periods of regional earthquake activity in the area with durations of 42, 22, 7 and 14 years, respectively and the intensity of earthquake activity changing with time. It doesn't make sense to talk about active or quiet periods of earthquake activity unless it is based on a specific time scale. In addition, the tendency analysis of earthquake activity using the primary period of seismic activity and wavelet coefficients of varied time scales indicates that the earthquake activity in this region will be high in the forthcoming years.
Wavelet-analysis of gastric microcirculation in rats with ulcer bleedings
Pavlov, A. N.; Semyachkina-Glushkovskaya, O. V.; Pavlova, O. N.; Bibikova, O. A.; Kurths, J.
2013-10-01
Nitric oxide (NO) plays an important role in regulation of central and peripheral circulation in normal state and during hemorrhagic stress. Because the impaired gastric mucosal blood flow is the major cause of gastroduodenal lesions including ulcer bleeding (UB), we study in this work the NO-ergic mechanism responsible for regulation of this blood flow. Our study is performed in rats with a model of stress-induced UB using laser Doppler flowmetry (LDF) that characterizes the rate of blood flow by measuring a Doppler shift of the laser beam scattered by the moving red blood cells. Numerical analysis of LDF-data is based on the discrete wavelet-transform (DWT) using Daubechies wavelets aiming to quantify influences of NO on the gastric microcirculation. We show that the stress-induced UB is associated with an increased level of NO in the gastric tissue and a stronger vascular sensitivity to pharmacological modulation of NO-production by L-NAME. We demonstrate that wavelet-based analyses of NO-dependent regulation of gastric microcirculation can provide an effective endoscopic diagnostics of a risk of UB.
Saini, Shiwani; Dewan, Lillie
2016-01-01
This paper highlights the potential of discrete wavelet transforms in the analysis and comparison of genomic sequences of Mycobacterium tuberculosis (MTB) with different resistance characteristics. Graphical representations of wavelet coefficients and statistical estimates of their parameters have been used to determine the extent of similarity between different sequences of MTB without the use of conventional methods such as Basic Local Alignment Search Tool. Based on the calculation of the energy of wavelet decomposition coefficients of complete genomic sequences, their broad classification of the type of resistance can be done. All the given genomic sequences can be grouped into two broad categories wherein the drug resistant and drug susceptible sequences form one group while the multidrug resistant and extensive drug resistant sequences form the other group. This method of segregation of the sequences is faster than conventional laboratory methods which require 3-4 weeks of culture of sputum samples. Thus the proposed method can be used as a tool to enhance clinical diagnostic investigations in near real-time.
Campo, D.; Quintero, O. L.; Bastidas, M.
2016-04-01
We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform - was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.
Directory of Open Access Journals (Sweden)
Asem Khmag
2014-07-01
Full Text Available This study proposes novel image denoising algorithm using combination method. This method combines both Wavelet Based Denoising (WBD and Principle Component Analysis (PCA to increase the superiority of the observed image, subjectively and objectively. We exploit the important property of second generation WBD and PCA to increase the performance of our designed filter. One of the main advantages of the second generation wavelet transformation in noise reduction is its ability to keep the signal energy in small amount of coefficients in the wavelet domain. On the other hand, one of the main features of PCA is that the energy of the signal concentrates on a very few subclasses in PCA domain, while the noise’s energy equally spreads over the entire signal; this characteristic helps us to isolate the noise perfectly. Our algorithm compares favorably against several state-of-the-art filtering systems algorithms, such as Contourlet soft thresholding, Scale mixture by WT, Sparse 3D transformation and Normal shrink. In addition, the combined algorithm achieves very competitive performance compared with the traditional algorithms, especially when it comes to investigating the problem of how to preserve the fine structure of the tested image and in terms of the computational complexity reduction as well.
Analyzing Crude Oil Spot Price Dynamics versus Long Term Future Prices: A Wavelet Analysis Approach
Directory of Open Access Journals (Sweden)
Josué M. Polanco-Martínez
2016-12-01
Full Text Available The West Texas Intermediate (WTI spot price shows high volatility and in 2014 and 2015 when quoted prices declined sharply, long-term prices in future markets were less volatile. These prices are different and diverge depending on how they process fundamental and transitory factors. US tight oil production has been a major innovation with significant macroeconomic effects. In this paper we use WTI spot prices and long-term futures prices, the latter calculated as the expected value with a stochastic model calibrated with the futures quotes of each sample day. These long-term prices are the long-term equilibrium value under risk neutral measurement. In order to analyze potential time-scale relationships between spots and future, we perform a wavelet cross-correlation analysis using a novel wavelet graphical tool recently proposed. To check the direction of the causality, we apply non-linear causality tests to raw data and log returns as well as to the wavelet transform of the spot and futures prices. Our results show that in the spot and futures markets for the period 24 February 2006–2 April 2016 there is a bi-directional causality effect for most time scales (from intra-week to biannual. This suggests that spot and futures prices react simultaneously to new information.
Ali, Abebe Mohammed; Skidmore, Andrew K.; Darvishzadeh, Roshanak; van Duren, Iris; Holzwarth, Stefanie; Mueller, Joerg
2016-12-01
Quantification of vegetation properties plays an important role in the assessment of ecosystem functions with leaf dry mater content (LDMC) and specific leaf area (SLA) being two key functional traits. For the first time, these two leaf traits have been estimated from the airborne images (HySpex) using the INFORM radiative transfer model and Continuous Wavelet Analysis (CWA). Ground truth data, were collected for 33 sample plots during a field campaign in July 2013 in the Bavarian Forest National Park, Germany, concurrent with the hyperspectral overflight. The INFORM model was used to simulate the canopy reflectance of the test site and the simulated spectra were transformed to wavelet features by applying CWA. Next, the top 1% strongly correlated wavelet features with the LDMC and SLA were used to develop predictive (regression) models. The two leaf traits were then retrieved using the CWA transformed HySpex imagery and the predictive models. The results were validated using R2 and the RMSE of the estimated and measured variables. Our results revealed strong correlations between six wavelet features and LDMC, as well as between four wavelet features and SLA. The wavelet features at 1741 nm (scale 5) and 2281 nm (scale 4) were the two most strongly correlated with LDMC and SLA respectively. The combination of all the identified wavelet features for LDMC yielded the most accurate prediction (R2 = 0.59 and RMSE = 4.39%). However, for SLA the most accurate prediction was obtained from the single most correlated feature: 2281 nm, scale 4 (R2 = 0.85 and RMSE = 4.90). Our results demonstrate the applicability of Continuous Wavelet Analysis (CWA) when inverting radiative transfer models, for accurate mapping of forest leaf functional traits.
Energy Technology Data Exchange (ETDEWEB)
Pysmenetska, Inna
2009-07-22
The present thesis consists of two parts. In the first part a novel experimental method for the measurement of the proton root-mean-square radius at the S-DALINAC is presented. A setup based on semiconductor detectors is realized. In contrast to previous experiments it allows a simultaneous measurement of the momentum transfer dependence of the elastic electron scattering cross section. A possible suppression of the significant electron and bremsstrahlung background observed in a test experiment was investigated with the help of different methods, such as {delta}E-E telescopes, the time of flight method with a pulsed beam and pulse shape discrimination. The combination of these methods allows a reduction of the background at all scattering angles, which should allow a successful measurement. The response of the detector system was studied with the help of Monte-Carlo simulations with an emphasis on the dependence of the expected accuracy of different parameters. The second part of this work describes an investigation of the fine structure of giant resonances in {sup 28}Si, {sup 48}Ca and {sup 166}Er with the help of a wavelet analysis. The discrete wavelet transform was used for a background determination in spectra of the iso vector E1 and the M2 giant resonances in {sup 48}Ca. This allows the extraction of 1{sup -} und 2{sup -} level densities in the excitation energy region of the respective resonances with the help of a fluctuation analysis. A fluctuation analysis of the fine structure of the isoscalar E2 resonance in {sup 166}Er allows the extraction of the coherent widths of the 2{sup +} states. In the excitation energy region E{sub x}=10-16 MeV widths between 30 and 80 eV are found. The fine structure of the giant resonances is furthermore specified by characteristic scales. In this thesis scales in {sup 28}Si and {sup 48}Ca are extracted with the help of the above mentioned wavelet transform. In {sup 28}Si the isovector E1 and isoscalar E2 resonances were
Wavelet decomposition based principal component analysis for face recognition using MATLAB
Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish
2016-03-01
For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.
Soni, Jalpa; Ghosh, Sayantan; Pradhan, Asima; Sengupta, Tapas K; Panigrahi, Prasanta K; Ghosh, Nirmalya
2011-01-01
The refractive index fluctuations in the connective tissue layer (stroma) of human cervical tissues having different grades of precancers (dysplasia) was quantified using a wavelet-based multifractal detrended fluctuation analysis model. The results show clear signature of multi-scale self-similarity in the index fluctuations of the tissues. Importantly, the refractive index fluctuations were found to be more anti-correlated at higher grades of precancers. Moreover, the strength of multifractality was also observed to be considerably weaker in higher grades of precancers. These results were further complemented by Fourier domain analysis of the spectral fluctuations.
Parallel Factor Analysis as an exploratory tool for wavelet transformed event-related EEG
DEFF Research Database (Denmark)
Mørup, Morten; Hansen, Lars Kai; Hermann, Cristoph S.
2006-01-01
to extract the expected features of a previously reported ERP paradigm: namely, a quantitative difference of coherent occipital gamma activity between conditions of a visual paradigm. Furthermore, the method revealed a qualitative difference which has not previously been reported. The PARAFAC decomposition...... of the 3-way array of ANOVA F test values clearly showed the difference of regions of interest across modalities, while the 5-way analysis enabled visualization of both quantitative and qualitative differences. Consequently, PARAFAC is a promising data exploratory tool in the analysis of the wavelets...
Advantages Of A Time Series Analysis Using Wavelet Transform As Compared With A Fourier Analysis
Directory of Open Access Journals (Sweden)
Sleziak Patrik
2015-06-01
Full Text Available The paper presents an analysis of changes in the structure of the average annual discharges, average annual air temperature, and average annual precipitation time series in Slovakia. Three time series with lengths of observation from 1961 to 2006 were analyzed. An introduction to spectral analysis with Fourier analysis (FA is given. This method is used to determine significant periods of a time series. Later in this article a description of a wavelet transform (WT is reviewed. This method is able to work with non-stationary time series and detect when significant periods are presented. Subsequently, models for the detection of potential changes in the structure of the time series analyzed were created with the aim of capturing changes in the cyclical components and the multiannual variability of the time series selected for Slovakia. Finally, some of the comparisons of the time series analyzed are discussed. The aim of the paper is to show the advantages of time series analysis using WT compared with FT. The results were processed in the R software environment.
Wavelet Transform Signal Processing Applied to Ultrasonics.
1995-05-01
THE WAVELET TRANSFORM IS APPLIED TO THE ANALYSIS OF ULTRASONIC WAVES FOR IMPROVED SIGNAL DETECTION AND ANALYSIS OF THE SIGNALS. In instances where...the mother wavelet is well defined, the wavelet transform has relative insensitivity to noise and does not need windowing. Peak detection of...ultrasonic pulses using the wavelet transform is described and results show good detection even when large white noise was added. The use of the wavelet
Four-Point Wavelets and Their Applications
Institute of Scientific and Technical Information of China (English)
魏国富; 陈发来
2002-01-01
Multiresolution analysis (MRA) and wavelets provide useful and efficient tools for representing functions at multiple levels of details. Wavelet representations have been used in a broad range of applications, including image compression, physical simulation and numerical analysis. In this paper, the authors construct a new class of wavelets, called four-point wavelets,based on an interpolatory four-point subdivision scheme. They are of local support, symmetric and stable. The analysis and synthesis algorithms have linear time complexity. Depending on different weight parameters w, the scaling functions and wavelets generated by the four-point subdivision scheme are of different degrees of smoothness. Therefore the user can select better wavelets relevant to the practice among the classes of wavelets. The authors apply the fourpoint wavelets in signal compression. The results show that the four-point wavelets behave much better than B-spline wavelets in many situations.
Joint Time-Frequency And Wavelet Analysis - An Introduction
Majkowski Andrzej; Kołodziej Marcin; Rak Remigiusz J.
2014-01-01
A traditional frequency analysis is not appropriate for observation of properties of non-stationary signals. This stems from the fact that the time resolution is not defined in the Fourier spectrum. Thus, there is a need for methods implementing joint time-frequency analysis (t/f) algorithms. Practical aspects of some representative methods of time-frequency analysis, including Short Time Fourier Transform, Gabor Transform, Wigner-Ville Transform and Cone-Shaped Transform are described in thi...
Application of Wavelet Analysis on FOG North Seeking%小波分析在光纤陀螺寻北中的应用
Institute of Scientific and Technical Information of China (English)
衣昌明; 庞湘萍; 钱坤
2012-01-01
FOG measurement data from experiments were analyzed in this paper. To reduce the impact of noises on the FOG north-seeking and improve the precision of north seeking. Filter was carried on using three smoothness every five points,digital filter and the wavelet analysis method to the FOG static output, through contrasting around the FOG output curve, the standard deviation as well as spectrum graph, it can be concluded that wavelet threshold filter compensates effectively the FOG random error. The trend of low frequency signal in FOG can be shown by the wavelet analysis. Using de-noised data, two-position north seeking was computed. The results of experiments show that wavelet analysis can increase the precision hv ？.9 8%对某型光纤陀螺仪的实测信号进行了分析。为了降低噪声对光纤陀螺寻北的影响，提高寻北精度，利用五点三次平滑、数字滤波、小波变换分析法对光纤陀螺静态输出数据进行了滤波处理，通过对比滤波前后陀螺的输出曲线、标准差以及频谱图，可以发现：小波阈值滤波能够有效的补偿光纤陀螺的随机误差。通过小波分析可以显示出陀螺低频信号的发展趋势。利用去噪后的数据进行二位置寻北计算，结果表明：小波分析可以使精度提高29．8％。
Lemaster, Richard L
2010-01-01
The research described in this study is part of a project to provide the technology and theory to quantify surface quality for a variety of wood and wood-based products. The ultimate goal is to provide a means of monitoring trends in surface quality, which can be used to discriminate between "good" products and "bad" products (the methods described in this research are not intended to provide "grading" of individual workpieces) as well as to provide information to the machine operator as to the source of poor-quality machined surfaces. This research investigates the use of both frequency domain analysis as well as the more advanced joint time frequency analysis (JTFA). The disadvantages of traditional frequency analysis as well as the potential of the JTFA are illustrated. Sample surface profiles from actual machining defects were analyzed using traditional frequency analysis. A surface with multiple machining defects was analyzed with both traditional frequency analysis and JTFA (harmonic wavelet). Although the analysis was empirical in nature, the results show that the harmonic wavelet transform is able to detect both stationary and non-stationary surface irregularities as well as pulses (localized defects).
Construction of compactly supported biorthogonal wavelet based on Human Visual System
Hu, Haiping; Hou, Weidong; Liu, Hong; Mo, Yu L.
2000-11-01
As an important analysis tool, wavelet transform has made a great development in image compression coding, since Daubechies constructed a kind of compact support orthogonal wavelet and Mallat presented a fast pyramid algorithm for wavelet decomposition and reconstruction. In order to raise the compression ratio and improve the visual quality of reconstruction, it becomes very important to find a wavelet basis that fits the human visual system (HVS). Marr wavelet, as it is known, is a kind of wavelet, so it is not suitable for implementation of image compression coding. In this paper, a new method is provided to construct a kind of compactly supported biorthogonal wavelet based on human visual system, we employ the genetic algorithm to construct compactly supported biorthogonal wavelet that can approximate the modulation transform function for HVS. The novel constructed wavelet is applied to image compression coding in our experiments. The experimental results indicate that the visual quality of reconstruction with the new kind of wavelet is equivalent to other compactly biorthogonal wavelets in the condition of the same bit rate. It has good performance of reconstruction, especially used in texture image compression coding.
Du, Kongchang; Zhao, Ying; Lei, Jiaqiang
2017-09-01
In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt 'future' values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of 'future' values. These hybrid models caused incorrect 'high' prediction performance and may cause large errors in practice.
Institute of Scientific and Technical Information of China (English)
2007-01-01
An energy distribution theory was presented based on regular evolvement of energy fraction of acous-tic signals with fluidization velocity. Wavelet packet analysis was used in processing the acoustic sig-nals originated from particle impact on the wall of a fluidized bed. A new criterion of judging incipient fluidization(Umf) velocity and minimum turbulent velocity(Umt) was proposed according to the energy distribution theory. Experiments were performed with five groups of high density polyethylene(PE) particles and one bimodal PE to acquire incipient fluidization velocity and minimum turbulent velocity by using the criterion. The feasibility of this method in obtaining characteristic fluidization parameters was further verified by comparing it to results from the pressure drop method and the empirical value from industry.
Institute of Scientific and Technical Information of China (English)
WANG JingDai; YANG YongRong; GE PengFei; SHU WeiJie; HOU LinXi
2007-01-01
An energy distribution theory was presented based on regular evolvement of energy fraction of acoustic signals with fluidization velocity. Wavelet packet analysis was used in processing the acoustic signals originated from particle impact on the wall of a fluidized bed. A new criterion of judging incipient fluidization (Umf) velocity and minimum turbulent velocity (Umt) was proposed according to the energy distribution theory. Experiments were performed with five groups of high density polyethylene (PE)particles and one binodal PE to acquire incipient fluidization velocity and minimum turbulent velocity by using the criterion. The feasibility of this method in obtaining characteristic fluidization parameters was further verified by comparing it to results from the pressure drop method and the empirical value from industry.
The application of modeling and prediction with MRA wavelet network
Institute of Scientific and Technical Information of China (English)
LU Shu-ping; YANG Xue-jing; ZHAO Xi-ren
2004-01-01
As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was carried out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model.The research indicates that it is feasible to use the MRA wavelet network in the short -time prediction of ship motion.
Institute of Scientific and Technical Information of China (English)
李建平; 唐远炎; 严中洪; 张万萍
2001-01-01
Based on sine and cosine functions, the compactly supported orthogonal wavelet filter coefficients with arbitrary length are constructed for the first time. When/N = 2k- 1 and N = 2k , the unified analytic constructions of orthogonal wavelet filters are put forward,respectively. The famous Daubechies filter and some other well-known wavelet filters are tested by the proposed novel method which is very useful for wavelet theory research and many application areas such as pattern recognition.
Signal Estimation Using Wavelet Analysis of Solution Monitoring Data for Nuclear Safeguards
Directory of Open Access Journals (Sweden)
Tom Burr
2013-03-01
Full Text Available Wavelets are explored as a data smoothing (or de-noising option for solution monitoring data in nuclear safeguards. In wavelet-smoothed data, the Gibbs phenomenon can obscure important data features that may be of interest. This paper compares wavelet smoothing to piecewise linear smoothing and local kernel smoothing, and illustrates that the Haar wavelet basis is effective for reducing the Gibbs phenomenon.
A novel wavelet method for electric signals analysis in underwater arc welding
Institute of Scientific and Technical Information of China (English)
Zhang Weimin; Wang Guorong; Shi Yonghua; Zhong Biliang
2009-01-01
Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet (MMW) method. A novel threshold algorithm, which compromises the hard-threshold wavelet (HTW) and soft-threshold wavelet (STW) methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.
Non-Stationary Dynamics Data Analysis with Wavelet-Svd Filtering
Brenner, M. J.
2003-07-01
Non-stationary time-frequency analysis is used for identification and classification of aeroelastic and aeroservoelastic dynamics. Time-frequency multiscale wavelet processing generates discrete energy density distributions. The distributions are processed using the singular-value decomposition (SVD). Discrete density functions derived from the SVD generate moments that detect the principal features in the data. The SVD standard basis vectors are applied and then compared with a transformed-SVD, or TSVD, which reduces the number of features into more compact energy density concentrations. Finally, from the feature extraction, wavelet-based modal parameter estimation is applied. The primary objective is the automation of time-frequency analysis with modal system identification. The contribution is a more general approach in which distinct analysis tools are merged into a unified procedure for linear and non-linear data analysis. This method is first applied to aeroelastic pitch-plunge wing section models. Instability is detected in the linear system, and non-linear dynamics are observed from the time-frequency map and parameter estimates of the non-linear system. Aeroelastic and aeroservoelastic flight data from the drone for aerodynamic and structural testing and F18 aircraft are also investigated and comparisons made between the SVD and TSVD results. Input-output data are used to show that this process is an efficient and reliable tool for automated on-line analysis. Published by Elsevier Science Ltd.
Application of Wavelet Packet De-noising in Time-Frequency Analysis of the Local Wave Method
Institute of Scientific and Technical Information of China (English)
LI Hong-kun; MA Xiao-jiang; WANG Zhen; ZHU Hong
2003-01-01
The local wave method is a very good time-frequency method for nonstationary vibration signal analysis. But the interfering noise has a big influence on the accuracy of time-frequency analysis. The wavelet packet de-noising method can eliminate the interference of noise and improve the signal-noise-ratio. This paper uses the local wave method to decompose the de-noising signal and perform a time-frequency analysis. We can get better characteristics. Finally, an example of wavelet packet de-noising and a local wave time-frequency spectrum application of diesel engine surface vibration signal is put forward.
Prediction of nonregular secondary structures of proteins based on wavelet analysis
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The secondary structures of proteins fall into two classes: regular structure and nonregular structure. Helices and sheets are termed "regular" structures because their residues have repeating main-chain torsion angles, and their backbone N-H and C-O groups are arranged in a periodic pattern of hydrogen bonding. In contrast, the remaining structures with nonrepeating backbone torsion angles are called nonregular secondary structures. In this note, we performed an extensive sequence analysis of nonregular secondary structures and showed that these nonregular parts could be effectively predicted by continuous wavelet transform.
THREE-DIMENSIONAL ANALYSIS OF FUNCTIONALLY GRADED PLATE BASED ON THE HAAR WAVELET METHOD
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A three-dimensional analysis of a simply-supported functionally graded rectangular plate with an arbitrary distribution of material properties is made using a simple and effective method based on the Haar wavelet. With good features in treating singularities, Haar series solution converges rapidly for arbitrary distributions, especially for the case where the material properties change rapidly in some regions. Through numerical examples the influences of the ratio of material constants on the top and bottom surfaces and different material gradient distributions on the structural response of the plate to mechanical stimuli are studied.
Fourier and Wavelet Transform Analysis of Pressure Signals during Explosive Boiling
Institute of Scientific and Technical Information of China (English)
YIN Tie-Nan; HUAI Xiu-Lan
2008-01-01
@@ The transient pressure in a liquid-pool during explosive boiling of acetone is measured by a micro-pressure-measuring system.The Fast Fourier transform and continuous wavelet transform methods are applied to investigate the frequency characteristics.The results show that the dominant frequency of the explosive boiling is 0-2MHz,and the bubble cluster formed by numerous tiny bubbles departs twice.Analysis and discussions are also conducted to explain the bubble evolution during the explosive boiling.
Discrete Fourier analysis and wavelets applications to signal and image processing
Broughton, S Allen
2008-01-01
A thorough guide to the classical and contemporary mathematical methods of modern signal and image processing. Discrete Fourier Analysis and Wavelets presents a thorough introduction to the mathematical foundations of signal and image processing. Key concepts and applications are addressed in a thought-provoking manner and are implemented using vector, matrix, and linear algebra methods. With a balanced focus on mathematical theory and computational techniques, this self-contained book equips readers with the essential knowledge needed to transition smoothly from mathematical models to practic
Energy Technology Data Exchange (ETDEWEB)
Ullah, Saleem, E-mail: ullah19488@itc.nl [Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Skidmore, Andrew K. [Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Naeem, Mohammad [Department of Chemistry, Abdul Wali Khan University Mardan (AWKUM), KPK (Pakistan); Schlerf, Martin [Centre de Recherche Public-Gabriel Lippmann (CRPGL), L-4422 Belvaux (Luxembourg)
2012-10-15
Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 {mu}m) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R{sup 2} = 0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R{sup 2} = 0.88, RMSE = 8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation. -- Highlights: Black-Right-Pointing-Pointer The mid and thermal infrared spectra are sensitive to variation in leaf water content. Black-Right-Pointing-Pointer Continuous wavelet analysis detected the variation caused by leaf water content. Black-Right-Pointing-Pointer The selected wavelet features are highly correlated with leaf water content. Black-Right-Pointing-Pointer Mid wave and thermal infrared spectra have the potential to estimate leaf water content.
Fourier and wavelet spectral analysis of EMG signals in supramaximal constant load dynamic exercise.
Camata, Thiago V; Dantas, Jose L; Abrao, Taufik; Brunetto, Maria A C; Moraes, Antonio C; Altimari, Leandro R
2010-01-01
Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in supramaximal constant load dynamic exercise (110% VO(2peak)). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in supramaximal constant load dynamic exercise (P>0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.
Fourier and wavelet spectral analysis of EMG signals in maximal constant load dynamic exercise.
Costa, Marcelo V; Pereira, Lucas A; Oliveira, Ricardo S; Pedro, Rafael E; Camata, Thiago V; Abrao, Taufik; Brunetto, Maria A C; Altimari, Leandro R
2010-01-01
Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in maximal constant load dynamic exercise (100% W(max)). The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in maximal constant load dynamic exercise (P>0.05). However, the results of the variance was significantly lower for at least one of the muscles studied in CWT compared to STFT (P〈0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.
Identifying the impact of climate and anthropic pressures on karst aquifers using wavelet analysis
Charlier, Jean-Baptiste; Ladouche, Bernard; Maréchal, Jean-Christophe
2015-04-01
This paper assesses the implications of climate and anthropic pressures on short to long-term changes in water resources in a Mediterranean karst using wavelet analysis. This approach was tested on 38-year (1974-2011) hydrogeological time series recorded at the Lez spring (South France), which is exploited for water supply. Firstly, we investigated inter-relationships in the frequency domain by cross-correlation across multiresolution levels. Our results showed that rainfall and spring discharge are highly correlated in the high frequency domain which reflects the hydrogeological response during flood events of typical highly karstified systems. Pumping and groundwater level are correlated in a lower frequency domain, illustrating seasonal to multi-year relationships. Secondly, continuous wavelet transform was applied to characterize the temporal variability of the inter-relationships involved. On the contrary to examples of "non-managed" karst aquifers in the literature, our results showed that the 10-year rainfall component was attenuate in the discharge signal. We assume that the reason is that the storage variations are strongly affected by pumping. This interesting result shows that possible long-term impacts of rainfall variability due to climate change may be masked by a high pumping rate. We showed also that despite an increase of the pumping rate from the 1980s, the stress on the groundwater resource does not increase from year to year. The present pumping strategy does not affect the drawdown in the long term, avoiding an over-exploitation of the aquifer. Finally, this study highlights the effectiveness of wavelet analysis in characterizing the response variability of karst systems where the hydrogeological regime is modified by pumping.
Blind component separation in wavelet space. Application to CMB analysis
Delabrouille, J.; J. -L. Starck; J.-F. Cardoso; Moudden, Y.
2004-01-01
It is a recurrent issue in astronomical data analysis that observations are unevenly sampled or incomplete maps with missing patches or intentionaly masked parts. In addition, many astrophysical emissions are non stationary processes over the sky. Hence spectral estimation using standard Fourier transforms is no longer reliable. Spectral matching ICA (SMICA) is a source separation method based on covariance matching in Fourier space which is successfully used for the separation of diffuse ast...
Spatiotemporal variation of the ozone QBO in MLS data by wavelet analysis
Directory of Open Access Journals (Sweden)
S. Fadnavis
2008-11-01
Full Text Available Spatiotemporal characteristics of the ozone quasi-biennial oscillation (QBO over the tropical-subtropical stratosphere (40° S–40° N have been examined by analyzing data from the Microwave Limb Sounder (MLS aboard Upper Atmospheric Research Satellite (UARS for the period 1992–1999. A combination of regression analysis and wavelet analysis combines to act as an accurate QBO filter. Wavelet analysis provides inter-annual variability of amplitude and phase of the ozone QBO in the vertical structure of tropical-subtropical stratosphere. It gives minute details of phase propagation and descend rates, which can be used as input to models. Latitude-height structure shows evidence of a secondary meridional circulation induced by the QBO as double peak structure at the equator with maximum amplitude at two pressure levels 30 hPa and 9 hPa and a node at 14 hPa. The equatorial maxima are out of phase with each other. The maximum amplitude (~1.4 ppmv of the ozone QBO was observed near the equator at 10 hPa. Descent rate of the easterly phase is greater than westerly. The lag correlation of the ozone QBO with circulation and variation of descent rates in the vertical structure of the stratosphere are examined in detail. In the equatorial upper stratosphere ozone anomalies descent with the rate ~1.5 km/month but in tropics and subtropics (above 2 hPa they propagate upward.
Institute of Scientific and Technical Information of China (English)
曹玲燕; 胡铟; 贺枫
2014-01-01
Considering instability and incomplete deduction of the chromatographic baseline of transformer oil on-line monitoring system,a wavelet packet analysis-based baseline chromatographic signal reprocessing meth-od was proposed,which having wavelet packet used to implement multi-resolution decomposition and to deduct both low and high-frequency disturbance signals so as to obtain a chromatographic signal needed.The experi-mental results show that compared to the traditional wavelet transform,the wavelet packet analysis can do bet-ter in signal processing,reducing baseline interference and improving analysis precision and model stability.%针对变压器油色谱在线监测系统在现场使用中存在的色谱基线不稳定、扣除不完全等问题，提出了基于小波包分析的色谱信号预处理。该方法通过小波包对信号进行多分辨率分解，扣除部分干扰高、低频信号，得到所需要的色谱信号。实验结果表明：将小波包应用到色谱信号数据预处理中，相对于传统小波变换能更细致地进行信号分析，较好地进行色谱基线的扣除，减少基线对色谱信号的干扰，从而有效提高油色谱在线监测系统的分析精度和模型稳定性。
Directory of Open Access Journals (Sweden)
A. Sreenivasa Murthy
2014-11-01
Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.
Institute of Scientific and Technical Information of China (English)
Kun Tan; Peijun Du
2011-01-01
@@ Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.%Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four
Liu, Yao; Zhu, Xiaoqin; Huang, Zufang; Cai, Jianyong; Chen, Rong; Xiong, Shuyuan; Chen, Guannan; Zeng, Haishan
2015-01-01
Quantitative methods for noninvasive diagnosis of scars are a challenging issue in medicine. This work aims to implement a texture analysis method for quantitatively discriminating abnormal scars from normal scars based on second-harmonic generation (SHG) images. A local difference local binary pattern (LD-LBP) operator combined with a wavelet transform was explored to extract diagnosis features from scar SHG images that were related to the alteration in collagen morphology. Based on the quantitative parameters including the homogeneity, directional and coarse features in SHG images, the scar collagen SHG images were classified into normal or abnormal scars by a support vector machine classifier in a leave-one-out cross-validation procedure. Our experiments and data analyses demonstrated apparent differences between normal and abnormal scars in terms of their morphological structure of collagen. By comparing with gray level co-occurrence matrix, wavelet transform, and combined basic local binary pattern and wavelet transform with respect to the accuracy and receiver operating characteristic analysis, the method proposed herein was demonstrated to achieve higher accuracy and more reliable classification of SHG images. This result indicated that the extracted texture features with the proposed method were effective in the classification of scars. It could provide assistance for physicians in the diagnostic process.
The Analysis of Curved Beam Using B-Spline Wavelet on Interval Finite Element Method
Directory of Open Access Journals (Sweden)
Zhibo Yang
2014-01-01
Full Text Available A B-spline wavelet on interval (BSWI finite element is developed for curved beams, and the static and free vibration behaviors of curved beam (arch are investigated in this paper. Instead of the traditional polynomial interpolation, scaling functions at a certain scale have been adopted to form the shape functions and construct wavelet-based elements. Different from the process of the direct wavelet addition in the other wavelet numerical methods, the element displacement field represented by the coefficients of wavelets expansions is transformed from wavelet space to physical space by aid of the corresponding transformation matrix. Furthermore, compared with the commonly used Daubechies wavelet, BSWI has explicit expressions and excellent approximation properties, which guarantee satisfactory results. Numerical examples are performed to demonstrate the accuracy and efficiency with respect to previously published formulations for curved beams.
Multiresolution mesh segmentation based on surface roughness and wavelet analysis
Roudet, Céline; Dupont, Florent; Baskurt, Atilla
2007-01-01
During the last decades, the three-dimensional objects have begun to compete with traditional multimedia (images, sounds and videos) and have been used by more and more applications. The common model used to represent them is a surfacic mesh due to its intrinsic simplicity and efficacity. In this paper, we present a new algorithm for the segmentation of semi-regular triangle meshes, via multiresolution analysis. Our method uses several measures which reflect the roughness of the surface for all meshes resulting from the decomposition of the initial model into different fine-to-coarse multiresolution meshes. The geometric data decomposition is based on the lifting scheme. Using that formulation, we have compared various interpolant prediction operators, associated or not with an update step. For each resolution level, the resulting approximation mesh is then partitioned into classes having almost constant roughness thanks to a clustering algorithm. Resulting classes gather regions having the same visual appearance in term of roughness. The last step consists in decomposing the mesh into connex groups of triangles using region growing ang merging algorithms. These connex surface patches are of particular interest for adaptive mesh compression, visualisation, indexation or watermarking.
Institute of Scientific and Technical Information of China (English)
Xiao Gang; Wang Shu
2006-01-01
IHS (Intensity, Hue and Saturation) transform is one of the most commonly used fusion algorithm. But the matching error causes spectral distortion and degradation in processing of image fusion with IHS method. A study on IHS fusion indicates that the color distortion can't be avoided. Meanwhile, the statistical property of wavelet coefficient with wavelet decomposition reflects those significant features, such as edges, lines and regions. So, a united optimal fusion method, which uses the statistical property and IHS transform on pixel and feature levels, is proposed. That is, the high frequency of intensity component I is fused on feature level with multi-resolution wavelet in IHS space. And the low frequency of intensity component I is fused on pixel level with optimal weight coefficients. Spectral information and spatial resolution are two performance indexes of optimal weight coefficients. Experiment results with QuickBird data of Shanghai show that it is a practical and effective method.
The cross wavelet analysis of dengue fever variability influenced by meteorological conditions
Lin, Yuan-Chien; Yu, Hwa-Lung; Lee, Chieh-Han
2015-04-01
The multiyear variation of meteorological conditions induced by climate change causes the changing diffusion pattern of infectious disease and serious epidemic situation. Among them, dengue fever is one of the most serious vector-borne diseases distributed in tropical and sub-tropical regions. Dengue virus is transmitted by several species of mosquito and causing lots amount of human deaths every year around the world. The objective of this study is to investigate the impact of meteorological variables to the temporal variation of dengue fever epidemic in southern Taiwan. Several extreme and average indices of meteorological variables, i.e. temperature and humidity, were used for this analysis, including averaged, maximum and minimum temperature, and average rainfall, maximum 1-hr rainfall, and maximum 24-hr rainfall. This study plans to identify and quantify the nonlinear relationship of meteorological variables and dengue fever epidemic, finding the non-stationary time-frequency relationship and phase lag effects of those time series from 1998-2011 by using cross wavelet method. Results show that meteorological variables all have a significant time-frequency correlation region to dengue fever epidemic in frequency about one year (52 weeks). The associated phases can range from 0 to 90 degrees (0-13 weeks lag from meteorological factors to dengue incidences). Keywords: dengue fever, cross wavelet analysis, meteorological factor
Neural networks and wavelet analysis in the computer interpretation of pulse oximetry data
Energy Technology Data Exchange (ETDEWEB)
Dowla, F.U.; Skokowski, P.G.; Leach, R.R. Jr.
1996-03-01
Pulse oximeters determine the oxygen saturation level of blood by measuring the light absorption of arterial blood. The sensor consists of red and infrared light sources and photodetectors. A method based on neural networks and wavelet analysis is developed for improved saturation estimation in the presence of sensor motion. Spectral and correlation functions of the dual channel oximetry data are used by a backpropagation neural network to characterize the type of motion. Amplitude ratios of red to infrared signals as a function of time scale are obtained from the multiresolution wavelet decomposition of the two-channel data. Motion class and amplitude ratios are then combined to obtain a short-time estimate of the oxygen saturation level. A final estimate of oxygen saturation is obtained by applying a 15 s smoothing filter on the short-time measurements based on 3.5 s windows sampled every 1.75 s. The design employs two backpropagation neural networks. The first neural network determines the motion characteristics and the second network determines the saturation estimate. Our approach utilizes waveform analysis in contrast to the standard algorithms that are based on the successful detection of peaks and troughs in the signal. The proposed algorithm is numerically efficient and has stable characteristics with a reduced false alarm rate with a small loss in detection. The method can be rapidly developed on a digital signal processing platform.
Wang, H. X.; Zong, W. J.; Sun, T.; Liu, Q.
2010-06-01
The wavelet analysis method has been extensively employed to analyze the surface structures and evaluate the surface roughness. In this work, however, the wavelet analysis method was introduced to decompose and reconstruct the sampled surface profile signals in the cutting direction that achieved by SPDT (single point diamond turning) operation, and the surface profile signals in tool feeding direction were reconstructed with the approximate harmonic functions directly. And moreover, the orthogonal design method, i.e. the combination design of general rotary method, was resorted to model the variations of the independent frequency and amplitude of different simulated harmonic signals in the cutting and tool feeding directions. As expected resultantly, a novel 3D surface topography modeling solution was established, which aims to predict and modify the finished KDP (potassium dihydrogen phosphate or KH 2PO 4) crystal surfaces. The validation tests were carried out finally under different cutting conditions, and the collected average surface roughness in any case was compared with the corresponding value as predicted. The results indicated the experimental data were well consistent with the predictions, and only an average relative error of 11.4% occurred in predicting the average surface roughness.
Application of Wavelet Analysis in Signal De-noising of Blast Shock Wave Overpressure
Institute of Scientific and Technical Information of China (English)
Jian-wei JIANG; Yu-jun FANG; Li-zhen WAN; Jian-bing MEN
2010-01-01
It's a problem to be solved how to de-noise the signal of blast shock wave overpressure.In the conventional methods,the high frequency of the signal is cut directly by some mathematics algorithms,such as Fourier Transform,but some of the useful signal will be cut together.We adopt a new method for the signal de-noising of shock wave overpressure by wavelet analysis.There are four steps in this method.Firstly,the original signal is de-cpmposed.Then the time-frequency features of the signal and noise are analyzed.Thirdly,the noise is separated from the signal by only cutting its frequency while the useful signal frequency is reserved as much as possible.Lastly,the useful signal with least loss of information is recovered by reconstruction process.To verify this method,a blast shock wave signal is de-noised with FFT to make a comparison.The results show that the signal de-noised by wavelet analysis approximates the ideal signal well.
Rolling Element Bearing Diagnostics by Combination of Envelope Analysis and Wavelet Transform
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Rolling element-bearing diagnostics has been studied over the last thirty years, and envelope analysis is widely recognized as being the best approach for detection and diagnosis of rolling element bearing incipient failure. But one of the on-going difficulties with envelope technique is to determine the best frequency band to envelop. Here, wavelet transform technique is introduced into envelope analysis to solve the problem by capturing bearing defects-sensory scales (i.e. frequency bands). A modulated Gaussian function is chosen to be the analytical wavelet because it coincides well with bearing defect-induced vibration signal patterns. Vibration signals measured from railway bearing tests were studied by the proposed method. Cases of bearings with single and multiple defects on inner and outer race under different testing conditions are presented. Experimental results showed that the proposed method allowed a more accurate local description and separation of transient signal part, which were caused by impacts between defects and the mating surfaces in the bearing. The combination method provides an effective signal detection technique for rolling element-bearing diagnostics.
Zou, Ling; Zhang, Yingchun; Yang, Laurence T; Zhou, Renlai
2010-02-01
The authors have developed a new approach by combining the wavelet denoising and principal component analysis methods to reduce the number of required trials for efficient extraction of brain evoked-related potentials (ERPs). Evoked-related potentials were initially extracted using wavelet denoising to enhance the signal-to-noise ratio of raw EEG measurements. Principal components of ERPs accounting for 80% of the total variance were extracted as part of the subspace of the ERPs. Finally, the ERPs were reconstructed from the selected principal components. Computer simulation results showed that the combined approach provided estimations with higher signal-to-noise ratio and lower root mean squared error than each of them alone. The authors further tested this proposed approach in single-trial ERPs extraction during an emotional process and brain responses analysis to emotional stimuli. The experimental results also demonstrated the effectiveness of this combined approach in ERPs extraction and further supported the view that emotional stimuli are processed more intensely.
Wavelet analysis of pressure fluctuation signals in a gas-solid fluidized bed
Institute of Scientific and Technical Information of China (English)
甄玲; 王晓萍; 黄海; 陈伯川; 黄春燕
2002-01-01
It has been shown that much dynamic information is hidden in the pressure fluctuation signals of a gas-solid fluidized bed. Unfortunately, due to the random and capricious nature of this signal, it is hard to realize reliable analysis using traditional signal processing methods such as statistical analysis or spectral analysis, which is done in Fourier domain. Information in different frequency band can be extracted by using wavelet analysis. On the evidence of the composition of the pressure fluctuation signals, energy of low frequency (ELF) is proposed to show the transition of fluidized regimes from bubbling fluidization to turbulent fluidization. Plots are presented to describe the fluidized bed's evolution to help identify the state of different flow regimes and provide a characteristic curve to identify the fluidized status effectively and reliably.
Ullah, Saleem; Skidmore, Andrew K; Naeem, Mohammad; Schlerf, Martin
2012-10-15
Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 μm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R(2)=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R(2)=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.
Fang, Li-Zhi
1998-01-01
Recent advances have shown wavelets to be an effective, and even necessary, mathematical tool for theoretical physics. This book is a timely overview of the progress of this new frontier. It includes an introduction to wavelet analysis, and applications in the fields of high energy physics, astrophysics, cosmology and statistical physics. The topics are selected for the interests of physicists and graduate students of theoretical studies. It emphasizes the need for wavelets in describing and revealing structure in physical problems, which is not easily accomplishing by other methods.
Directory of Open Access Journals (Sweden)
Gang Li
2013-12-01
Full Text Available Driving while fatigued is just as dangerous as drunk driving and may result in car accidents. Heart rate variability (HRV analysis has been studied recently for the detection of driver drowsiness. However, the detection reliability has been lower than anticipated, because the HRV signals of drivers were always regarded as stationary signals. The wavelet transform method is a method for analyzing non-stationary signals. The aim of this study is to classify alert and drowsy driving events using the wavelet transform of HRV signals over short time periods and to compare the classification performance of this method with the conventional method that uses fast Fourier transform (FFT-based features. Based on the standard shortest duration for FFT-based short-term HRV evaluation, the wavelet decomposition is performed on 2-min HRV samples, as well as 1-min and 3-min samples for reference purposes. A receiver operation curve (ROC analysis and a support vector machine (SVM classifier are used for feature selection and classification, respectively. The ROC analysis results show that the wavelet-based method performs better than the FFT-based method regardless of the duration of the HRV sample that is used. Finally, based on the real-time requirements for driver drowsiness detection, the SVM classifier is trained using eighty FFT and wavelet-based features that are extracted from 1-min HRV signals from four subjects. The averaged leave-one-out (LOO classification performance using wavelet-based feature is 95% accuracy, 95% sensitivity, and 95% specificity. This is better than the FFT-based results that have 68.8% accuracy, 62.5% sensitivity, and 75% specificity. In addition, the proposed hardware platform is inexpensive and easy-to-use.
Directory of Open Access Journals (Sweden)
Jayakishan Meher
2012-08-01
Full Text Available Correlation between gene expression profiles to disease or different developmental stages of a cell through microarray data and its analysis has been a great deal in molecular biology. As the microarray data have thousands of genes and very few sample, thus efficient feature extraction and computational method development is necessary for the analysis. In this paper we have proposed an effective feature extraction method based on factor analysis (FA with discrete wavelet transform (DWT to detect informative genes. Radial basis function neural network (RBFNN classifier is used to efficiently predict the sample class which has a low complexity than other classifier. The potential of the proposed approach is evaluated through an exhaustive study by many benchmark datasets. The experimental results show that the proposed method can be a useful approach for cancer classification.
Precursors of stall and surge processes in gas turbines revealed by wavelet analysis
Energy Technology Data Exchange (ETDEWEB)
Dremin, I.M.; Ivanov, O.V.; Nechitailo, V.A. [P.N. Lebedev Physical Institute, Moscow (Russian Federation); Furletov, V.I. [Central Institute for Aviation Motors, Moscow (Russian Federation); Terziev, V.G. [TEKO, Moscow (Russian Federation)
2002-06-01
Multiresolution wavelet analysis of pressure variations in a gas turbine compressor reveals the existence of precursors of stall and surge processes. Signals from eight pressure sensors positioned at various places within the compressor were recorded and digitized in three different operating modes in stationary conditions with a recording interval of 1 ms during 5-6 s. It has been discovered that there exists a scale of 32 intervals over which the dispersion (variance) of the wavelet coefficients shows a remarkable drop of about 40% for more than 1 s prior to the development of the malfunction. A shuffled sample of the same values of the pressure does not show such a drop demonstrating the dynamical origin of this effect. Higher order correlation moments reveal different slopes in these two regions differing by the variance values. The log-log dependence of the moments does not show clear fractal behavior because the scales of 16 and 32 intervals are not on the straight line of monofractals. This is a clear indication of the nonlinear response of the system at this scale. These results provide a means for automatic regulation of an engine, preventing possible failures. (author)
Efficient ECG signal analysis using wavelet technique for arrhythmia detection: an ANFIS approach
Khandait, P. D.; Bawane, N. G.; Limaye, S. S.
2010-02-01
This paper deals with improved ECG signal analysis using Wavelet Transform Techniques and employing subsequent modified feature extraction for Arrhythmia detection based on Neuro-Fuzzy technique. This improvement is based on suitable choice of features in evaluating and predicting life threatening Ventricular Arrhythmia . Analyzing electrocardiographic signals (ECG) includes not only inspection of P, QRS and T waves, but also the causal relations they have and the temporal sequences they build within long observation periods. Wavelet-transform is used for effective feature extraction and Adaptive Neuro-Fuzzy Inference System (ANFIS) is considered for the classifier model. In a first step, QRS complexes are detected. Then, each QRS is delineated by detecting and identifying the peaks of the individual waves, as well as the complex onset and end. Finally, the determination of P and T wave peaks, onsets and ends is performed. We evaluated the algorithm on several manually annotated databases, such as MIT-BIH Arrhythmia and CSE databases, developed for validation purposes. Features based on the ECG waveform shape and heart beat intervals are used as inputs to the classifiers. The performance of the ANFIS model is evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in classifying the ECG signals. Cross validation is used to measure the classifier performance. A testing classification accuracy of 95.13% is achieved which is a significant improvement.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Research on information spillover effects between financial markets remains active in the economic community. A Granger-type model has recently been used to investigate the spillover between London Metal Exchange (LME) and Shanghai Futures Exchange (SHFE), however, possible correlation between the future price and return on different time scales have been ignored. In this paper, wavelet multiresolution decomposition is used to investigate the spillover effects of copper future returns between the two markets. The daily return time series are decomposed on 2n (n=1, ..., 6) frequency bands through wavelet multiresolution analysis. The correlation between the two markets is studied with decomposed data. It is shown that high frequency detail components represent much more energy than low-frequency smooth components. The relation between copper future daily returns in LME and that in SHFE are different on different time scales. The fluctuations of the copper future daily returns in LME have large effect on that in SHFE in 32-day scale, but small effect in high frequency scales. It also has evidence that strong effects exist between LME and SHFE for monthly responses of the copper futures but not for daily responses.
Wavelet data analysis of micro-Raman spectra for follow-up monitoring in oral pathologies
Camerlingo, C.; Zenone, F.; Perna, G.; Capozzi, V.; Cirillo, N.; Gaeta, G. M.; Lepore, M.
2008-02-01
A wavelet multi-component decomposition algorithm has been used for data analysis of micro-Raman spectra from human biological samples. In particular, measurements have been performed on some samples of oral tissue and blood serum from patients affected by pemphigus vulgaris at different stages. Pemphigus is a chronic, autoimmune, blistering disease of the skin and mucous membranes with a potentially fatal outcome. The disease is characterized histologically by intradermal blisters and immunopathologically by the finding of tissue bound and circulating immunoglobulin G (IgG) antibody directed against the cell surface of keratinocytes. More than 150 spectra were measured by means of a Raman confocal microspectrometer apparatus using the 632.8 nm line of a He-Ne laser source. A discrete wavelet transform decomposition method has been applied to the recorded Raman spectra in order to overcome related to low-level signals and the presence of noise and background components due to light scattering and fluorescence. The results indicate that appropriate data processing can contribute to enlarge the medical applications of micro-Raman spectroscopy.
Griffiths, K. R.; Hicks, B. J.; Keogh, P. S.; Shires, D.
2016-08-01
In general, vehicle vibration is non-stationary and has a non-Gaussian probability distribution; yet existing testing methods for packaging design employ Gaussian distributions to represent vibration induced by road profiles. This frequently results in over-testing and/or over-design of the packaging to meet a specification and correspondingly leads to wasteful packaging and product waste, which represent 15bn per year in the USA and €3bn per year in the EU. The purpose of the paper is to enable a measured non-stationary acceleration signal to be replaced by a constructed signal that includes as far as possible any non-stationary characteristics from the original signal. The constructed signal consists of a concatenation of decomposed shorter duration signals, each having its own kurtosis level. Wavelet analysis is used for the decomposition process into inner and outlier signal components. The constructed signal has a similar PSD to the original signal, without incurring excessive acceleration levels. This allows an improved and more representative simulated input signal to be generated that can be used on the current generation of shaker tables. The wavelet decomposition method is also demonstrated experimentally through two correlation studies. It is shown that significant improvements over current international standards for packaging testing are achievable; hence the potential for more efficient packaging system design is possible.
Wavelet analysis and interpretation of gravity data in Sichuan-Yunnan region, China
Institute of Scientific and Technical Information of China (English)
LOU Hai; WANG Chun-yong
2005-01-01
The Bouguer gravity anomaly data of Sichuan-Yunnan region and its vicinity were analyzed with wavelet transformation method. In the process, complete orthogonal wavelet function system with good symmetry and higher vanishing moment was selected to decompose the gravity anomaly into two parts. With the power spectral analysis on the decomposed anomalies, we interpreted that the two parts of anomalies represent the density variation in upper and middle crust, and in deep crust and uppermost mantle, respectively. The two parts of anomalies indicate the difference between shallow and deep tectonics. The results of shallow-layer apparent density mapping reveal that: a) the crustal density in Sichuan basin is higher than that in Songpan-Garze orogenic zone; b) the density of Kangdian rhombic block is heterogeneous; c) the boundary faults of Kangdian block are of different density features, suggesting different tectonic signification. The results of deep-layer apparent density mapping show a similar,but not the same, density distribution pattern as the shallow results, and indicate that the tectonics of shallow and deep crust are different, they may be in a status of incomplete coupling. Our results also show that the earthquakes in this area are controlled not only by the fracture zones but also by the deep density distribution.
Interpretation of Normal and Pathological ECG Beats using Multiresolution Wavelet Analysis
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Shubhada S.Ardhapurkar
2012-12-01
Full Text Available The Discrete wavelet transform has great capability to analyse the temporal and spectral properties of non stationary signal like ECG. In this paper, we have developed and evaluated a robust algorithm using multiresolution analysis based on the discrete wavelet transform (DWT for twelve-lead electrocardiogram (ECG temporal feature extraction. In the first step, ECG was denoised considerably by employing kernel density estimation on subband coefficients then QRS complexes were detected. Further, by selecting appropriate coefficients and applying wave segmentation strategy P and T wave peaks were detected. Finally, the determination of P and T wave onsets and ends was performed. The novelty of this approach lies in detection of different morphologies in ECG wave with few decision rules. We have evaluated the algorithm on normal and abnormal beats from various manually annotated databases from physiobank having different sampling frequencies. The QRS detector obtained a sensitivity of 99.5% and a positive predictivity of 98.9% over the first lead of the MIT-BIH Arrhythmia Database.
Vaudor, Lise; Piegay, Herve; Wawrzyniak, Vincent; Spitoni, Marie
2016-04-01
The form and functioning of a geomorphic system result from processes operating at various spatial and temporal scales. Longitudinal channel characteristics thus exhibit complex patterns which vary according to the scale of study, might be periodic or segmented, and are generally blurred by noise. Describing the intricate, multiscale structure of such signals, and identifying at which scales the patterns are dominant and over which sub-reach, could help determine at which scales they should be investigated, and provide insights into the main controlling factors. Wavelet transforms aim at describing data at multiple scales (either in time or space), and are now exploited in geophysics for the analysis of nonstationary series of data. They provide a consistent, non-arbitrary, and multiscale description of a signal's variations and help explore potential causalities. Nevertheless, their use in fluvial geomorphology, notably to study longitudinal patterns, is hindered by a lack of user-friendly tools to help understand, implement, and interpret them. We have developed a free application, The Wavelet ToolKat, designed to facilitate the use of wavelet transforms on temporal or spatial series. We illustrate its usefulness describing longitudinal channel curvature and slope of three freely meandering rivers in the Amazon basin (the Purus, Juruá and Madre de Dios rivers), using topographic data generated from NASA's Shuttle Radar Topography Mission (SRTM) in 2000. Three types of wavelet transforms are used, with different purposes. Continuous Wavelet Transforms are used to identify in a non-arbitrary way the dominant scales and locations at which channel curvature and slope vary. Cross-wavelet transforms, and wavelet coherence and phase are used to identify scales and locations exhibiting significant channel curvature and slope co-variations. Maximal Overlap Discrete Wavelet Transforms decompose data into their variations at a series of scales and are used to provide
Research of Signal De-noising Technique Based on Wavelet
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Shigang Hu
2013-09-01
Full Text Available During the process of signal testing, often exposed to interference and influence of all kinds of noise signal, such as data collection and transmission and so may introduce noise. So in practical applications, before analysis of the data measured, the need for de-noising processing. The signal de-noising is a method for filtering the high frequency noise of the signal and makes the signal more precise. This paper deals with the general theory of wavelet transform, the application of wavelet transform in signal de-noising as well as the analysis of the characteristics of noise-polluted signa1. Matlab is used to be carried out the simu1ation where the different wavelet and different threshold of the same wavelet for signal de-noising are applied. An indicator of wavelet de-noising is presented , it is the indicator of smoothness. Through analysis of the experiment , considered MSE , SNR and smoothness , it can be a good way to evaluate the equality of wavelet de-noising. The results show that the wavelet transform can achieve excellent results in signal de-noising.
Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao
2013-07-01
[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness - 50.28%; happiness - 79.03%; fear - 77.78%; disgust - 88.69%; and neutral - 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.
Local Analysis, Cardinality, and Split Trick of Quasi-biorthogonal Frame Wavelets
Institute of Scientific and Technical Information of China (English)
Zhi Hua ZHANG
2011-01-01
The notion of quasi-biorthogonal frame wavelets is a generalization of the notion of orthogonal wavelets. A quasi-biorthogonal frame wavelet with the cardinality r consists of r pairs of functions.In this paper we first analyze the local property of the quasi-biorthogonal frame wavelet and show that its each pair of functions generates reconstruction formulas of the corresponding subspaces. Next we show that the lower bound of its cardinalities depends on a pair of dual frame multiresolution analyses deriving it. Finally, we present a split trick and show that any quasi-biorthogonal frame wavelet can be split into a new quasi-biorthogonal frame wavelet with an arbitrarily large cardinality. For generality,we work in the setting of matrix dilations.
A wavelet phase filter for emission tomography
Energy Technology Data Exchange (ETDEWEB)
Olsen, E.T.; Lin, B. [Illinois Inst. of Tech., Chicago, IL (United States). Dept. of Mathematics
1995-07-01
The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography (PET). Wavelet methods can smooth out noise while preserving significant features of images. Mallat et al. proposed a wavelet based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, the authors explore the properties of wavelet phase, with a focus on reconstruction of emission tomography images. Specifically, they show that the wavelet phase of regular Poisson noise under a Haar-type wavelet transform converges in distribution to a random variable uniformly distributed on [0, 2{pi}). They then propose three wavelet-phase-based denoising schemes which exploit this property: edge tracking, local phase variance thresholding, and scale phase variation thresholding. Some numerical results are also presented. The numerical experiments indicate that wavelet phase techniques show promise for wavelet based denoising methods.
Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain
Liu, Yipeng; Jin, Jing; Wang, Qiang; Shen, Yi
2012-10-01
Speckle reduction is a difficult task for ultrasound image processing because of low resolution and contrast. As a novel tool of image analysis, quaternion wavelet (QW) has some superior properties compared to discrete wavelets, such as nearly shift-invariant wavelet coefficients and phase-based texture presentation. We aim to exploit the excellent performance of speckle reduction in quaternion wavelet domain based on the soft thresholding method. First, we exploit the characteristics of magnitude and phases in quaternion wavelet transform (QWT) to the denoising application, and find that the QWT phases of the images are little influenced by the noises. Then we model the QWT magnitude using the Rayleigh distribution, and derive the thresholding criterion. Furthermore, we conduct several experiments on synthetic speckle images and real ultrasound images. The performance of the proposed speckle reduction algorithm, using QWT with soft thresholding, demonstrates superiority to those using discrete wavelet transform and classical algorithms.
Wavelet despiking of fractographs
Aubry, Jean-Marie; Saito, Naoki
2000-12-01
Fractographs are elevation maps of the fracture zone of some broken material. The technique employed to create these maps often introduces noise composed of positive or negative 'spikes' that must be removed before further analysis. Since the roughness of these maps contains useful information, it must be preserved. Consequently, conventional denoising techniques cannot be employed. We use continuous and discrete wavelet transforms of these images, and the properties of wavelet coefficients related to pointwise Hoelder regularity, to detect and remove the spikes.
Wavelet Transform Based Higher Order Statistical Analysis of Wind and Wave Time Histories
Habib Huseni, Gulamhusenwala; Balaji, Ramakrishnan
2017-10-01
Wind, blowing on the surface of the ocean, imparts the energy to generate the waves. Understanding the wind-wave interactions is essential for an oceanographer. This study involves higher order spectral analyses of wind speeds and significant wave height time histories, extracted from European Centre for Medium-Range Weather Forecast database at an offshore location off Mumbai coast, through continuous wavelet transform. The time histories were divided by the seasons; pre-monsoon, monsoon, post-monsoon and winter and the analysis were carried out to the individual data sets, to assess the effect of various seasons on the wind-wave interactions. The analysis revealed that the frequency coupling of wind speeds and wave heights of various seasons. The details of data, analysing technique and results are presented in this paper.
Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis
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SU, H.
2011-08-01
Full Text Available Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.
Wavelet De-noising of Speech Using Singular Spectrum Analysis for Decomposition Level Selection
Institute of Scientific and Technical Information of China (English)
CAI Tie; ZHU Jie
2007-01-01
The problem of speech enhancement using threshold de-noising in wavelet domain was considered. The appropriate decomposition level is another key factor pertinent to de-noising performance. This paper proposed a new wavelet-based de-noising scheme that can improve the enhancement performance significantly in the presence of additive white Gaussian noise. The proposed algorithm can adaptively select the optimal decomposition level of wavelet transformation according to the characteristics of noisy speech. The experimental results demonstrate that this proposed algorithm outperforms the classical wavelet-based de-noising method and effectively improves the practicability of this kind of techniques.
Pereira, Danilo Cesar; Ramos, Rodrigo Pereira; do Nascimento, Marcelo Zanchetta
2014-04-01
In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained from mammography equipments are one of the most frequently used techniques for helping in early diagnosis. Due to factors related to cost and professional experience, in the last two decades computer systems to support detection (Computer-Aided Detection - CADe) and diagnosis (Computer-Aided Diagnosis - CADx) have been developed in order to assist experts in detection of abnormalities in their initial stages. Despite the large number of researches on CADe and CADx systems, there is still a need for improved computerized methods. Nowadays, there is a growing concern with the sensitivity and reliability of abnormalities diagnosis in both views of breast mammographic images, namely cranio-caudal (CC) and medio-lateral oblique (MLO). This paper presents a set of computational tools to aid segmentation and detection of mammograms that contained mass or masses in CC and MLO views. An artifact removal algorithm is first implemented followed by an image denoising and gray-level enhancement method based on wavelet transform and Wiener filter. Finally, a method for detection and segmentation of masses using multiple thresholding, wavelet transform and genetic algorithm is employed in mammograms which were randomly selected from the Digital Database for Screening Mammography (DDSM). The developed computer method was quantitatively evaluated using the area overlap metric (AOM). The mean ± standard deviation value of AOM for the proposed method was 79.2 ± 8%. The experiments demonstrate that the proposed method has a strong potential to be used as the basis for mammogram mass segmentation in CC and MLO views. Another important aspect is that the method overcomes the limitation of analyzing only CC and MLO views. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.
Power Line Communication Experiment using Wavelet OFDM in U.S..
Koga, Hisao; Kodama, Nobutaka
Recently, the demand of high speed network in home is increasing, and PLC is expected as one of the solutions. We can see related researches on the high speed PLC system using a frequency band 2 MHz to 30 MHz. In this paper, we propose a wavelet based OFDM as a suitable method for realizing the high speed PLC system. The proposed wavelet OFDM method is composed of the M-band transmultiplexer which consists of the perfect reconstruction cosine-modulated filter bank. And the attenuation of the first side-lobe is above 35dB, which is a characteristic of the proposed method. As a result, we show that the proposed method has the inter-carrier interference characteristic which is superior to FFT-OFDM, and it also provides the flexible notch filter function which can reduce the influence on other communication systems existing in the communication band which the PLC uses. Finally, we describe that the simulation results about the BER characteristic of the proposed method in AWGN were almost the same as the theory, and that transmission rates which were measured by using prototype modems in a field test house in U.S. were above 35Mbps.
Kim, Won Hwa; Singh, Vikas; Chung, Moo K.; Hinrichs, Chris; Pachauri, Deepti; Okonkwo, Ozioma C.; Johnson, Sterling C.
2014-01-01
Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer’s disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer’s Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer’s Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation. PMID:24614060
Bravo-Imaz, Inaki; Davari Ardakani, Hossein; Liu, Zongchang; García-Arribas, Alfredo; Arnaiz, Aitor; Lee, Jay
2017-09-01
This paper focuses on analyzing motor current signature for fault diagnosis of gearboxes operating under transient speed regimes. Two different strategies are evaluated, extensively tested and compared to analyze the motor current signature in order to implement a condition monitoring system for gearboxes in industrial machinery. A specially designed test bench is used, thoroughly monitored to fully characterize the experiments, in which gears in different health status are tested. The measured signals are analyzed using discrete wavelet decomposition, in different decomposition levels using a range of mother wavelets. Moreover, a dual-level time synchronous averaging analysis is performed on the same signal to compare the performance of the two methods. From both analyses, the relevant features of the signals are extracted and cataloged using a self-organizing map, which allows for an easy detection and classification of the diverse health states of the gears. The results demonstrate the effectiveness of both methods for diagnosing gearbox faults. A slightly better performance was observed for dual-level time synchronous averaging method. Based on the obtained results, the proposed methods can used as effective and reliable condition monitoring procedures for gearbox condition monitoring using only motor current signature.
THE APPLICATION OF WAVELET-MULTIFRACTAL ANALYSIS IN PROBLEMS OF METAL STRUCTURE
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VOLCHUK V. N.
2015-09-01
Full Text Available Raising of problem. In order to obtain acceptable results of the evaluation of the metal structure developed methodology should include the use of both classical and modern methods of its evaluation and the properties of the produced goods. Thus, to establish the relationship between mechanical properties and structural elements of metal to use multifractal theory. The proposed method is the most appropriate to quantify the majority of real structures, which are integral approximation figures Euclid introduces some uncertainty, and therefore not always acceptable in practical problems of modern materials science. According to the proposed method, each of heterogeneous objects, which are the structures most metals can be characterized by variety of statistical Renyi dimensions. The range of dimensions multifractals interpreted as some of the physical laws, which have a separate statistical properties that make it possible to their financial performance. Application of statistical dimensions of the structural elements for the assessment of qualitative characteristics of metal contributes to their formalization as a function of the fractal dimension. This in turn makes it possible to identify and anticipate the physical and mechanical properties of the metal without producing special mechanical tests. Purpose obtain information about the possible application of wavelet-multifractal analysis to assess the microstructure of the metal. Conclusion. Using the methods of wavelet multifractal analysis, a statistical evaluation of the structural elements of steel St3ps. An analysis of the characteristics of uniformity, consistency and regularity of the structural elements has shown that most of the change observed in the samples subjected to accelerated cooling water in the temperature range of the intermediate (bainitic conversion 550 – 4500С, less - in samples cooled in the temperature range 650 pearlite transformation 6000С and the smallest
Research on Auto-detection for Remainder Particles of Aerospace Relay Based on Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
GAO Hong-liang; ZHANG Hui; WANG Shu-juan
2007-01-01
Aerospace relay is one kind of electronic components which is used widely in national defense system and aerospace system. The existence of remainder particles induces the reliability declining, which has become a severe problem in the development of aerospace relay. Traditional particle impact noise detection (PIND) method for remainder detection is ineffective for small particles, due to its low precision and involvement of subjective factors. An auto-detection method for PIND output signals is proposed in this paper, which is based on direct wavelet de-noising (DWD), cross-correlation analysis (CCA) and homo-filtering (HF), the method enhances the affectivity of PIND test about the small particles. In the end, some practical PIND output signals are analysed, and the validity of this new method is proved.
Wavelet analysis of the slow non-linear dynamics of wave turbulence
Energy Technology Data Exchange (ETDEWEB)
Miquel, Benjamin; Mordant, Nicolas, E-mail: benjamin.miquel@lps.ens.fr [Laboratoire de Physique Statistique, Ecole Normale Superieure (France)
2011-12-22
In wave turbulence, the derivation of solutions in the frame of the Weak Turbulence Theory relies on the existence of a double time-scale separation: first, between the period of the waves and characteristic nonlinear time t{sub NL} corresponding to energy exchange among waves; and secondly, between t{sub NL} and the characteristic dissipation time t{sub d}. Due to the lack of space and time resolved measurement, this hypothesis have remained unverified so far. We study the turbulence of flexion waves in thin elastic plates. t{sub d} is measured using the decline stage of the turbulence whereas a wavelet analysis is performed to measure the characteristic non-linear time t{sub NL}.
Ukwatta, T N
2016-01-01
Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features incorporate both temporal and spectral properties of the astronomical data. Classification is then performed on those abstract ...
Segmentation of Magnetic Resonance Imaging MRI using LS-SVM and Wavelet Multiresolution Analysis
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Luis A. Muñoz-Bedoya
2013-11-01
Full Text Available Currently, support vector machines (SVM have become a powerful tool to solve nonlinear classification problems. For the optimization of the tool, has developed a reformulation known as LS-SVM (Support Vector Machine least squares, which works with a model based on function minimization and Lagrange polynomials. Therefore, this paper presents a method for segmentation of magnetic resonance images specifically to study the morphology of the lungs and reach the quantification of relevant features in these images using SVM and LS-SVM. In addition to sorting technique in this work using techniques such as wavelet analysis to eliminate irrelevant information (compression and Splines algorithms to interpolate the information found and quantify the characteristics, which in this work were based on the recognition area, shape and abnormal structures present in the lung of these images.
A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic
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Meng Fan-Bo
2016-01-01
Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.
Generalized Thermostatistics and Wavelet Analysis of the Solar Wind and Proton Density Variability
Bolzan, M J A; Ramos, F M; Fagundes, P R; Sahai, Y; Bolzan, Mauricio J. A.; Rosa, Reinaldo R.; Ramos, Fernando M.; Fagundes, Paulo R.; Sahai, Yogeshwar
2005-01-01
In this paper, we analyze the probability density function (PDF) of solar wind velocity and proton density, based on generalized thermostatistics (GT) approach, comparing theoretical results with observational data. The time series analyzed were obtained from the SOHO satellite mission where measurements were sampled every hour. We present in the investigations data for two years of different solar activity: (a) moderate activity (MA) period (1997) and (b) high activity (HA) period (2000). For the MA period, the results show good agreement between experimental data and GT model. For the HA period, the agreement between experimental and theoretical PDFs was fairly good, but some distortions were observed, probably due to intermittent characteristics of turbulent processes. As a complementary analysis, the Global Wavelet Spectrum (GWS) was obtained allowing the characterization of the predominant temporal variability scales for both the periods and the stochastics aspects of the nonlinear solar wind variability...
Block Based Video Watermarking Scheme Using Wavelet Transform and Principle Component Analysis
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Nisreen I. Yassin
2012-01-01
Full Text Available In this paper, a comprehensive approach for digital video watermarking is introduced, where a binary watermark image is embedded into the video frames. Each video frame is decomposed into sub-images using 2 level discrete wavelet transform then the Principle Component Analysis (PCA transformation is applied for each block in the two bands LL and HH. The watermark is embedded into the maximum coefficient of the PCA block of the two bands. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility where there is no noticeable difference between the watermarked video frames and the original frames. The computed PSNR achieves high score which is 44.097 db. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment.
Research on mine noise sources analysis based on least squares wave-let transform
Institute of Scientific and Technical Information of China (English)
CHENG Gen-yin; YU Sheng-chen; CHEN Shao-jie; WEI Zhi-yong; ZHANG Xiao-chen
2010-01-01
In order to determine the characteristics of noise source accurately, the noise distribution at different frequencies was determined by taking the differences into account between aerodynamic noises, mechanical noise, electrical noise in terms of in frequency and intensity. Designed a least squares wavelet with high precision and special effects for strong interference zone (multi-source noise), which is applicable to strong noise analysis produced by underground mine, and obtained distribution of noise in different frequency and achieves good results. According to the results of decomposition, the characteristics of noise sources production can be more accurately determined, which lays a good foundation for the follow-up focused and targeted noise control, and provides a new method that is greatly applicable for testing and analyzing noise control.
Energy Technology Data Exchange (ETDEWEB)
Martinez-Torres, C.; Streppa, L. [CNRS, UMR5672, Laboratoire de Physique, Ecole Normale Supérieure de Lyon, 46 Allée d' Italie, Université de Lyon, 69007 Lyon (France); Arneodo, A.; Argoul, F. [CNRS, UMR5672, Laboratoire de Physique, Ecole Normale Supérieure de Lyon, 46 Allée d' Italie, Université de Lyon, 69007 Lyon (France); CNRS, UMR5798, Laboratoire Ondes et Matière d' Aquitaine, Université de Bordeaux, 351 Cours de la Libération, 33405 Talence (France); Argoul, P. [Université Paris-Est, Ecole des Ponts ParisTech, SDOA, MAST, IFSTTAR, 14-20 Bd Newton, Cité Descartes, 77420 Champs sur Marne (France)
2016-01-18
Compared to active microrheology where a known force or modulation is periodically imposed to a soft material, passive microrheology relies on the spectral analysis of the spontaneous motion of tracers inherent or external to the material. Passive microrheology studies of soft or living materials with atomic force microscopy (AFM) cantilever tips are rather rare because, in the spectral densities, the rheological response of the materials is hardly distinguishable from other sources of random or periodic perturbations. To circumvent this difficulty, we propose here a wavelet-based decomposition of AFM cantilever tip fluctuations and we show that when applying this multi-scale method to soft polymer layers and to living myoblasts, the structural damping exponents of these soft materials can be retrieved.
Directory of Open Access Journals (Sweden)
D. Pancheva
Full Text Available On the basis of bispectral analysis applied to the hourly data set of neutral wind measured by meteor radar in the MLT region above Bulgaria it was demonstrated that nonlinear processes are frequently and regularly acting in the mesopause region. They contribute significantly to the short-term tidal variability and are apparently responsible for the observed complicated behavior of the tidal characteristics. A Morlet wavelet transform is proposed as a technique for studying nonstationary signals. By simulated data it was revealed that the Morlet wavelet transform is especially convenient for analyzing signals with: (1 a wide range of dominant frequencies which are localized in different time intervals; (2 amplitude and frequency modulated spectral components, and (3 singular, wave-like events, observed in the neutral wind of the MLT region and connected mainly with large-scale disturbances propagated from below. By applying a Morlet wavelet transform to the hourly values of the amplitudes of diurnal and semidiurnal tides the basic oscillations with periods of planetary waves (1.5-20 days, as well as their development in time, are obtained. A cross-wavelet analysis is used to clarify the relation between the tidal and mean neutral wind variability. The results of bispectral analysis indicate which planetary waves participated in the nonlinear coupling with the atmospheric tides, while the results of cross-wavelet analysis outline their time intervals if these interactions are local.
Key words: Meteorology and atmospheric dynamics (middle atmosphere dynamics; waves and tides - Radio science (nonlinear phenomena
Comparison of fast discrete wavelet transform algorithms
Institute of Scientific and Technical Information of China (English)
MENG Shu-ping; TIAN Feng-chun; XU Xin
2005-01-01
This paper presents an analysis on and experimental comparison of several typical fast algorithms for discrete wavelet transform (DWT) and their implementation in image compression, particularly the Mallat algorithm, FFT-based algorithm, Short-length based algorithm and Lifting algorithm. The principles, structures and computational complexity of these algorithms are explored in details respectively. The results of the experiments for comparison are consistent to those simulated by MATLAB. It is found that there are limitations in the implementation of DWT. Some algorithms are workable only for special wavelet transform, lacking in generality. Above all, the speed of wavelet transform, as the governing element to the speed of image processing, is in fact the retarding factor for real-time image processing.
Element analysis: a wavelet-based method for analysing time-localized events in noisy time series
Lilly, Jonathan M.
2017-04-01
A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized `events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event's `region of influence' within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis, is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry.
Energy Technology Data Exchange (ETDEWEB)
Nunez-Carrera, A. [Comision Nacional de Seguridad Nuclear y Salvaguardias, Doctor Barragan 779, Col. Narvarte, Mexico D.F. 03020 (Mexico); Prieto-Guerrero, A. [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco, 186, Col. Vicentina, Mexico D.F. 09340 (Mexico); Espinosa-Martinez, E.-G. [Retorno Quebec 6, Col. Burgos de Cuernavaca 62580, Temixco, Mor. (Mexico); Espinosa-Paredes, G., E-mail: gepe@xanum.uam.m [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco, 186, Col. Vicentina, Mexico D.F. 09340 (Mexico)
2009-12-15
This paper is concerned about bistable flow, which is manifested by a small and spontaneous change in the recirculation loop flow that has been reported in some Boiling Water Reactors. Here some real time series of the bistable flow from the Laguna Verde Nuclear Power Plant (LVNPP) are analyzed using a methodology based on wavelet transform. This methodology involves the decomposition of the original signal using the Continuous Wavelet Transform (CWT) and the application of the Discrete Wavelet Transform (DWT) based on the Multiresolution Analysis (MRA). The CWT provides information about ruptures, discontinuities and fractal behavior. The MRA allows a fast implementation of the Discrete Wavelet Transform providing information about frequencies, discontinuities and transients that can be detected with analysis at different levels of details coefficients. The combination of both techniques allows the definition of an integral methodology for the study of reactor signals. We found that the associated frequencies for the singularities observed due to bistable flow for the case of LVNPP, correspond to the interval 0.01-0.1 Hz.
Complex Wavelet Transform-Based Face Recognition
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT and the single-tree complex wavelet transform (ST-CWT for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.
Review Paper :Comparative Analysis Of Mother Wavelet Functions With The ECG Signals
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Kapil Tajane
2014-01-01
Full Text Available Electrocardiographic ECG gives the information about electrical activity of the heart captured over time by attaching an external electrode to the skin. Now a days ECG signal is used as a baseline to determine the hearts condition. It is very much essential to detect and process ECG signal accurately. ECG consists of various types of noise such as muscle noise, baseline wander and power line interference etc. To remove such types of noise wavelet transform is used. Mother wavelet is an effective tool for denoising such signals. But selection of proper mother wavelet for the ECG signal is again a challenging task. This paper gives the survey about the wavelet transforms useful for ECG denoising. The different wavelet transform are compared and from that we can decide which one is more suitable.
Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis
Qin, Yi; Xing, Jianfeng; Mao, Yongfang
2016-08-01
Aimed at solving the key problem in weak transient detection, the present study proposes a new transient feature extraction approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding. Firstly, a fast optimization algorithm based on the Shannon entropy is developed to obtain the optimized Morlet wavelet parameter. Compared to the existing Morlet wavelet parameter optimization algorithm, this algorithm has lower computation complexity. After performing the optimized Morlet wavelet transform on the analyzed signal, the kurtosis index is used to select the characteristic scales and obtain the corresponding wavelet coefficients. From the time-frequency distribution of the periodic impulsive signal, it is found that the transient signal can be reconstructed by the wavelet coefficients at several characteristic scales, rather than the wavelet coefficients at just one characteristic scale, so as to improve the accuracy of transient detection. Due to the noise influence on the characteristic wavelet coefficients, the adaptive soft-thresholding method is applied to denoise these coefficients. With the denoised wavelet coefficients, the transient signal can be reconstructed. The proposed method was applied to the analysis of two simulated signals, and the diagnosis of a rolling bearing fault and a gearbox fault. The superiority of the method over the fast kurtogram method was verified by the results of simulation analysis and real experiments. It is concluded that the proposed method is extremely suitable for extracting the periodic impulsive feature from strong background noise.
Sych, Robert; Nakariakov, Valery; Anfinogentov, Sergey
Wavelet analysis is suitable for investigating waves and oscillating in solar atmosphere, which are limited in both time and frequency. We have developed an algorithms to detect this waves by use the Pixelize Wavelet Filtration (PWF-method). This method allows to obtain information about the presence of propagating and non-propagating waves in the data observation (cube images), and localize them precisely in time as well in space. We tested the algorithm and found that the results of coronal waves detection are consistent with those obtained by visual inspection. For fast exploration of the data cube, in addition, we applied early-developed Period- Map analysis. This method based on the Fast Fourier Transform and allows on initial stage quickly to look for "hot" regions with the peak harmonic oscillations and determine spatial distribution at the significant harmonics. We propose the detection procedure of coronal waves separate on two parts: at the first part, we apply the PeriodMap analysis (fast preparation) and than, at the second part, use information about spatial distribution of oscillation sources to apply the PWF-method (slow preparation). There are two possible algorithms working with the data: in automatic and hands-on operation mode. Firstly we use multiply PWF analysis as a preparation narrowband maps at frequency subbands multiply two and/or harmonic PWF analysis for separate harmonics in a spectrum. Secondly we manually select necessary spectral subband and temporal interval and than construct narrowband maps. For practical implementation of the proposed methods, we have developed the remote data processing system at Institute of Solar-Terrestrial Physics, Irkutsk. The system based on the data processing server - http://pwf.iszf.irk.ru. The main aim of this resource is calculation in remote access through the local and/or global network (Internet) narrowband maps of wave's sources both in whole spectral band and at significant harmonics. In addition
Fusion of Daubechies Wavelet Coefficients for Human Face Recognition
Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas
2010-01-01
In this paper fusion of visual and thermal images in wavelet transformed domain has been presented. Here, Daubechies wavelet transform, called as D2, coefficients from visual and corresponding coefficients computed in the same manner from thermal images are combined to get fused coefficients. After decomposition up to fifth level (Level 5) fusion of coefficients is done. Inverse Daubechies wavelet transform of those coefficients gives us fused face images. The main advantage of using wavelet transform is that it is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information. Fused images thus found are passed through Principal Component Analysis (PCA) for reduction of dimensions and then those reduced fused images are classified using a multi-layer perceptron. For experiments IRIS Thermal/Visual Face Database was used. Experimental results show that the performance of the approach presented here achieves maximum...
Noise reduction in LOS wind velocity of Doppler lidar using discrete wavelet analysis
Wu, Songhua; Liu, Zhishen; Sun, Dapeng
2003-12-01
The line of sight (LOS) wind velocity can be determined from the incoherent Doppler lidar backscattering signals. Noise and interference in the measurement greatly degrade the inversion accuracy. In this paper, we apply the discrete wavelet denoising method by using biorthogonal wavelets and adopt a distancedependent thresholds algorithm to improve the accuracy of wind velocity measurement by incoherent Doppler lidar. The noisy simulation data are processed and compared with the true LOS wind velocity. The results are compared by the evaluation of both the standard deviation and correlation coefficient.The results suggest that wavelet denoising with distance-dependent thresholds can considerably reduce the noise and interfering turbulence for wind lidar measurement.
Orthogonal Matrix-Valued Wavelet Packets
Institute of Scientific and Technical Information of China (English)
Qingjiang Chen; Cuiling Wang; Zhengxing Cheng
2007-01-01
In this paper,we introduce matrix-valued multiresolution analysis and matrixvalued wavelet packets. A procedure for the construction of the orthogonal matrix-valued wavelet packets is presented. The properties of the matrix-valued wavelet packets are investigated. In particular,a new orthonormal basis of L2(R,Cs×s) is obtained from the matrix-valued wavelet packets.
Wavelet-fractional Fourier transforms
Institute of Scientific and Technical Information of China (English)
Yuan Lin
2008-01-01
This paper extends the definition of fractional Fourier transform (FRFT) proposed by Namias V by using other orthonormal bases for L2 (R) instead of Hermite-Ganssian functions.The new orthonormal basis is gained indirectly from multiresolution analysis and orthonormal wavelets. The so defined FRFT is called wavelets-fractional Fourier transform.
Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space.
Kim, Won Hwa; Adluru, Nagesh; Chung, Moo K; Charchut, Sylvia; GadElkarim, Johnson J; Altshuler, Lori; Moody, Teena; Kumar, Anand; Singh, Vikas; Leow, Alex D
2013-01-01
Advances in resting state fMRI and diffusion weighted imaging (DWI) have led to much interest in studies that evaluate hypotheses focused on how brain connectivity networks show variations across clinically disparate groups. However, various sources of error (e.g., tractography errors, magnetic field distortion, and motion artifacts) leak into the data, and make downstream statistical analysis problematic. In small sample size studies, such noise have an unfortunate effect that the differential signal may not be identifiable and so the null hypothesis cannot be rejected. Traditionally, smoothing is often used to filter out noise. But the construction of convolving with a Gaussian kernel is not well understood on arbitrarily connected graphs. Furthermore, there are no direct analogues of scale-space theory for graphs--ones which allow to view the signal at multiple resolutions. We provide rigorous frameworks for performing 'multi-resolutional' analysis on brain connectivity graphs. These are based on the recent theory of non-Euclidean wavelets. We provide strong evidence, on brain connectivity data from a network analysis study (structural connectivity differences in adult euthymic bipolar subjects), that the proposed algorithm allows identifying statistically significant network variations, which are clinically meaningful, where classical statistical tests, if applied directly, fail.
Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis
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Samuel Akwei-Sekyere
2015-07-01
Full Text Available The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.
Piersanti, Mirko; Materassi, Massimo; Spogli, Luca; Cicone, Antonio; Alberti, Tommaso
2016-04-01
Highly irregular fluctuations of the power of trans-ionospheric GNSS signals, namely radio power scintillation, are, at least to a large extent, the effect of ionospheric plasma turbulence, a by-product of the non-linear and non-stationary evolution of the plasma fields defining the Earth's upper atmosphere. One could expect the ionospheric turbulence characteristics of inter-scale coupling, local randomness and high time variability to be inherited by the scintillation on radio signals crossing the medium. On this basis, the remote sensing of local features of the turbulent plasma could be expected as feasible by studying radio scintillation. The dependence of the statistical properties of the medium fluctuations on the space- and time-scale is the distinctive character of intermittent turbulent media. In this paper, a multi-scale statistical analysis of some samples of GPS radio scintillation is presented: the idea is that assessing how the statistics of signal fluctuations vary with time scale under different Helio-Geophysical conditions will be of help in understanding the corresponding multi-scale statistics of the turbulent medium causing that scintillation. In particular, two techniques are tested as multi-scale decomposition schemes of the signals: the discrete wavelet analysis and the Empirical Mode Decomposition. The discussion of the results of the one analysis versus the other will be presented, trying to highlight benefits and limits of each scheme, also under suitably different helio-geophysical conditions.
Time Scale Analysis of Interest Rate Spreads and Output Using Wavelets
Directory of Open Access Journals (Sweden)
Marco Gallegati
2013-04-01
Full Text Available This paper adds to the literature on the information content of different spreads for real activity by explicitly taking into account the time scale relationship between a variety of monetary and financial indicators (real interest rate, term and credit spreads and output growth. By means of wavelet-based exploratory data analysis we obtain richer results relative to the aggregate analysis by identifying the dominant scales of variation in the data and the scales and location at which structural breaks have occurred. Moreover, using the “double residuals” regression analysis on a scale-by-scale basis, we find that changes in the spread in several markets have different information content for output at different time frames. This is consistent with the idea that allowing for different time scales of variation in the data can provide a fruitful understanding of the complex dynamics of economic relationships between variables with non-stationary or transient components, certainly richer than those obtained using standard time domain methods.
Zhou, Jing; Schalkoff, Robert J; Dean, Brian C; Halford, Jonathan J
2013-01-01
Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.
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Anutam
2014-10-01
Full Text Available Image Denoising is an important part of diverse image processing and computer vision problems. The important property of a good image denoising model is that it should completely remove noise as far as possible as well as preserve edges. One of the most powerful and perspective approaches in this area is image denoising using discrete wavelet transform (DWT. In this paper, comparison of various Wavelets at different decomposition levels has been done. As number of levels increased, Peak Signal to Noise Ratio (PSNR of image gets decreased whereas Mean Absolute Error (MAE and Mean Square Error (MSE get increased . A comparison of filters and various wavelet based methods has also been carried out to denoise the image. The simulation results reveal that wavelet based Bayes shrinkage method outperforms other methods.
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Arif Billah Dar
2014-01-01
Full Text Available This paper investigates the synchronization of fixed income markets within Eurozone countries using the new wavelet based methodology. Conventional wavelet methods that use multivariate set of variables to calculate pairwise correlation and cross correlation lead to spurious correlation due to possible relationships with other variables, amplification of type-1 errors, and results, in the form of large set of erroneous graphs. Given these disadvantages of conventional wavelet based pairwise correlation and cross-correlation method, we avoid these limitations by using wavelet multiple correlation and multiple cross correlations to analyze the relationships in Eurozone fixed income markets. Our results based on this methodology indicate that Eurozone fixed income markets are highly integrated and this integration grows with timescales, and hence there is almost no scope for independent monetary policy and bond diversification in these countries.
Institute of Scientific and Technical Information of China (English)
CEN Wei; YANG ShiFeng; XUE Rong; XU RiWei; YU DingSheng
2007-01-01
Surface morphologies of supported polyethylene (PE) catalysts are investigated by an approach combining fractal with wavelet. The multiscale edge (detail) pictures of catalyst surface are extracted by wavelet transform modulus maxima (WTMM) method. And, the distribution of edge points on the edge image at every scale is studied with fractal and multifractal method. Furthermore, the singularity intensity distribution of edge points in the PE catalyst is analyzed by multifractal spectrum based on WTMM. The results reveal that the fractal dimension values and multifractal spectrums of edge images at small scales have a good relation with the activity and surface morphology of PE catalyst. Meanwhile the catalyst exhibiting the higher activity shows the wider singular strength span of multifractal spectrum based on WTMM, as well as the more edge points with the higher singular intensity. The research on catalyst surface morphology with hybrid fractal and wavelet method exerts the superiorities of wavelet and fractal theories and offers a thought for studying solid surfaces morphologies.
Wavelet spectrum analysis on energy transfer of multi-scale structures in wall turbulence
Institute of Scientific and Technical Information of China (English)
Zhen-yan XIA; Yan TIAN; Nan JIANG
2009-01-01
The streamwise velocity components at different vertical heights in wall turbulence were measured. Wavelet transform was used to study the turbulent energy spectra, indicating that the global spectrum results from the weighted average of Fourier spectrum based on wavelet scales. Wavelet transform with more vanishing moments can express the declining of turbulent spectrum. The local wavelet spectrum shows that the physical phenomena such as deformation or breakup of eddies are related to the vertical position in the boundary layer, and the energy-containing eddies exist in a multi-scale form. Moreover, the size of these eddies increases with the measured points moving out of the wall. In the buffer region, the small scale energy-containing eddies with higher frequency are excited. In the outer region, the maximal energy is concentrated in the low-frequency large-scale eddies, and the frequency domain of energy-containing eddies becomes narrower.
Vibration analysis of composite pipes using the finite element method with B-spline wavelets
Energy Technology Data Exchange (ETDEWEB)
Oke, Wasiu A.; Khulief, Yehia A. [King Fahd University of Petroleum and Minerals, Dhahran (Saudi Arabia)
2016-02-15
A finite element formulation using the B-spline wavelets on the interval is developed for modeling the free vibrations of composite pipes. The composite FRP pipe element is treated as a beam element. The finite pipe element is constructed in the wavelet space and then transformed to the physical space. Detailed expressions of the mass and stiffness matrices are derived for the composite pipe using the Bspline scaling and wavelet functions. Both Euler-Bernoulli and Timoshenko beam theories are considered. The generalized eigenvalue problem is formulated and solved to obtain the modal characteristics of the composite pipe. The developed wavelet-based finite element discretization scheme utilizes significantly less elements compared to the conventional finite element method for modeling composite pipes. Numerical solutions are obtained to demonstrate the accuracy of the developed element, which is verified by comparisons with some available results in the literature.
Wei, Jiahong; Liu, Chong; Ren, Tongqun; Liu, Haixia; Zhou, Wenjing
2017-02-08
The rail fastening system is an important part of a high-speed railway track. It is always critical to the operational safety and comfort of railway vehicles. Therefore, the condition detection of the rail fastening system, looseness or absence, is an important task in railway maintenance. However, the vision-based method cannot identify the severity of rail fastener looseness. In this paper, the condition of rail fastening system is monitored based on an automatic and remote-sensing measurement system. Meanwhile, wavelet packet analysis is used to analyze the acceleration signals, based on which two damage indices are developed to locate the damage position and evaluate the severity of rail fasteners looseness, respectively. To verify the effectiveness of the proposed method, an experiment is performed on a high-speed railway experimental platform. The experimental results show that the proposed method is effective to assess the condition of the rail fastening system. The monitoring system significantly reduces the inspection time and increases the efficiency of maintenance management.
Directory of Open Access Journals (Sweden)
Jiahong Wei
2017-02-01
Full Text Available The rail fastening system is an important part of a high-speed railway track. It is always critical to the operational safety and comfort of railway vehicles. Therefore, the condition detection of the rail fastening system, looseness or absence, is an important task in railway maintenance. However, the vision-based method cannot identify the severity of rail fastener looseness. In this paper, the condition of rail fastening system is monitored based on an automatic and remote-sensing measurement system. Meanwhile, wavelet packet analysis is used to analyze the acceleration signals, based on which two damage indices are developed to locate the damage position and evaluate the severity of rail fasteners looseness, respectively. To verify the effectiveness of the proposed method, an experiment is performed on a high-speed railway experimental platform. The experimental results show that the proposed method is effective to assess the condition of the rail fastening system. The monitoring system significantly reduces the inspection time and increases the efficiency of maintenance management.
Wavelet Transform - A New Tool for Analysis of Harmonics in Power Systems
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Osipov D. S.
2016-01-01
Full Text Available This paper presents a review of application of discrete wavelet transform in power system transient analyses. Based on the discrete time domain approximation, the system components such as resistor and inductor are modeled respectively in discrete wavelet domain for the purpose of transient and steady state analyses. Numerical results for transient inductor model can be implemented by any kind of power system including normal and emergency operating modes.
Application of Wavelet Analysis in Detecting Foreign Object Debris on the Runway
Yu Zhi-jing; Yang Xue-you; Guo Xiaojing
2013-01-01
FOD is dangerous for aircraft safety. And it can be suggested to use image processing technology on the FODâ€™s detection. Depending on image processing system, a major subsystem in foreign object debris (FOD) detecting system on the runway, FOD image will be observed efficiently and rapidly with few economy costs and highly accuracy and reliability. The thesis analyses the characteristics and principles of wavelet transformation algorithm and applies wavelet theory on FODâ€™s Identification ...
Palm Oil Price, Exchange Rate, and Stock Market: A Wavelet Analysis on the Malaysian Market
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Buerhan Saiti
2014-05-01
Full Text Available The study investigates causality between palm oil price, exchange rate and the Kuala Lumpur Composite Index (KLCI based on the theory of wavelets on the basis of monthly data from the period January 1990 - December 2012. This methodology enables us to identify that the causality between these economic variables at different time intervals. This wavelet decomposition also provides additional evidence to the “reverse causality” theory. We found that the wavelet cross-correlations between stock price and exchange rate skewed to the right at all levels with negative significant correlations which implies that the exchange rate leads the stock price. In the case of stock and commodity prices, there is no significant wavelet-crosscorrelation at first four levels. However, the wavelet cross-correlations skewed to the left at level 5 which implies that the stock price leads commodity price in the long-run. Finally, there is no significant wavelet cross-correlations at all levels as long as we concern between commodity price and exchange rate. It implies that there is no lead-lag relationship between commodity price and exchange rate.
A wavelet analysis for the X-ray absorption spectra of molecules
Energy Technology Data Exchange (ETDEWEB)
Penfold, T. J. [Ecole polytechnique Federale de Lausanne, Laboratoire de spectroscopie ultrarapide, ISIC, FSB-BSP, CH-1015 Lausanne (Switzerland); Ecole polytechnique Federale de Lausanne, Laboratoire de chimie et biochimie computationnelles, ISIC, FSB-BCH, CH-1015 Lausanne (Switzerland); SwissFEL, Paul Scherrer Inst, CH-5232 Villigen (Switzerland); Tavernelli, I.; Rothlisberger, U. [Ecole polytechnique Federale de Lausanne, Laboratoire de chimie et biochimie computationnelles, ISIC, FSB-BCH, CH-1015 Lausanne (Switzerland); Milne, C. J.; Abela, R. [SwissFEL, Paul Scherrer Inst, CH-5232 Villigen (Switzerland); Reinhard, M.; Nahhas, A. El; Chergui, M. [Ecole polytechnique Federale de Lausanne, Laboratoire de spectroscopie ultrarapide, ISIC, FSB-BSP, CH-1015 Lausanne (Switzerland)
2013-01-07
We present a Wavelet transform analysis for the X-ray absorption spectra of molecules. In contrast to the traditionally used Fourier transform approach, this analysis yields a 2D correlation plot in both R- and k-space. As a consequence, it is possible to distinguish between different scattering pathways at the same distance from the absorbing atom and between the contributions of single and multiple scattering events, making an unambiguous assignment of the fine structure oscillations for complex systems possible. We apply this to two previously studied transition metal complexes, namely iron hexacyanide in both its ferric and ferrous form, and a rhenium diimine complex, [ReX(CO){sub 3}(bpy)], where X = Br, Cl, or ethyl pyridine (Etpy). Our results demonstrate the potential advantages of using this approach and they highlight the importance of multiple scattering, and specifically the focusing phenomenon to the extended X-ray absorption fine structure (EXAFS) spectra of these complexes. We also shed light on the low sensitivity of the EXAFS spectrum to the Re-X scattering pathway.
Online Test and Fault Diagnosis of Yarn Quality Using Wavelet Analysis And FFT
Institute of Scientific and Technical Information of China (English)
HONG Xi-jun; QiU Hao-bo; LI Yu-ming; LI Cong-xin
2002-01-01
A new online system of monitoring yarn quality and fault diagnosis is presented. This system integrates the technologies of sensor, signal process, commuunication,network, computer, control, instrument structure and mass knowledge of expert. Comparing with conventional off. Line yarn test, the new systemcan find the quality defects of yarn online in time and compensate for the lack of expert knowledge in manual analysis. It can save a lot of yarn wasted in off- line test and improve product quality. By using laser sensor to sample the diameter signal of yarn and doing wavelet analysis and FFT to extract fault characteristics, a set of reasoning mechanism is established to aralyze yarn quality and locate the fault origination. The experimental results show that new system can do well in monitoring yarn quality online comparing with conventional off-line yarn test. It can test the quality of yarn in real-time with high efficiency and analyze the fault reason accurately. It is very useful to apply this new system to upgrade yarn quality in cotton textile industry at present.
Lamb wave feature extraction using discrete wavelet transformation and Principal Component Analysis
Ghodsi, Mojtaba; Ziaiefar, Hamidreza; Amiryan, Milad; Honarvar, Farhang; Hojjat, Yousef; Mahmoudi, Mehdi; Al-Yahmadi, Amur; Bahadur, Issam
2016-04-01
In this research, a new method is presented for eliciting the proper features for recognizing and classifying the kinds of the defects by guided ultrasonic waves. After applying suitable preprocessing, the suggested method extracts the base frequency band from the received signals by discrete wavelet transform and discrete Fourier transform. This frequency band can be used as a distinctive feature of ultrasonic signals in different defects. Principal Component Analysis with improving this feature and decreasing extra data managed to improve classification. In this study, ultrasonic test with A0 mode lamb wave is used and is appropriated to reduce the difficulties around the problem. The defects under analysis included corrosion, crack and local thickness reduction. The last defect is caused by electro discharge machining (EDM). The results of the classification by optimized Neural Network depicts that the presented method can differentiate different defects with 95% precision and thus, it is a strong and efficient method. Moreover, comparing the elicited features for corrosion and local thickness reduction and also the results of the two's classification clarifies that modeling the corrosion procedure by local thickness reduction which was previously common, is not an appropriate method and the signals received from the two defects are different from each other.
Prabusankarlal, K M; Thirumoorthy, P; Manavalan, R
2016-11-01
Earliest detection and diagnosis of breast cancer reduces mortality rate of patients by increasing the treatment options. A novel method for the segmentation of breast ultrasound images is proposed in this work. The proposed method utilizes undecimated discrete wavelet transform to perform multiresolution analysis of the input ultrasound image. As the resolution level increases, although the effect of noise reduces, the details of the image also dilute. The appropriate resolution level, which contains essential details of the tumor, is automatically selected through mean structural similarity. The feature vector for each pixel is constructed by sampling intra-resolution and inter-resolution data of the image. The dimensionality of feature vectors is reduced by using principal components analysis. The reduced set of feature vectors is segmented into two disjoint clusters using spatial regularized fuzzy c-means algorithm. The proposed algorithm is evaluated by using four validation metrics on a breast ultrasound database of 150 images including 90 benign and 60 malignant cases. The algorithm produced significantly better segmentation results (Dice coef = 0.8595, boundary displacement error = 9.796, dvi = 1.744, and global consistency error = 0.1835) than the other three state of the art methods.
Directory of Open Access Journals (Sweden)
Giuseppa Sciortino
2016-04-01
Full Text Available We propose a methodology to support the physician in the automatic identification of mid-sagittal sections of the fetus in ultrasound videos acquired during the first trimester of pregnancy. A good mid-sagittal section is a key requirement to make the correct measurement of nuchal translucency which is one of the main marker for screening of chromosomal defects such as trisomy 13, 18 and 21. NT measurement is beyond the scope of this article. The proposed methodology is mainly based on wavelet analysis and neural network classifiers to detect the jawbone and on radial symmetry analysis to detect the choroid plexus. Those steps allow to identify the frames which represent correct mid-sagittal sections to be processed. The performance of the proposed methodology was analyzed on 3000 random frames uniformly extracted from 10 real clinical ultrasound videos. With respect to a ground-truth provided by an expert physician, we obtained a true positive, a true negative and a balanced accuracy equal to 87.26%, 94.98% and 91.12% respectively.
Bozhokin, S. V.
2010-09-01
Quantitative parameters characterizing transient processes of mastering and forgetting of photostimulation (PST) rhythms for a nonstationary electroencephalogram (EEG) are developed on the basis of a continuous wavelet transformation. Nonstationarity factor K nst(μ), as well as rhythm mastering K M (μ) and confinement K C (μ) factors are calculated for various spectral ranges μ. Photoflash mastering time τ M = τ S + τ I , which is the sum of latent silence period τ S after PST actuation and the rhythm increasing period τ I is calculated. In the case of PST, the EEG rhythm retardation time τ R relative to the beginning of PST is calculated. Rhythm forgetting time τ F = τ P + τ D after PST actuation is the sum of the preservation time τ P of the corresponding rhythm over a certain time interval and its decay period τ D . The lag time τ L of the EEG signal relative to the PST signal after its removal is determined. The proposed method is used in quantitative analysis and classification of transient processes characterizing the properties of the central nervous system. Possible applications of the method in analysis of various nonstationary signals in physics are discussed.
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Jaison Bennet
2014-01-01
Full Text Available Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN, naive Bayes, and support vector machine (SVM. Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT and moving window technique (MWT is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.
Spherical 3D Isotropic Wavelets
Lanusse, F; Starck, J -L
2011-01-01
Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis in is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the Fourier-Bessel decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. 2006. We also present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large...
Bartels, Richard; Weniger, Christoph
2017-01-01
A clear excess in the Fermi-LAT data is present at energies around a few GeV. The spectrum of this so-called 'GeV excess' is remarkably similar to the expected annihilation signal of WIMP dark matter. However, a large bulge population of millisecond pulsars living below the Fermi-LAT detection threshold could also explain the excess spectrum. In a recent work we optimized the search for sub-threshold sources, by applying a wavelet transform to the Fermi-LAT gamma-ray data. In the Inner-Galaxy the wavelet signal is significantly enhanced, providing supportive evidence for the point source interpretation of the excess. In these proceedings we will extent our previous work with a spectral analysis and elaborate on the potential contamination from substructures in the gas.
Qin, Lei; He, Bin
2005-12-01
Electroencephalogram (EEG) recordings during motor imagery tasks are often used as input signals for brain-computer interfaces (BCIs). The translation of these EEG signals to control signals of a device is based on a good classification of various kinds of imagination. We have developed a wavelet-based time-frequency analysis approach for classifying motor imagery tasks. Time-frequency distributions (TFDs) were constructed based on wavelet decomposition and event-related (de)synchronization patterns were extracted from symmetric electrode pairs. The weighted energy difference of the electrode pairs was then compared to classify the imaginary movement. The present method has been tested in nine human subjects and reached an averaged classification rate of 78%. The simplicity of the present technique suggests that it may provide an alternative method for EEG-based BCI applications.
Kandala, Chari V.; Sundaram, Jaya; Govindarajan, K. N.; Butts, Chris L.; Subbiah, Jeyam
2009-03-01
Moisture and oil contents are important quality factors often measured and monitored in the processing and storage of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell peanuts) from the impedance measurements. The wavelet analysis yielded similar R2 values with fewer variables as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts without shelling them will be of considerable help to the peanut industry.
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2016-01-01
Full Text Available This study introduces the analysis of structural health monitoring (SHM system based on acceleration measurements during an earthquake. The SHM system is applied to assess the performance investigation of the administration building in Seoul National University of Education, South Korea. The statistical and wavelet analysis methods are applied to investigate and assess the performance of the building during an earthquake shaking which took place on March 31, 2014. The results indicate that (1 the acceleration, displacement, and torsional responses of the roof recording point on the top floor of the building are more dominant in the X direction; (2 the rotation of the building has occurred at the base recording point; (3 95% of the energy content of the building response is shown in the dominant frequency range (6.25–25 Hz; (4 the wavelet spectrum illustrates that the roof vibration is more obvious and dominant during the shaking; and (5 the wavelet spectrum reveals the elasticity responses of the structure during the earthquake shaking.
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Evgeniya eGerasimova
2014-05-01
Full Text Available Breast cancer is the most common type of cancer among women and despite recent advances in the medical field, there are still some inherent limitations in the currently used screening techniques. The radiological interpretation of screening X-ray mammograms often leads to over-diagnosis and, as a consequence, to unnecessary traumatic and painful biopsies. Here we propose a computer-aided multifractal analysis of dynamic infrared (IR imaging as an efficient method for identifying women with risk of breast cancer. Using a wavelet-based multi-scale method to analyze the temporal fluctuations of breast skin temperature collected from a panel of patients with diagnosed breast cancer and some female volunteers with healthy breasts, we show that the multifractal complexity of temperature fluctuations observed in healthy breasts is lost in mammary glands with malignant tumor. Besides potential clinical impact, these results open new perspectives in the investigation of physiological changes that may precede anatomical alterations in breast cancer development.
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Hanung Tyas Saksono
2010-07-01
Full Text Available Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA. Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.
Wavelet-based multifractal analysis on a time series of solar activity and PDO climate index
Maruyama, Fumio; Kai, Kenji; Morimoto, Hiroshi
2017-09-01
There is increasing interest in finding the relation between solar activity and climate change. In general, fractal properties may be observed in the time series of the dynamics of complex systems, such as solar activity and climate. This study investigates the relations among solar activity, geomagnetic activity, and climatic regime shift by performing a multifractal analysis. To investigate the change in multifractality, we apply a wavelet transform to time series. The change in fractality of the sunspot number (SSN) correlates closely with that of the solar polar field strength. For the SSN and solar polar field strength, a weak multifractality or monofractality is present at the maximum SSN, minimum SSN, and maximum solar polar field strength. A strong multifractality is present two years before the maximum SSN. The climatic regime shift occurs when the SSN increases and the disturbance of the geomagnetic activity is large. At the climatic regime shift, the changes in the fractality of the Pacific Decadal Oscillation (PDO) index and changes in that of the solar activity indices corresponded with each other. From the fractals point of view, we clarify the relations among solar activity, geomagnetic activity, and climatic regime shift. The formation of the magnetic field of the sunspots is correlated with the solar polar field strength. The solar activity seems to influence the climatic regime shift. These findings will contribute to investigating the relation between solar activity and climate change.
Wavelet-based analysis of transient electromagnetic wave propagation in photonic crystals.
Shifman, Yair; Leviatan, Yehuda
2004-03-01
Photonic crystals and optical bandgap structures, which facilitate high-precision control of electromagnetic-field propagation, are gaining ever-increasing attention in both scientific and commercial applications. One common photonic device is the distributed Bragg reflector (DBR), which exhibits high reflectivity at certain frequencies. Analysis of the transient interaction of an electromagnetic pulse with such a device can be formulated in terms of the time-domain volume integral equation and, in turn, solved numerically with the method of moments. Owing to the frequency-dependent reflectivity of such devices, the extent of field penetration into deep layers of the device will be different depending on the frequency content of the impinging pulse. We show how this phenomenon can be exploited to reduce the number of basis functions needed for the solution. To this end, we use spatiotemporal wavelet basis functions, which possess the multiresolution property in both spatial and temporal domains. To select the dominant functions in the solution, we use an iterative impedance matrix compression (IMC) procedure, which gradually constructs and solves a compressed version of the matrix equation until the desired degree of accuracy has been achieved. Results show that when the electromagnetic pulse is reflected, the transient IMC omits basis functions defined over the last layers of the DBR, as anticipated.
The relevance of the cross-wavelet transform in the analysis of human interaction - a tutorial.
Issartel, Johann; Bardainne, Thomas; Gaillot, Philippe; Marin, Ludovic
2014-01-01
This article sheds light on a quantitative method allowing psychologists and behavioral scientists to take into account the specific characteristics emerging from the interaction between two sets of data in general and two individuals in particular. The current article outlines the practical elements of the cross-wavelet transform (CWT) method, highlighting WHY such a method is important in the analysis of time-series in psychology. The idea is (1) to bridge the gap between physical measurements classically used in physiology - neuroscience and psychology; (2) and demonstrates how the CWT method can be applied in psychology. One of the aims is to answer three important questions WHO could use this method in psychology, WHEN it is appropriate to use it (suitable type of time-series) and HOW to use it. Throughout these explanations, an example with simulated data is used. Finally, data from real life application are analyzed. This data corresponds to a rating task where the participants had to rate in real time the emotional expression of a person. The objectives of this practical example are (i) to point out how to manipulate the properties of the CWT method on real data, (ii) to show how to extract meaningful information from the results, and (iii) to provide a new way to analyze psychological attributes.
Wavelet analysis of ultrasonic A-scan signal of solid-state welded joints
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In the ultrasonic nondestructive evaluation of the quality of solid-state welded joints, such as friction bonding and diffusion bonding, the main difficulty is the identification of micro defects which are most likely to emerge in the welding process. The ultrasonic echo on the screen of a commercial ultrasonic detector due to a micro defect is so weak that it is completely masked by noise, and impossible to be pointed out. In the present paper, wavelet analysis (WA) is utilized to process A-scan ultrasonic signals from weak-bonding defects in friction bonding joints and porosity in diffusion bonding joints. First, perception of WA for engineers is given, which demonstrates the physical mechanism of WA when applied to signal processing. From this point of view, WA can be understood easily and more thoroughly. Then the signals from welding joints are decomposed into a time-scale plane by means of WA. We notice that noise and the signal echo attributed to the micro defect occupy different scales, which make it possible to enhance the signal-to-noise ratio of the signals by proper selection and threshold processing of the time-scale components of the signals, followed by reconstruction of the processed components.
Institute of Scientific and Technical Information of China (English)
Yuan TIAN; Xinxiao YU; Derong SU; Xin YANG
2007-01-01
The response of sediment discharge rate to the following four ecohydrological factors: temperature, rainfall, evapotranspiration (ET), and stream flow was evaluated by conducting wavelet analysis on Luergou small catchment data ranging from 1982 to 2000. For sediment discharge rate, there was an overall trend of reduction that included a periodic oscillation of 6 to 7 years per cycle. Rainfall also had an overall trend of reduction that included two periodic oscillations of 7 years per cycle and 2 years per cycle, respectively. Stream flow had the same trend as rainfall but had one periodic oscillation of 6 to 7 years per cycle. In contrast with rainfall and stream flow, the trends for temperature and ET each showed an overall increasing tendency, and both had the same two periodic oscillations of 6 to 7 years per cycle and 4 years per cycle, respectively. The sediment discharge rate had significant relationships with the four ecohydrological factors, with stream flow and rainfall having positive correlations, while ET and temperature had negative correlations. The correlation between ET and sediment discharge rate became stronger when ET was compared to the sediment discharge rate of the following year. The relationship between sediment discharge rate and the four ecohydrological factors was further expressed by the multi-linear regression model that was constructed, which makes sediment discharge rate a function of stream flow, rainfall, ET, and temperature.
Wavelet analysis of cerebral oxygenation oscillations in the screening of Moyamoya disease.
He, Ying; Jiang, Pengjun; Han, Shanshan; Wang, Rong; Li, Yue; Teng, Yichao; Gao, Tianxin
2014-01-01
Near-infrared spectroscopy (NIRS) was used to investigate the cerebral oxygenation of Moyamoya and healthy subjects. Continuous recordings of NIRS signals for 20 min (20 min signals) were obtained from 17 healthy subjects (age: 37.4 ± 11.3) and 17 Moyamoya subjects (age: 40.1 ± 11.2). Spectral analysis based on wavelet transformation identified five frequency intervals (I, 0.0095 Hz to 0.02 Hz; II, 0.02 Hz to 0.06 Hz; III, 0.06 Hz to 0.15 Hz; IV, 0.15 Hz to 0.40 Hz; and V, 0.40 Hz to 2.00 Hz) in the 20 min signals and three frequency intervals (III, 0.06 Hz to 0.15 Hz; IV, 0.15 Hz to 0.40 Hz; and V, 0.40 Hz to 2.00 Hz) in the 3 min signals (the first 3 min signals were continuously extracted from the 20 min signals). Significant differences (p Moyamoya disease. As a potential screening method for Moyamoya disease, the simple threshold method exhibited 73.5% accuracy.
Ben Zakour, Sihem; Taleb, Hassen
2016-06-01
Endpoint detection (EPD) is very important undertaking on the side of getting a good understanding and figuring out if a plasma etching process is done on the right way. It is truly a crucial part of supplying repeatable effects in every single wafer. When the film to be etched has been completely erased, the endpoint is reached. In order to ensure the desired device performance on the produced integrated circuit, many sensors are used to detect the endpoint, such as the optical, electrical, acoustical/vibrational, thermal, and frictional. But, except the optical sensor, the other ones show their weaknesses due to the environmental conditions which affect the exactness of reaching endpoint. Unfortunately, some exposed area to the film to be etched is very low (signal and showing the incapacity of the traditional endpoint detection method to determine the wind-up of the etch process. This work has provided a means to improve the endpoint detection sensitivity by collecting a huge numbers of full spectral data containing 1201 spectra for each run, then a new unsophisticated algorithm is proposed to select the important endpoint traces named shift endpoint trace selection (SETS). Then, a sensitivity analysis of linear methods named principal component analysis (PCA) and factor analysis (FA), and the nonlinear method called wavelet analysis (WA) for both approximation and details will be studied to compare performances of the methods mentioned above. The signal to noise ratio (SNR) is not only computed based on the main etch (ME) period but also the over etch (OE) period. Moreover, a new unused statistic for EPD, coefficient of variation (CV), is proposed to reach the endpoint in plasma etches process.
Morlet Wavelets in Quantum Mechanics
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John Ashmead
2012-11-01
Full Text Available Wavelets offer significant advantages for the analysis of problems in quantum mechanics. Because wavelets are localized in both time and frequency they avoid certain subtle but potentially fatal conceptual errors that can result from the use of plane wave or δ function decomposition. Morlet wavelets in particular are well-suited for this work: as Gaussians, they have a simple analytic form and they work well with Feynman path integrals. But to take full advantage of Morlet wavelets we need to supply an explicit form for the inverse Morlet transform and a manifestly covariant form for the four-dimensional Morlet wavelet. We construct both here.Quanta 2012; 1: 58–70.
Wavelet Based Image Denoising Technique
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Sachin D Ruikar
2011-03-01
Full Text Available This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poissonâ€™s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.
Wavelets on Planar Tesselations
Energy Technology Data Exchange (ETDEWEB)
Bertram, M.; Duchaineau, M.A.; Hamann, B.; Joy, K.I.
2000-02-25
We present a new technique for progressive approximation and compression of polygonal objects in images. Our technique uses local parameterizations defined by meshes of convex polygons in the plane. We generalize a tensor product wavelet transform to polygonal domains to perform multiresolution analysis and compression of image regions. The advantage of our technique over conventional wavelet methods is that the domain is an arbitrary tessellation rather than, for example, a uniform rectilinear grid. We expect that this technique has many applications image compression, progressive transmission, radiosity, virtual reality, and image morphing.
Trains Trouble Shooting Based on Wavelet Analysis and Joint Selection Feature Classifier
Directory of Open Access Journals (Sweden)
Bo Yu
2014-02-01
Full Text Available According to urban train running status, this paper adjusts constraints, air spring and lateral damper components running status and vibration signals of vertical acceleration of the vehicle body, combined with characteristics of urban train operation, we build an optimized train operation adjustment model and put forward corresponding estimation method-- wavelet packet energy moment, for the train state. First, we analyze characteristics of the body vertical vibration, conduct wavelet packet decomposition of signals according to different conditions and different speeds, and reconstruct the band signal which with larger energy; we introduce the hybrid ideas of particle swarm algorithm, establish fault diagnosis model and use improved particle swarm algorithm to solve this model; the algorithm also gives specific steps for solution; then calculate features of each band wavelet packet energy moment. Changes of wavelet packet energy moment with different frequency bands reflect changes of the train operation state; finally, wavelet packet energy moments with different frequency band are composed as feature vector to support vector machines for fault identification
Wavelet analysis of hemispheroid flow separation toward understanding human vocal fold pathologies
Plesniak, Daniel H.; Carr, Ian A.; Bulusu, Kartik V.; Plesniak, Michael W.
2014-11-01
Physiological flows observed in human vocal fold pathologies, such as polyps and nodules, can be modeled by flow over a wall-mounted protuberance. The experimental investigation of flow separation over a surface-mounted hemispheroid was performed using particle image velocimetry (PIV) and measurements of surface pressure in a low-speed wind tunnel. This study builds on the hypothesis that the signatures of vortical structures associated with flow separation are imprinted on the surface pressure distributions. Wavelet decomposition methods in one- and two-dimensions were utilized to elucidate the flow behavior. First, a complex Gaussian wavelet was used for the reconstruction of surface pressure time series from static pressure measurements acquired from ports upstream, downstream, and on the surface of the hemispheroid. This was followed by the application of a novel continuous wavelet transform algorithm (PIVlet 1.2) using a 2D-Ricker wavelet for coherent structure detection on instantaneous PIV-data. The goal of this study is to correlate phase shifts in surface pressure with Strouhal numbers associated with the vortex shedding. Ultimately, the wavelet-based analytical framework will be aimed at addressing pulsatile flows. This material is based in part upon work supported by the National Science Foundation under Grant Number CBET-1236351, and GW Center for Biomimetics and Bioinspired Engineering (COBRE).
S2LET: A code to perform fast wavelet analysis on the sphere
Leistedt, B; Vandergheynst, P; Wiaux, Y
2013-01-01
We describe S2LET, a fast and robust implementation of the scale-discretised wavelet transform on the sphere. Wavelets are constructed through a tiling of the harmonic line and can be used to probe spatially localised, scale-depended features of signals on the sphere. The scale-discretised wavelet transform was developed previously and reduces to the needlet transform in the axisymmetric case. The reconstruction of a signal from its wavelets coefficients is made exact here through the use of a sampling theorem on the sphere. Moreover, a multiresolution algorithm is presented to capture all information of each wavelet scale in the minimal number of samples on the sphere. In addition S2LET supports the HEALPix pixelisation scheme, in which case the transform is not exact but nevertheless achieves good numerical accuracy. The core routines of S2LET are written in C and have interfaces in Matlab, IDL and Java. Real signals can be written to and read from FITS files and plotted as Mollweide projections. The S2LET ...
DDoS detection based on wavelet kernel support vector machine
Institute of Scientific and Technical Information of China (English)
YANG Ming-hui; WANG Ru-chuan
2008-01-01
To enhance the detection accuracy and deduce false positive rate of distributed denial of service (DDoS) attack detection, a new machine learning method was proposed. With the analysis of support vector machine (SVM) and the wavelet kernel function theory, an admissive support vector kernel, which is a wavelet kernel constructed in this article, implements the combination of the wavelet technique with SVM. Then, wavelet support vector machine (WSVM) is applied to DDoS attack detections and as a classifying means to test the validity of the wavelet kernel function. Simulation experiments show that under the same conditions, the predictive ability of WSVM is improved and the computation burden is alleviated. The detection accuracy of WSVM is higher than the traditional SVM by about 4%, while its false positive is lower than the traditional SVM. Thus, for DDoS detections, WSVM shows better detection performance and is more adaptive to the changing network environment.
Directory of Open Access Journals (Sweden)
Bours Ralph
2012-08-01
Full Text Available Abstract Background Quantification of leaf movement is an important tool for characterising the effects of environmental signals and the circadian clock on plant development. Analysis of leaf movement is currently restricted by the attachment of sensors to the plant or dependent upon visible light for time-lapse photography. The study of leaf growth movement rhythms in mature plants under biological relevant conditions, e.g. diurnal light and dark conditions, is therefore problematic. Results Here we present OSCILLATOR, an affordable system for the analysis of rhythmic leaf growth movement in mature plants. The system contains three modules: (1 Infrared time-lapse imaging of growing mature plants (2 measurement of projected distances between leaf tip and plant apex (leaf tip tracking growth-curves and (3 extraction of phase, period and amplitude of leaf growth oscillations using wavelet analysis. A proof-of-principle is provided by characterising parameters of rhythmic leaf growth movement of different Arabidopsis thaliana accessions as well as of Petunia hybrida and Solanum lycopersicum plants under diurnal conditions. The amplitude of leaf oscillations correlated to published data on leaf angles, while amplitude and leaf length did not correlate, suggesting a distinct leaf growth profile for each accession. Arabidopsis mutant accession Landsberg erecta displayed a late phase (timing of peak oscillation compared to other accessions and this trait appears unrelated to the ERECTA locus. Conclusions OSCILLATOR is a low cost and easy to implement system that can accurately and reproducibly quantify rhythmic growth of mature plants for different species under diurnal light/dark cycling.
Detection of ultrasound contrast agent microbubble with constructed bubble wavelet
Institute of Scientific and Technical Information of China (English)
LI Bin; WAN Mingxi
2005-01-01
To detect the echo irradiated by microbubble out from the signal reflected by surrounding tissues, a mother wavelet named bubble wavelet according to the modified Herring oscillation equation was constructed and then applied to the original ultrasound radio frequency signal to perform the wavelet transformation. The transformed wavelet coefficients were extracted by selected threshold values to differentiate the echo of microbubble from signal of surround tissues. The effect of bubble wavelet was compared with other three commonly used mother wavelets by computer simulation and phantom experiment. The results demonstrated that there existed a highly correlation between the bubble wavelet and the experimental echo irradiated by microbubble because bubble wavelet had represented the dynamics of microbubble in advance. Furthermore, the wavelet transform results showed a better signal-noise-ratio and a sharper contrast between the echo of microbubble and the signal of surrounding tissues. Finally,constructing an overall mother wavelet library can improve the applicability and robustness of this detection method.
Frère, Julien; Göpfert, Beat; Slawinski, Jean; Tourny-Chollet, Claire
2012-04-01
This study aimed at determining the upper limb muscles coordination during a power backward giant swing (PBGS) and the recruitment pattern of motor units (MU) of co-activated muscles. The wavelet transformation (WT) was applied to the surface electromyographic (EMG) signal of eight shoulder muscles. Total gymnast's body energy and wavelet synergies extracted from the WT-EMG by using a non-negative matrix factorization were analyzed as a function of the body position angle of the gymnast. A cross-correlation analysis of the EMG patterns allowed determining two main groups of co-activated muscles. Two wavelet synergies representing the main spectral features (82% of the variance accounted for) discriminated the recruitment of MU. Although no task-group of MU was found among the muscles, it appeared that a higher proportion of fast MU was recruited within the muscles of the first group during the upper part of the PBGS. The last increase of total body energy before bar release was induced by the recruitment of the muscles of the second group but did not necessitate the recruitment of a higher proportion of fast MU. Such muscle coordination agreed with previous simulations of elements on high bar as well as the findings related to the recruitment of MU. Copyright © 2012 Elsevier B.V. All rights reserved.
He, Ling-Yun; Wen, Xing-Chun
2015-12-01
In this paper, we use a time-frequency domain technique, namely, wavelet squared coherency, to examine the associations between the trading volumes of three agricultural futures and three different forms of these futures' daily closing prices, i.e. prices, returns and volatilities, over the past several years. These agricultural futures markets are selected from China as a typical case of the emerging countries, and from the US as a representative of the developed economies. We investigate correlations and lead-lag relationships between the trading volumes and the prices to detect the predictability and efficiency of these futures markets. The results suggest that the information contained in the trading volumes of the three agricultural futures markets in China can be applied to predict the prices or returns, while that in US has extremely weak predictive power for prices or returns. We also conduct the wavelet analysis on the relationships between the volumes and returns or volatilities to examine the existence of the two "stylized facts" proposed by Karpoff [J. M. Karpoff, The relation between price changes and trading volume: A survey, J. Financ. Quant. Anal.22(1) (1987) 109-126]. Different markets in the two countries perform differently in reproducing the two stylized facts. As the wavelet tools can decode nonlinear regularities and hidden patterns behind price-volume relationship in time-frequency space, different from the conventional econometric framework, this paper offers a new perspective into the market predictability and efficiency.
An Overview on Wavelet Software Packages
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Wavelet analysis provides very powerful problem-solving tools foranalyzing, en coding, compressing, reconstructing, and modeling signals and images. The amount of wavelets-related software has been constantly multiplying. Many wavelet ana lysis tools are widely available. This overview represents a significant survey for many currently available packages. It will be of great benefit to engineers and researchers for using the toolkits and developing new software. The beginner to learning wavelets can also get a great help from the review. If you browse a round at some of the Internet sites listed in the reference of this paper, you m ay find more plentiful wavelet resources.
基于小波分析的直线拟合及应用%Based on wavelet analysis and application of linear fitting
Institute of Scientific and Technical Information of China (English)
王江荣
2011-01-01
Image analysis in the line is very important descriptor. The phenomenon of industrial control is usually used for image processing on the linear least squares fitting. Estimated with high precision for the case, the traditional least square method often can not meet the requirements. Based on this, the discrete wavelet transform and the traditional least squares method, an algorithm based on least squares estimation of wavelet pretreatment measurement of the new method, obtained much better than the traditional least squares estimation results of experiments show the validity of the method and accuracy of.%直线是图像分析中非常重要的描述符号.对工业控制中的现象进行图像处理时通常用到最小二乘法对直线拟合.对十估计精度要求较高的情形,传统最小二乘法往往不能满足要求.基于此,本文将离散小波变换和传统最小二乘法相结合,建立了一种基于小波测量预处理的最小二乘估计的新型解法,获得了比传统最小二乘法好得多的估计结果,实验证明了该方法的有效性及高精度性.
基于小波分析的最小二乘拟合及应用%Application of the Least Square Fitting Based on Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
王江荣
2012-01-01
Straight line is a very important descriptor in image analysis process. In industrial control, the least square method is commonly used for fitting of straight line in image processing. When higher accuracy of estimation is requested, traditional least square method cannot satisfy the requirement. Thus, the discrete wavelet transform is combined with traditional least square method, to establish the new type of solution based on wavelet pre-processing for the least square estimation, to acquire better estimation result than that of traditional least square method. The experiments verify the effectiveness and high precision of this method.%直线是图像分析过程中非常重要的描述符号.在工业控制中,图像处理通常采用最小二乘法对直线进行拟合,但在对估计精度要求较高时,传统最小二乘法往往不能满足要求.将离散小波变换和传统最小二乘法相结合,建立了一种基于小波预处理的最小二乘估计的新方法,获得了比传统最小二乘法效果更好的估计结果.试验证明了该方法的有效性和高精度性.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Based on wavelet packet decomposition (WPD) algorithm and Teager energy operator (TEO), a novel gearbox fault detection and diagnosis method is proposed. Its process is expatiated after the principles of WPD and TEO modulation are introduced respectively. The preprocessed signal is interpolated with the cubic spline function, then expanded over the selected basis wavelets. Grouping its wavelet packet components of the signal based on the minimum entropy criterion, the interpolated signal can be decomposed into its dominant components with nearly distinct fault frequency contents. To extract the demodulation information of each dominant component, TEO is used. The performance of the proposed method is assessed by means of several tests on vibration signals collected from the gearbox mounted on a heavy truck. It is proved that hybrid WPD-TEO method is effective and robust for detecting and diagnosing localized gearbox faults.
Wavelet approach for analysis of neutronic power using data of Ringhals stability benchmark
Energy Technology Data Exchange (ETDEWEB)
Espinosa-Paredes, Gilberto [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco, 186, Col. Vicentina, 09340 Mexico D.F. (Mexico)]. E-mail: gepe@xanum.uam.mx; Nunez-Carrera, Alejandro [Comision Nacional de Seguridad Nuclear y Salvaguardias, Dr. Barragan 779, Col. Narvarte, 03020 Mexico D.F. (Mexico); Prieto-Guerrero, Alfonso [Division de Ciencias Basicas e Ingenieria, Universidad Autonoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco, 186, Col. Vicentina, 09340 Mexico D.F. (Mexico); Cecenas, Miguel [Instituto de Investigaciones Electricas, Av. Reforma 113, Col. Palmira, 62490 Cuernavaca, Morelos (Mexico)
2007-05-15
We have studied neutronic power oscillation in a boiling water nuclear reactor for three different scenarios of the Ringhals stability benchmark with a proposed wavelets-based method: the first scenario is a stable operating state which was considered as a base case in this study, and the last two correspond to unstable operating conditions of in-phase and out-of-phase events. The results obtained with the methodology presented here suggest that a wavelet-based method can help the understanding and monitoring of the power dynamics in boiling water nuclear reactors. The stability parameters frequency and decay ratio were calculated as a function of time, based on the theory of wavelet ridges. This method allows us to analyze both stationary and highly non-stationary signals. The resonant frequencies of the oscillation are consistent with previous measurements or calculated values.
Performance Analysis of Multi Spectral Band Image Compression using Discrete Wavelet Transform
Directory of Open Access Journals (Sweden)
S. S. Ramakrishnan
2012-01-01
Full Text Available Problem statement: Efficient and effective utilization of transmission bandwidth and storage capacity have been a core area of research for remote sensing images. Hence image compression is required for multi-band satellite imagery. In addition, image quality is also an important factor after compression and reconstruction. Approach: In this investigation, the discrete wavelet transform is used to compress the Landsat5 agriculture and forestry image using various wavelets and the spectral signature graph is drawn. Results: The compressed image performance is analyzed using Compression Ratio (CR, Peak Signal to Noise Ratio (PSNR. The compressed image using dmey wavelet is selected based on its Digital Number Minimum (DNmin and Digital Number Maximum (DNmax. Then it is classified using maximum likelihood classification and the accuracy is determined using error matrix, kappa statistics and over all accuracy. Conclusion: Hence the proposed compression technique is well suited to compress the agriculture and forestry multi-band image.
Noise reduction in LOS wind velocity of Doppler lidar using discrete wavelet analysis
Institute of Scientific and Technical Information of China (English)
Songhua Wu(吴松华); Zhishen Liu(刘智深); Dapeng Sun(孙大鹏)
2003-01-01
The line of sight (LOS) wind velocity can be determined from the incoherent Doppler lidar backscattering signals. Noise and interference in the measurement greatly degrade the inversion accuracy. In this paper,we apply the discrete wavelet denoising method by using biorthogonal wavelets and adopt a distancedependent thresholds algorithm to improve the accuracy of wind velocity measurement by incoherent Doppler lidar. The noisy simulation data are processed and compared with the true LOS wind velocity.The results are compared by the evaluation of both the standard deviation and correlation coefficient.The results suggest that wavelet denoising with distance-dependent thresholds can considerably reduce the noise and interfering turbulence for wind lidar measurement.
Efficient local statistical analysis via integral histograms with discrete wavelet transform.
Lee, Teng-Yok; Shen, Han-Wei
2013-12-01
Histograms computed from local regions are commonly used in many visualization applications, and allowing the user to query histograms interactively in regions of arbitrary locations and sizes plays an important role in feature identification and tracking. Computing histograms in regions with arbitrary location and size, nevertheless, can be time consuming for large data sets since it involves expensive I/O and scan of data elements. To achieve both performance- and storage-efficient query of local histograms, we present a new algorithm called WaveletSAT, which utilizes integral histograms, an extension of the summed area tables (SAT), and discrete wavelet transform (DWT). Similar to SAT, an integral histogram is the histogram computed from the area between each grid point and the grid origin, which can be be pre-computed to support fast query. Nevertheless, because one histogram contains multiple bins, it will be very expensive to store one integral histogram at each grid point. To reduce the storage cost for large integral histograms, WaveletSAT treats the integral histograms of all grid points as multiple SATs, each of which can be converted into a sparse representation via DWT, allowing the reconstruction of axis-aligned region histograms of arbitrary sizes from a limited number of wavelet coefficients. Besides, we present an efficient wavelet transform algorithm for SATs that can operate on each grid point separately in logarithmic time complexity, which can be extended to parallel GPU-based implementation. With theoretical and empirical demonstration, we show that WaveletSAT can achieve fast preprocessing and smaller storage overhead than the conventional integral histogram approach with close query performance.
Serial identification of EEG patterns using adaptive wavelet-based analysis
Nazimov, A. I.; Pavlov, A. N.; Nazimova, A. A.; Grubov, V. V.; Koronovskii, A. A.; Sitnikova, E.; Hramov, A. E.
2013-10-01
A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.
OIL PRICES AND TRADE IN TURKEY: A WAVELET CONTINUOUS TRANSFORM ANALYSIS
Directory of Open Access Journals (Sweden)
Nuray Terzi
2016-11-01
Full Text Available Since the beginning of the Great Recession, the conceptuality of the economic literature has been going through an unprecedented change at a rate which is mind-boggling. The flaws of the DSGE model that let to its breakdown, the existence of a zero lower bound for a period that is much longer than expected, the important and intriguing models that the literature on nowcasting offers, heterodox beliefs of yesterday that became orthodox notions such as the non-linearity of all variables used in empirical analysis as well as the role of measurement errors in these variables as the main cause of continuous fluctuations have all been at the forefront of this wave of new research in economics to build robust (or at least not flawed models that are somewhat capable of explaining the nature of human behavior that has been shaped by the global technological advances which hardly has been a part of the past conventional economic analysis. Moreover, questions surrounding the models used to employ expectation formation of individuals and the shifting focus to company culture rather than just a representative agent have added additional fuel to a debate which seems to be only at its infant stages. Nonetheless, there are still important topics which are much simpler to tackle with that are left unattended by the literature among all this chaos that dominates the research and the empirical applications. One of them is the literature between the relation of oil prices and trade deficit. This paper studies the oil price-trade deficit relationship in the emerging market of Turkey, employing one of the recent unconventional methods that take into account the non-linear nature of the variables, the wavelet methodology. Our findings show that these two variables are definitely positively related and oil prices are leading the trade deficit, especially during the periods of turmoil and fluctuations.
Directory of Open Access Journals (Sweden)
VOLCHUK V. M.
2015-10-01
Full Text Available Problem statement. At present , to implement a deterministic method of assessment of the mechanical features is not possible based on the analysis of causalit links, because they are influenced with a large number of variables that are highly correlated with each other, and some part of them are changing in a wide range of unpredictable ways. Especially, this problem is in assessing the mechanical properties of metal constructions and products of special purpose in the process of their expluatation: oil pipes, carcasses of residential buildings, etc. In these cases, mechanical testing is the problem is not always technically feasible, and out of variety of express methods of non-destructive control are used often in practice in verbal or semiquantitative. The difficulty is that under the impact of various factors: temperature, corrosive environments, etc., structural changes occur far from thermodynamic equilibrium, and as result the mixed structures are got, including widmanshtatten structure. Use of classical methods of metallography is not always possible to quantify such structures with the precision that may be necessary for practical purposes. In this regard, considerable interest is the search for new approaches to assess the metal structure with a purpose of prognosis of its mechanical properties. Purpose. To obtain information about the possible application of wavelet-multifractal analysis to assess the mechanical properties of metal. Conclusion. Sensitiveness between strength properties and uniformity is set with regularity of structure elements of bainite-perlite group, and also between the viscous properties and uniformity, a regularity of element of the ferrite group. The results suggest that the realization of this method allows in the minimal and possible cost for the real tests to provide the necessary accuracy for practical purposes.
An overview on preseismic anomalies in LF radio signals revealed in Italy by wavelet analysis
Directory of Open Access Journals (Sweden)
A. Ermini
2008-06-01
Full Text Available Since 1996, the electric field strength of the two broadcasting stations MCO (f=216 kHz, southeast France and CZE (f=270 kHz, Czech Republic has been sampled every ten minutes by a receiver (AS located in central Italy. Here, we review the results obtained by a detailed analysis applied to the data recorded from February 1996 up to December 2004. At first, the daytime and nighttime data were extracted and then, in the daytime data, the data collected in winter were separated from those collected in summer. On the second step the wavelet transform was applied. The results of this analysis are radio anomalies detected as earthquake precursors both for MCO and CZE data. In particular, regarding the MCO data, the main result was the appearance of a very clear anomaly during May-August 1998, at daytime and at nighttime. Such an anomaly can be considered as a precursor of a seismic sequence started on August 15, 1998 with 17 earthquakes (M=2.2-4.6 on the Reatini mountains, a seismogenic zone located 30 km far from the AS receiver along the path MCO-AS. As concerns with the CZE data, the first result was obtained from the summer daytime data and it was the appearance of a very clear anomaly during August-September 1997, that can be considered a precursor of the two earthquakes with magnitude M=5.6 and M=5.9 that occurred on September 26 in the Umbria-Marche region (Central Italy. The second result was the appearance of an anomaly during February-March 1998, at daytime and at nighttime, that can be related to the preparatory phase of the strong (M=5.1-6.0 Slovenia seismic sequence that occurred in a zone lying in the middle of the CZE-AS path.
Institute of Scientific and Technical Information of China (English)
高云芳; 张昭; 李劲风; 曹发和; 程英亮; 张鉴清
2003-01-01
The potential noise during corrosion of pure aluminum in sodium chloride solution was recorded and analyzed with wavelet transform technique. The typical results show that the electrochemical noise (EN) signal is composed of distinct type of events, which can be classified according to their scales, i.e. time constants. And the process underlying the rapid fluctuations of EN, which is characterized by a small scaling value, i.e. high-frequency components and which is usually used for local analysis, is not consistant with time; whilst those associated with slower processes or characterized by a large scaling value, which are usually used for global analysis, are continuous.
Directional spin wavelets on the sphere
McEwen, Jason D; Büttner, Martin; Peiris, Hiranya V; Wiaux, Yves
2015-01-01
We construct a directional spin wavelet framework on the sphere by generalising the scalar scale-discretised wavelet transform to signals of arbitrary spin. The resulting framework is the only wavelet framework defined natively on the sphere that is able to probe the directional intensity of spin signals. Furthermore, directional spin scale-discretised wavelets support the exact synthesis of a signal on the sphere from its wavelet coefficients and satisfy excellent localisation and uncorrelation properties. Consequently, directional spin scale-discretised wavelets are likely to be of use in a wide range of applications and in particular for the analysis of the polarisation of the cosmic microwave background (CMB). We develop new algorithms to compute (scalar and spin) forward and inverse wavelet transforms exactly and efficiently for very large data-sets containing tens of millions of samples on the sphere. By leveraging a novel sampling theorem on the rotation group developed in a companion article, only hal...
Xu, Chang
2016-08-01
We adopt the cross wavelet transform (XWT) to examine the potential geophysical contributors of seasonal oscillations in GPS observations. Daily vertical GPS position time series and mass loadings [atmospheric, oceanic, and hydrological loading (AOH)] of 30 globally distributed GPS sites, spanning from January 2002 to December 2014, are used to quantify the performance. First, we examine the spectra of GPS time series and AOH. The results confirm the anomalous sub-seasonal peaks in GPS spectra are seen to have not an obvious geophysical explanation. The Akaike information criteria is then used to quantify how well the noise models fit the two series. The Generalized Gauss Markov plus white noise (GGM + WH) model is in most cases the preferred noise model for GPS, and the fifth order autoregressive plus white noise (AR(5) + WH) model is the preferred noise model for AOH. Second, we test the significance of periodic oscillations in GPS residuals and AOH. We find both series have significantly high power located near one cycle per year frequency band, whereas harmonic signals at higher draconitic frequency are identified as non-white process. Finally, we adopt XWT to examine the relative phasing between the two series, and find the annual variations in two series are physically related for most sites. The time variable phase asynchrony obtained using the XWT-based semblance analysis confirms that the annual variations in GPS observations are resulting from a combination of geophysical signals and systematic errors. The weighted least squares fitting method where the covariance matrix follows a specific stochastic noise model is also performed for comparison.
Institute of Scientific and Technical Information of China (English)
JunJun Yang; ZhiBin He; WeiJun Zhao; Jun Du; LongFei Chen; Xi Zhu
2016-01-01
Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation and climate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributed hydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. In this study, a newly-developed technique of coupled (WA-ANN) wavelet analysis (WA) and artificial neural network (ANN) was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province, China. Datasets included seven meteorological factors: air and land surface temperatures, relative humidity, global radiation, atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate the effectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were: (1) ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepest soil layer studied (NSE >0.85, NSE means Nash-Sutcliffe Efficiency coefficient); (2) when input meteorological factors were transformed using maximum signal to noise ratio (SNR) and one-dimensional auto de-noising algorithm (heursure) in WA, the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; (3) the results of multi-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this can be used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. We conclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimum distortion of the raw-time series; the new method used here is applicable to soil sciences and management applications.
Wavelet analysis of near-inertial currents at the East Flower Garden Bank
Teague, W. J.; Wijesekera, H. W.; Jarosz, E.; Lugo-Fernández, A.; Hallock, Z. R.
2014-10-01
Near-inertial currents (NICs) often dominate the mean circulation at the East Flower Garden Bank (EFGB), part of the Flower Garden Banks National Marine Sanctuary. The EFGB, one of several submerged coral reefs, is located in the northwestern Gulf of Mexico, about 190 km southeast of Galveston, Texas. The bank is about 6 km wide in the east-west direction and rises to within about 20 m from the surface. NICs near the EFGB are described using current data from 5 acoustic Doppler current profilers that were moored at the edges of the bank and on top of the bank for about a year. A wavelet analysis was used in order to better describe the nonstationarity of the NICs. NICs were strongest during spring and summer due to their near resonant response with sea breeze and the shallowness of the mixed layer, and exhibited a first-baroclinic-mode vertical structure. NICS were generally larger near the surface and extended to the bottom on the west side of the EFGB but only to within about 20 m of the bottom on the eastern side of the bank. NIC ellipses were nearly circular and rotated clockwise above the top of the EFGB but became flatter and aligned with the bathymetry with increasing depth; occasionally, on the eastern side of the bank, the NIC vectors rotated counterclockwise due to probable effects of lee vortices arising from the mean flow interacting with the bank. Most energy input by the wind at the surface was likely transferred downward through divergence of the meridional flow against the coastal boundary. The inertial currents were at times more energetic than the mean flow, and often accounted for more than 50% of the total current energy.
WAVEPAL: A Software for Frequency and Wavelet Analysis of Irregularly Sampled Time Series
Lenoir, Guillaume; Crucifix, Michel
2017-04-01
WAVEPAL is based on a general theory that we have developed for the frequency and wavelet analysis of irregularly sampled time series. It is based on the Lomb-Scargle periodogram, that we extend to algebraic operators accommodating for the presence of a polynomial trend in the model for the data, in addition to the periodic component and the background noise. Special care is devoted to the correlation between the trend and the periodic component. This new tool is then cast into the formalism of the Welch overlapping segment averaging (WOSA) method, which is used to reduce the variance of the periodogram/scalogram. We also design a test of significance against a background noise which is a continuous autoregressive-moving-average (CARMA) Gaussian process. This widens the traditional choice of a Gaussian white or red noise process as the background noise. Estimation of CARMA parameters is performed in a Bayesian framework and relies on state of the art algorithms. We then provide algorithms computing the confidence levels for the periodogram/scalogram that fully take into account the uncertainty on the CARMA noise parameters. Alternatively, if one opts for the traditional choice of a unique set of parameters for the CARMA background noise, we develop a theory providing analytical confidence levels, which are more accurate than Markov chain Monte Carlo (MCMC) confidence levels and, below some threshold for the number of data points, less costly in computing time. We then estimate the amplitude of the periodic component with least squares methods, and derive an approximate proportionality between the squared amplitude and the periodogram/scalogram. The estimated signal amplitude also gives access to ridge filtering or filtering in a frequency band. Our results generalize and unify methods developed in the fields of geosciences, engineering, astronomy and astrophysics. WAVEPAL is written in python2.X and is available at https://github.com/guillaumelenoir/WAVEPAL
Energy Technology Data Exchange (ETDEWEB)
Wilczek, M; Friedrich, R [Institute for Theoretical Physics, University of Muenster, Wilhelm-Klemm-Str. 9, 48149 Muenster (Germany); Kadoch, B [Aix-Marseille Universite and M2P2-CNRS Ecole Centrale de Marseille, 38 Rue Joliot-Curie, 13451 Marseille Cedex 20 (France); Schneider, K [M2P2-CNRS and CMI, Universite de Provence, 39 Rue Joliot-Curie, 13453 Marseille Cedex 13 (France); Farge, M, E-mail: mwilczek@uni-muenster.de [LMD-CNRS, Ecole Normale Superieure, 24 Rue Lhomond, 75231 Paris Cedex 5 (France)
2011-12-22
We study the conditional balance of vortex stretching and vorticity diffusion of fully developed three-dimensional homogeneous isotropic turbulence with respect to coherent and incoherent flow contributions. This decomposition is achieved by the Coherent Vorticity Extraction based on orthogonal wavelets applied to DNS data, which yields insights into the influence of the different contributions as well as their interaction.
Investigation of cosmic ray penetration with wavelet cross-correlation analysis
Yang, Rui-zhi
2016-01-01
Aims. We use Fermi and Planck data to calculate the cross correlation between gamma ray signal and gas distribution in different scales in giant molecular clouds (GMC). Then we investigate the cosmic rays (CRs) penetration in GMCs with these informations. Methods.We use the wavelet technique to decompose both the gamma ray and dust opacity maps in different scales, then we calculate the wavelet cross correlation functions in these scales. We also define wavelet response as an analog to the impulsive response in Fourier transform and calculate that in different scales down to Fermi angular resolution. Results. The gamma ray maps above 2 GeV show strong correlation with the dust opacity maps, the correlation coefficient is larger than 0.9 above a scale of 0.4 degree.The derived wavelet response is uniform in different scales. Conclusions. We argue that the CR above 10 GeV can penetrate the giant molecular cloud freely and the CRs distributions in the same energy range are uniform down to parsec scale.
Analysis of embolic signals with directional dual tree rational dilation wavelet transform.
Serbes, Gorkem; Aydin, Nizamettin; Serbes, Gorkem; Aydin, Nizamettin; Aydin, Nizamettin; Serbes, Gorkem
2016-08-01
The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT). The DDT-RADWT, which can be directly applied to quadrature signals, is based on rational dilation factors and has adjustable frequency resolution. The analyses are done for both low and high Q-factors. It is proved that, when high Q-factor filters are employed in the DDT-RADWT, clearer representations of ESs can be attained in decomposed sub-bands and artifacts can be successfully separated.
基于小波分析的故障诊断方法研究%A fault diagnosis method base on wavelet analysis
Institute of Scientific and Technical Information of China (English)
史胜东; 柳勇; 阮竹青
2014-01-01
本文基于小波多分辨率的柴油机气阀漏气故障诊断方法研究，并在柴油机台架进行故障模拟试验，用基于小波多分辨率和功率谱密度分析方法对模拟试验采集的振动信号进行分析处理，提取气阀是否漏气的故障信息。%The experiments of the valve leakage (four states) simulated on diesel engine test bench were carried out. The vibration signals were measured, processed and analyzed by using Wavelet analysis and Power spectral density function. The fault information of the valve leakage is abstracted.
Wavelets and multiscale signal processing
Cohen, Albert
1995-01-01
Since their appearance in mid-1980s, wavelets and, more generally, multiscale methods have become powerful tools in mathematical analysis and in applications to numerical analysis and signal processing. This book is based on "Ondelettes et Traitement Numerique du Signal" by Albert Cohen. It has been translated from French by Robert D. Ryan and extensively updated by both Cohen and Ryan. It studies the existing relations between filter banks and wavelet decompositions and shows how these relations can be exploited in the context of digital signal processing. Throughout, the book concentrates on the fundamentals. It begins with a chapter on the concept of multiresolution analysis, which contains complete proofs of the basic results. The description of filter banks that are related to wavelet bases is elaborated in both the orthogonal case (Chapter 2), and in the biorthogonal case (Chapter 4). The regularity of wavelets, how this is related to the properties of the filters and the importance of regularity for t...
Spherical 3D isotropic wavelets
Lanusse, F.; Rassat, A.; Starck, J.-L.
2012-04-01
Context. Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D spherical Fourier-Bessel (SFB) analysis in spherical coordinates is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. Aims: The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. Methods: We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. (2006). We also present a new fast discrete spherical Fourier-Bessel transform (DSFBT) based on both a discrete Bessel transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and find we can successfully remove noise without much loss to the large scale structure. Results: We have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel DSFBT. We illustrate its potential use for denoising using a toy model. All the algorithms presented in this paper are available for download as a public code called MRS3D at http://jstarck.free.fr/mrs3d.html
Chen, H. X.; Chua, Patrick S. K.; Lim, G. H.
2008-10-01
The machinery fault diagnosis is important for improving reliability and performance of systems. Many methods such as Time Synchronous Average (TSA), Fast Fourier Transform (FFT)-based spectrum analysis and short-time Fourier transform (STFT) have been applied in fault diagnosis and condition monitoring of mechanical system. The above methods analyze the signal in frequency domain with low resolution, which is not suitable for non-stationary vibration signal. The Kolmogorov-Smirnov (KS) test is a simple and precise technique in vibration signal analysis for machinery fault diagnosis. It has limited use and advantage to analyze the vibration signal with higher noise directly. In this paper, a new method for the fault degradation assessment of the water hydraulic motor is proposed based on Wavelet Packet Analysis (WPA) and KS test to analyze the impulsive energy of the vibration signal, which is used to detect the piston condition of water hydraulic motor. WPA is used to analyze the impulsive vibration signal from the casing of the water hydraulic motor to obtain the impulsive energy. The impulsive energy of the vibration signal can be obtained by the multi-decomposition based on Wavelet Packet Transform (WPT) and used as feature values to assess the fault degradation of the pistons. The kurtosis of the impulsive energy in the reconstructed signal from the Wavelet Packet coefficients is used to extract the feature values of the impulse energy by calculating the coefficients of the WPT multi-decomposition. The KS test is used to compare the kurtosis of the impulse energy of the vibration signal statistically under the different piston conditions. The results show the applicability and effectiveness of the proposed method to assess the fault degradation of the pistons in the water hydraulic motor.
Wavelet transform analysis of the small-scale X-ray structure of the cluster Abell 1367
Grebeney, S. A.; Forman, W.; Jones, C.; Murray, S.
1995-01-01
We have developed a new technique based on a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies. We apply this technique to the ROSAT position sensitive proportional counter (PSPC) and Einstein high-resolution imager (HRI) images of the central region of the cluster Abell 1367 to detect sources embedded within the diffuse intracluster medium. In addition to detecting sources and determining their fluxes and positions, we show that the wavelet analysis allows a characterization of the sources extents. In particular, the wavelet scale at which a given source achieves a maximum signal-to-noise ratio in the wavelet images provides an estimate of the angular extent of the source. To account for the widely varying point response of the ROSAT PSPC as a function of off-axis angle requires a quantitative measurement of the source size and a comparison to a calibration derived from the analysis of a Deep Survey image. Therefore, we assume that each source could be described as an isotropic two-dimensional Gaussian and used the wavelet amplitudes, at different scales, to determine the equivalent Gaussian Full Width Half-Maximum (FWHM) (and its uncertainty) appropriate for each source. In our analysis of the ROSAT PSPC image, we detect 31 X-ray sources above the diffuse cluster emission (within a radius of 24 min), 16 of which are apparently associated with cluster galaxies and two with serendipitous, background quasars. We find that the angular extents of 11 sources exceed the nominal width of the PSPC point-spread function. Four of these extended sources were previously detected by Bechtold et al. (1983) as 1 sec scale features using the Einstein HRI. The same wavelet analysis technique was applied to the Einstein HRI image. We detect 28 sources in the HRI image, of which nine are extended. Eight of the extended sources correspond to sources previously detected by Bechtold et al. Overall, using both the
空间数据Kriging预测的小波分析方法%Wavelets Analysis on Kriging Spatial Prediction
Institute of Scientific and Technical Information of China (English)
陈励; 马煜
2001-01-01
This paper draws into wavelets analysis to consider spatial model, using biorthogonal sequence to decompose spatial model. We get the Kriging prediction through soft-shreshold estimate. It is practical. We also get a excellent decomposition of spatial model for the further research.%用小波方法处理在空间上相关的数据模型。通过小波压缩估计,利用双正交小波作最佳线性空间预测Kriging估计,获得了Kriging估计的非线性小波预测。
Kim, Won Hwa; Chung, Moo K; Singh, Vikas
2013-01-01
The analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.
Wavelet and wavelet packet compression of electrocardiograms.
Hilton, M L
1997-05-01
Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.
Wavelet transform of neural spike trains
Kim, Youngtae; Jung, Min Whan; Kim, Yunbok
2000-02-01
Wavelet transform of neural spike trains recorded with a tetrode in the rat primary somatosensory cortex is described. Continuous wavelet transform (CWT) of the spike train clearly shows singularities hidden in the noisy or chaotic spike trains. A multiresolution analysis of the spike train is also carried out using discrete wavelet transform (DWT) for denoising and approximating at different time scales. Results suggest that this multiscale shape analysis can be a useful tool for classifying the spike trains.
Topology in galaxy distributions: method for a multi-scale analysis. A use of the wavelet transform.
Escalera, E.; MacGillivray, H. T.
1995-06-01
We report the 2D analysis of distributions of galaxies in a search for structures on all scales, from groups up to superclusters (including the identification of voids), based on the use of the wavelet transform. The wavelet method is an objective, multi-scale technique which gives the position, dimension and probability for each individual feature (both structures and voids) detected. We are currently performing the analysis on data from the COSMOS/UKST Southern Sky Galaxy Catalogue. The subsample used in our investigation contains some 2.5x10^6^ galaxies in an area of ~140x45 degrees around the South Galactic Pole. This is the first search for multi-scale objects on such an extended database, allowing us to cover many related topics in present-day Cosmology: realisation of superclusters as large-scale entities in their own right (as opposed to being considered merely as regions of enhanced cluster numbers); improvement in the definition of clusters of galaxies with a new approach to their general behaviour (distribution, typical sizes, state of evolution, etc.); and the objective characterisation of voids, which is exclusive to the wavelet method. In the present paper, we demonstrate the power of the technique by applying it to a selected field covering approximately 3000deg^2^ in the Grus-Sculptor region. In this area, we find 7 large scale structures (of more than 5 degrees in extent) and 26 structures of smaller scales (cluster-sized down to 1 degree, or group-sized down to 0.5 degrees). Sixteen of these small scale aggregates are connected with the large scale structures while ten appear isolated in the field. All these features are significant, having high confidence levels for detection. Voids are also detected in this area, likewise with high significance levels.
Anastasiadou, Maria N; Christodoulakis, Manolis; Papathanasiou, Eleftherios S; Papacostas, Savvas S; Mitsis, Georgios D
2017-09-01
This paper proposes supervised and unsupervised algorithms for automatic muscle artifact detection and removal from long-term EEG recordings, which combine canonical correlation analysis (CCA) and wavelets with random forests (RF). The proposed algorithms first perform CCA and continuous wavelet transform of the canonical components to generate a number of features which include component autocorrelation values and wavelet coefficient magnitude values. A subset of the most important features is subsequently selected using RF and labelled observations (supervised case) or synthetic data constructed from the original observations (unsupervised case). The proposed algorithms are evaluated using realistic simulation data as well as 30min epochs of non-invasive EEG recordings obtained from ten patients with epilepsy. We assessed the performance of the proposed algorithms using classification performance and goodness-of-fit values for noisy and noise-free signal windows. In the simulation study, where the ground truth was known, the proposed algorithms yielded almost perfect performance. In the case of experimental data, where expert marking was performed, the results suggest that both the supervised and unsupervised algorithm versions were able to remove artifacts without affecting noise-free channels considerably, outperforming standard CCA, independent component analysis (ICA) and Lagged Auto-Mutual Information Clustering (LAMIC). The proposed algorithms achieved excellent performance for both simulation and experimental data. Importantly, for the first time to our knowledge, we were able to perform entirely unsupervised artifact removal, i.e. without using already marked noisy data segments, achieving performance that is comparable to the supervised case. Overall, the results suggest that the proposed algorithms yield significant future potential for improving EEG signal quality in research or clinical settings without the need for marking by expert
Savary, M.; Massei, N.; Johannet, A.; Dupont, J. P.; Hauchard, E.
2016-12-01
25% of the world populations drink water extracted from karst aquifer. The comprehension and the protection of these aquifers appear as crucial due to an increase of drinking water needs. In Normandie(North-West of France), the principal exploited aquifer is the chalk aquifer. The chalk aquifer highly karstified is an important water resource, regionally speaking. Connections between surface and underground waters thanks to karstification imply turbidity that decreases water quality. Both numerous parameters and phenomenons, and the non-linearity of the rainfall/turbidity relation influence the turbidity causing difficulties to model and forecast turbidity peaks. In this context, the Yport pumping well provides half of Le Havreconurbation drinking water supply (236 000 inhabitants). The aim of this work is thus to perform prediction of the turbidity peaks in order to help pumping well managers to decrease the impact of turbidity on water treatment. Database consists in hourly rainfalls coming from six rain gauges located on the alimentation basin since 2009 and hourly turbidity since 1993. Because of the lack of accurate physical description of the karst system and its surface basin, the systemic paradigm is chosen and a black box model: a neural network model is chosen. In a first step, correlation analyses are used to design the original model architecture by identifying the relation between output and input. The following optimization phases bring us four different architectures. These models were experimented to forecast 12h ahead turbidity and threshold surpassing. The first model is a simple multilayer perceptron. The second is a two-branches model designed to better represent the fast (rainfall) and low (evapotranspiration) dynamics. Each kind of model is developed using both a recurrent and feed-forward architecture. This work highlights that feed-forward multilayer perceptron is better to predict turbidity peaks when feed-forward two-branches model is
Komorowski, Dariusz; Pietraszek, Stanislaw
2016-01-01
This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal.
Optimization of dynamic measurement of receptor kinetics by wavelet denoising.
Alpert, Nathaniel M; Reilhac, Anthonin; Chio, Tat C; Selesnick, Ivan
2006-04-01
The most important technical limitation affecting dynamic measurements with PET is low signal-to-noise ratio (SNR). Several reports have suggested that wavelet processing of receptor kinetic data in the human brain can improve the SNR of parametric images of binding potential (BP). However, it is difficult to fully assess these reports because objective standards have not been developed to measure the tradeoff between accuracy (e.g. degradation of resolution) and precision. This paper employs a realistic simulation method that includes all major elements affecting image formation. The simulation was used to derive an ensemble of dynamic PET ligand (11C-raclopride) experiments that was subjected to wavelet processing. A method for optimizing wavelet denoising is presented and used to analyze the simulated experiments. Using optimized wavelet denoising, SNR of the four-dimensional PET data increased by about a factor of two and SNR of three-dimensional BP maps increased by about a factor of 1.5. Analysis of the difference between the processed and unprocessed means for the 4D concentration data showed that more than 80% of voxels in the ensemble mean of the wavelet processed data deviated by less than 3%. These results show that a 1.5x increase in SNR can be achieved with little degradation of resolution. This corresponds to injecting about twice the radioactivity, a maneuver that is not possible in human studies without saturating the PET camera and/or exposing the subject to more than permitted radioactivity.
Institute of Scientific and Technical Information of China (English)
ZHANG Xinming; HE Yongyong; HAO Rujiang; CHU Fulei
2007-01-01
Morlet wavelet is suitable to extract the impulse components of mechanical fault signals.And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm, an optimization strategy for the waveform parameters of the mother wavelet is proposed with wavelet entropy as the optimization target. Based on the optimized waveform parameters, the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing.The results indicate that the proposed method is useful and efficient to improve the quality of CWT.
Shirzaei, M.; Bürgmann, R.; Foster, J.; Walter, T. R.; Brooks, B. A.
2013-08-01
The Hilina Fault System (HFS) is located on the south flank of Kilauea volcano and is thought to represent the surface expression of an unstable edifice sector that is active during seismic events such as the 1975 Kalapana earthquake. Despite its potential for hazardous landsliding and associated tsunamis, no fault activity has yet been detected by means of modern geodetic methods, since the 1975 earthquake. We present evidence from individual SAR interferograms, as well as cluster analysis and wavelet analysis of GPS and InSAR time series, which suggest an inferred differential motion at HFS. To investigate the effect of atmospheric delay on the observed differential motion, we implement a statistical approach using wavelet transforms. We jointly analyze InSAR and continuous GPS deformation data from 2003 to 2010, to estimate the likelihood that the subtle time-dependent deformation signal about the HFS scarps is not associated with the atmospheric delay. This integrated analysis reveals localized deformation components in the InSAR deformation time series that are superimposed on the coherent motion of Kilauea's south flank. The statistical test suggests that at 95% confidence level, the identified differential deformation at HFS is not due to atmospheric artifacts. Since no significant shallow seismicity is observed over the study period, we suggest that this deformation occurred aseismically.
Directory of Open Access Journals (Sweden)
HariprasadNagarajan
2013-01-01
Full Text Available Multiple Input Multiple Output (MIMO and Orthogonal Frequency Division Multiplexing (OFDM are the two assuring technologies that offers high data rate as required for the 4G wireless systems. Conventionally OFDM is Fast Fourier Transform (FFT based system. It uses IFFT (Inverse FFT blocks in the transmitter and FFT blocks in the receiver. OFDM combined with MIMO gives increased throughput and better system performance and hence FFT based MIMO OFDM systems are widely used in 4G wireless schemes. Recent researches shows that replacing the FFT with Discrete Wavelet Transform (DWT the system performance can be further improved. This leads to a new scenario DWT based MIMO OFDM system. In this study one such system is simulated and the Bit Error Rate (BER performance of the system is analysed for the different types of wavelets under different channel environments.
Heart Disease Detection Using Wavelets
González S., A.; Acosta P., J. L.; Sandoval M., M.
2004-09-01
We develop a wavelet based method to obtain standardized gray-scale chart of both healthy hearts and of hearts suffering left ventricular hypertrophy. The hypothesis that early bad functioning of heart can be detected must be tested by comparing the wavelet analysis of the corresponding ECD with the limit cases. Several important parameters shall be taken into account such as age, sex and electrolytic changes.
The Application of Wavelet Transform in Analysis of Digital Precursory Observational Data
Institute of Scientific and Technical Information of China (English)
Song Zhiping; Wu Anxu; Wang Wei; Geng Jie; Song Xianyue; Ni Youzhong; Zhu Jiamiao; Kan Daoling
2004-01-01
Digital data of precursors is noted for its high accuracy. Therefore, it is important to extract the high frequency information from the low ones in the digital data of precursors and to discriminate between the trend anomalies and the short-term anomalies. This paper presents a method to separate the high frequency information from the low ones by using the wavelet transform to analyze the digital data of precursors, and illustrates with examples the train of thoughts of discriminating the short-term anomalies from trend anomalies by using the wavelet transform, thus provide a new effective approach for extracting the short-term and trend anomalies from the digital data of precursors.
Garces Correa, Agustina; Laciar Leber, Eric
2010-01-01
An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency, the Total Energy of the Spectrum, the Power of Theta and Alpha bands. In the Wavelet Domain, it was computed the number of Zero Crossing and the integrated from the scale 3, 4 and 5 of Daubechies 2 order WT. The classifying of epochs is being done with neural networks. The detection results obtained with this technique are 86.5 % for drowsiness stages and 81.7% for alertness segment. Those results show that the features extracted and the classifier are able to identify drowsiness EEG segments.
Institute of Scientific and Technical Information of China (English)
JIANG Nan; ZHANG Jin
2005-01-01
@@ Multi-scale decomposition by wavelet transform has been performed to velocity time sequences obtained by fine measurements of turbulent boundary layer flow. A conditional sampling technique for detecting multi-scale coherent eddy structures in turbulent field is proposed by using multi-scale instantaneous intensity factor and flatness factor of wavelet coefficients. Although the number of coherent eddy structures in the turbulent boundary layer is very small, their energy percentage with respect to the turbulence kinetic energy is high. Especially in buffer layer, the energy percentages of coherent structures are significantly higher than those in the logarithmic layer, indicating that the buffer layer is the most active region in the turbulent boundary layer. These multi-scale coherent eddy structures share some common dynamical characteristics and are responsible for the anomalous scaling law in the turbulent boundary layer.
Weld Defect Extraction Based on Adaptive Morphology Filtering and Edge Detection by Wavelet Analysis
Institute of Scientific and Technical Information of China (English)
WANGDonghua; ZHOUYuanhua; GANGTie
2003-01-01
One of the most key steps in X-ray au-tomatic inspection and intelligent recognition systems is how to extract defects and detect their edges effectively.In this paper, a novel method of defect extraction based on the adaptive morphology filtering (DEAMF) is pro-posed, whose structuring elements can be changed with the sizes of defects adaptively. By this method, defects in X-ray weld inspection images are extracted with well-kept shapes and high speeds. Then according to the theory of edge detection based on wavelet transform modulus max-ima, a locally supported wavelet with good antisymmetry is developed to extract edges of defects and the results are satisfying.
A Comparative Analysis of Exemplar Based and Wavelet Based Inpainting Technique
Directory of Open Access Journals (Sweden)
Vaibhav V Nalawade
2012-06-01
Full Text Available Image inpainting is the process of filling in of missing region so as to preserve its overall continuity. Image inpainting is manipulation and modification of an image in a form that is not easily detected. Digital image inpainting is relatively new area of research, but numerous and different approaches to tackle the inpainting problem have been proposed since the concept was first introduced. This paper compares two separate techniques viz, Exemplar based inpainting technique and Wavelet based inpainting technique, each portraying a different set of characteristics. The algorithms analyzed under exemplar technique are large object removal by exemplar based inpainting technique (Criminisi’s and modified exemplar (Cheng. The algorithm analyzed under wavelet is Chen’s visual image inpainting method. A number of examples on real and synthetic images are demonstrated to compare the results of different algorithms using both qualitative and quantitative parameters.
Zielinski, B.; Patorski, K.
2010-06-01
The aim of this paper is to analyze 2D fringe pattern denoising performed by two chosen methods based on quasi-1D two-arm spin filter and 2D discrete wavelet transform (DWT) signal decomposition and thresholding. The ultimate aim of this comparison is to estimate which algorithm is better suited for high-accuracy measurements by phase shifting interferometry (PSI) with the phase step evaluation using the lattice site approach. The spin filtering method proposed by Yu et al. (1994) was designed to minimize possible fringe blur and distortion. The 2D DWT also presents such features due to a lossless nature of the signal wavelet decomposition. To compare both methods, a special 2D histogram introduced by Gutman and Weber (1998) is used to evaluate intensity errors introduced by each of the presented algorithms.
Application of wavelet analysis to fault diagnosis of angular measuring system
Institute of Scientific and Technical Information of China (English)
邓辉宇; 苏宝库; 邹明杰
2003-01-01
For fault diagnosis, signal singularity and irregularity discontinuity fraction are very significant characteristics of signal. The discontinuity of output signal represents a system fault . In an angular measuring system, function transformer uses two D/A convertors, output circuit fault of a D/A convertor brings about discontinuity of one phase input voltage amplitude of inductosyn, results in a system error exceeding the allowable error and reduces the system accuracy. This is the reason why discontinuity is detected. Fourier transform has no resolution ability in angular-domain, but wavelet can analyse signal in angular and frequency-domains. So we decompose the error signal of angular measuring system by wavelet, detect the signal singularity at high frequency layer and find out the accurate position of it.
An Analysis of 2D Bi-Orthogonal Wavelet Transform Based On Fixed Point Approximation
Directory of Open Access Journals (Sweden)
P. Vijayalakshmi
2014-04-01
Full Text Available As the world advances with technology and research, images are being widely used in many fields such as biometrics, remote sensing, reconstruction etc. This tremendous growth in image processing applications, demands majorly for low power consumption, low cost and small chip area. In this paper we analyzed 2D bi-orthogonal wavelet transform based on Fixed point approximation. Filter coefficients of the bi-orthogonal wavelet filters are quantized before implementation. The efficiency of the results is measured for some standard gray scale images by comparing the original input images and the reconstructed images. SNR and PSNR value shows that this implementation is performed effectively without any loss in image quality.
3-D surface profilometry based on modulation measurement by applying wavelet transform method
Zhong, Min; Chen, Feng; Xiao, Chao; Wei, Yongchao
2017-01-01
A new analysis of 3-D surface profilometry based on modulation measurement technique by the application of Wavelet Transform method is proposed. As a tool excelling for its multi-resolution and localization in the time and frequency domains, Wavelet Transform method with good localized time-frequency analysis ability and effective de-noizing capacity can extract the modulation distribution more accurately than Fourier Transform method. Especially for the analysis of complex object, more details of the measured object can be well remained. In this paper, the theoretical derivation of Wavelet Transform method that obtains the modulation values from a captured fringe pattern is given. Both computer simulation and elementary experiment are used to show the validity of the proposed method by making a comparison with the results of Fourier Transform method. The results show that the Wavelet Transform method has a better performance than the Fourier Transform method in modulation values retrieval.
Energy Technology Data Exchange (ETDEWEB)
Bartosch, T. [Erlanger-Nuernberg Univ., Erlanger (Germany). Lehrstul fuer Nachrichtentechnik I; Seidl, D. [Seismologisches Zentralobservatorium Graefenberg, Erlanegen (Greece). Bundesanstalt fuer Geiwissenschaften und Rohstoffe
1999-06-01
Among a variety of spectrogram methods short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were selected to analyse transients in non-stationary signals. Depending on the properties of the tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli (Italy).
Dando, B.; Simons, F. J.; Allen, R. M.
2006-12-01
Earthquake early warning systems save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with unmatched accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave. The curious observation that such brief segments of the seismogram may contain information about the final magnitude even of very large earthquakes, which occur on faults that may rupture over tens of seconds, is central to a debate in the seismological community which we hope to stimulate but cannot attempt to address within the scope of this paper. Wavelet coefficients of the seismogram can be determined extremely rapidly and efficiently by the fast lifting wavelet transform. Extracting amplitudes at individual scales is a very simple procedure, involving a mere handful of lines of computer code. Scale-dependent thresholded amplitudes derived from the wavelet transform of the first 3--4 seconds of an incoming seismic P arrival are predictive of earthquake magnitude, with errors of one magnitude unit for seismograms recorded up to 150 km away from the earthquake source. Our procedure is a simple yet extremely efficient tool for implementation on low-power recording stations. It provides an accurate and precise method of autonomously detecting the incoming P wave and predicting the magnitude of the source from the scale-dependent character of its amplitude well before the arrival of damaging ground motion. Provided a dense array of networked seismometers exists, our procedure should become the tool of choice for earthquake early warning systems worldwide.
Construction of Time-Dependent Spectra Using Wavelet Analysis for Determination of Global Damage
DEFF Research Database (Denmark)
Micaletti, R. C.; Cakmak, A. S.; Nielsen, Søren R.K.;
A new method for computing Maximum Softening Damage Index (MSDI) is proposed. The MSDI, a measure of global damage, is based on the relative reduction of the first eigenfrequency (or equivalently, the relative increase in the fundamental period) of a structure over the course of a damage event. T....... The method proposed here makes use of wavelet transform coefficients of measured output response records to provide time-localized information on structural softening....
Directory of Open Access Journals (Sweden)
Triwiyanto Triwiyanto
2017-01-01
Full Text Available Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG signals using the Continuous Wavelet Transform (CWT. Four male participants were recruited to perform a repetitive motion (flexion and extension movements from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS and the Scale-Average Wavelet Power (SAWP parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF and the Instantaneous Mean Power Spectrum (IMNP parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1 and 0.0095mV^2Hz^(-1 for R^2 and slope, respectively.
High-Accuracy Methods for Numerical Flow Analysis Using Adaptive Non-Linear Wavelets
2012-08-01
to the research by Bacry, Mallat and Papanicolaou [10] or Holmström and Walden [11], AWGM solves PDE problems in a wavelet coefficient space. It is...of the threshold value, these variations are discarded and restricted by multiplying the weighting factor . This process can especially contribute the...weighting factor . This restriction technique enhances the convergence rate of steady state calculations. References [1] Harten A., “High
WAVELET KERNEL SUPPORT VECTOR MACHINES FOR SPARSE APPROXIMATION
Institute of Scientific and Technical Information of China (English)
Tong Yubing; Yang Dongkai; Zhang Qishan
2006-01-01
Wavelet, a powerful tool for signal processing, can be used to approximate the target function. For enhancing the sparse property of wavelet approximation, a new algorithm was proposed by using wavelet kernel Support Vector Machines (SVM), which can converge to minimum error with better sparsity. Here, wavelet functions would be firstly used to construct the admitted kernel for SVM according to Mercy theory; then new SVM with this kernel can be used to approximate the target funciton with better sparsity than wavelet approxiamtion itself. The results obtained by our simulation experiment show the feasibility and validity of wavelet kernel support vector machines.
Borisov, A A; Bruevich, V V; Rozgacheva, I K; Shimanovskaya, E V
2015-01-01
We applied the method of continuous wavelet-transform to high-quality time-frequency analysis to the sets of observations of relative sunspot numbers. Wavelet analysis of these data reveals the following pattern: at the same time there are several activity cycles whose periods vary widely from the quasi biennial up to the centennial period. These relatively low-frequency periodic variations of the solar activity gradually change the values of periods of different cycles in time. This phenomenon can be observed in every cycle of activity.
Energy Technology Data Exchange (ETDEWEB)
Mohd, Shukri [Nondestructive Testing Group, Industrial Technology Division, Malaysian Nuclear Agency, 43000, Bangi, Selangor (Malaysia); Holford, Karen M.; Pullin, Rhys [Cardiff School of Engineering, Cardiff University, Queen' s Buildings, The Parade, CARDIFF CF24 3AA (United Kingdom)
2014-02-12
Source location is an important feature of acoustic emission (AE) damage monitoring in nuclear piping. The ability to accurately locate sources can assist in source characterisation and early warning of failure. This paper describe the development of a novelAE source location technique termed 'Wavelet Transform analysis and Modal Location (WTML)' based on Lamb wave theory and time-frequency analysis that can be used for global monitoring of plate like steel structures. Source location was performed on a steel pipe of 1500 mm long and 220 mm outer diameter with nominal thickness of 5 mm under a planar location test setup using H-N sources. The accuracy of the new technique was compared with other AE source location methods such as the time of arrival (TOA) techniqueand DeltaTlocation. Theresults of the study show that the WTML method produces more accurate location resultscompared with TOA and triple point filtering location methods. The accuracy of the WTML approach is comparable with the deltaT location method but requires no initial acoustic calibration of the structure.
3D Face Compression and Recognition using Spherical Wavelet Parametrization
Directory of Open Access Journals (Sweden)
Rabab M. Ramadan
2012-09-01
Full Text Available In this research an innovative fully automated 3D face compression and recognition system is presented. Several novelties are introduced to make the system performance robust and efficient. These novelties include: First, an automatic pose correction and normalization process by using curvature analysis for nose tip detection and iterative closest point (ICP image registration. Second, the use of spherical based wavelet coefficients for efficient representation of the 3D face. The spherical wavelet transformation is used to decompose the face image into multi-resolution sub images characterizing the underlying functions in a local fashion in both spacial and frequency domains. Two representation features based on spherical wavelet parameterization of the face image were proposed for the 3D face compression and recognition. Principle component analysis (PCA is used to project to a low resolution sub-band. To evaluate the performance of the proposed approach, experiments were performed on the GAVAB face database. Experimental results show that the spherical wavelet coefficients yield excellent compression capabilities with minimal set of features. Haar wavelet coefficients extracted from the face geometry image was found to generate good recognition results that outperform other methods working on the GAVAB database.
Investigation of various orthogonal wavelets for precise analysis of X-ray images
Directory of Open Access Journals (Sweden)
Prof. G.Sasi Bhushana Rao
2015-02-01
Full Text Available Now-a-days X-rays are playing very important role in medicine. One of the most important applications of Xray is detecting fractures in bones. X-ray provides important information about the type and location of the fracture. Sometimes it is not possible to detect the fractures in X-rays with naked eye. So it needs further processing to detect the fractures even at minute levels. To detect minute fractures, in this paper various edge feature extraction methods are analyzed which helps medical practitioners to study the bone structure, detects the bone fracture, measurement of fracture treatment, and treatment planning prior to surgery. The classical derivative edge detection operators such as Roberts, Prewitt, sobel, Laplacian of Gaussian can be used as edge detectors, but a lot of false edge information will be extracted. Therefore a technique based on orthogonal wavelet transforms like Haar, daubechies, coiflet, symlets are applied to detect the edges and are compared. Among all the methods, Haar wavelet transform method performs well in detecting the edges with better quality. The various performance metrics like Ratio of Edge pixels to size of image (REPS, peak signal to noise ratio (PSNR and computation time are compared for various wavelets.
Multi-resolution Analysis of Multi-spectral Palmprints using Hybrid Wavelets for Identification
Directory of Open Access Journals (Sweden)
Dr. H.B. Kekre
2013-04-01
Full Text Available Palmprint is a relatively new physiological biometric used in identification systems due to its stable and unique characteristics. The vivid texture information of palmprint present at different resolutions offers abundant prospects in personal recognition. This paper describes a new method to authenticate individuals based on palmprint identification. In order to analyze the texture information at various resolutions, we introduce a new hybrid wavelet, which is generated using two or more component transforms incorporating both their properties. A unique property of this wavelet is its flexibility to vary the number of components at each level of resolution and hence can be made suitable for various applications. Multi-spectral palmprints have been identified using energy compaction of the hybrid wavelet transform coefficients. The scores generated for each set of palmprint images under red, green and blue illuminations are combined using score-level fusion using AND and OR operators. Comparatively low values of equal error rate and high security index have been obtained for all fusion techniques. The experimental results demonstrate the effectiveness and accuracy of the proposed method.
Harikumar, Rajaguru; Vijayakumar, Thangavel
2014-12-01
The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.
Workman, Michael J; Serov, Alexey; Halevi, Barr; Atanassov, Plamen; Artyushkova, Kateryna
2015-05-01
The discrete wavelet transform (DWT) has found significant utility in process monitoring, filtering, and feature isolation of SEM, AFM, and optical images. Current use of the DWT for surface analysis assumes initial knowledge of the sizes of the features of interest in order to effectively isolate and analyze surface components. Current methods do not adequately address complex, heterogeneous surfaces in which features across multiple size ranges are of interest. Further, in situations where structure-to-property relationships are desired, the identification of features relevant for the function of the material is necessary. In this work, the DWT is examined as a tool for quantitative, length-scale specific surface metrology without prior knowledge of relevant features or length-scales. A new method is explored for determination of the best wavelet basis to minimize variation in roughness and skewness measurements with respect to change in position and orientation of surface features. It is observed that the size of the wavelet does not directly correlate with the size of features on the surface, and a method to measure the true length-scale specific roughness of the surface is presented. This method is applied to SEM and AFM images of non-precious metal catalysts, yielding new length-scale specific structure-to-property relationships for chemical speciation and fuel cell performance. The relationship between SEM and AFM length-scale specific roughness is also explored. Evidence is presented that roughness distributions of SEM images, as measured by the DWT, is representative of the true surface roughness distribution obtained from AFM.
应用小波理论分析Palmer干旱指标关键问题%Some Key Problems in Palmer Index Analysis Using Wavelet Decomposition
Institute of Scientific and Technical Information of China (English)
李晓辉; 杨勇; 任传友
2013-01-01
应用干旱指标对旱情特征可以进行有效分析,但在分析干旱周期性、突变性等特征时主要依赖人工判别,准确性低,且难以在众多数据中提炼出干旱特征的一般规律和变化异常的年份.为了满足旱情特征分析的自动化、智能化发展的要求,采用小波理论与Palmer干旱指数标相结合的方法,对辽宁省朝阳地区的干旱周期特性、突变特性、变化趋势等进行研究.结果表明:小波分解结果能较直观的体现旱情特征,可以正确的反应旱情特征变化的周期特性,并且降水量具有明显的下降趋势,说明小波函数对Palmer指数分解可以有效的实现旱情特征的深入分析.%Drought characteristics can be effectively analyzed by using drought index,but in the period of analyzing periodical,turbulent features etc,it mainly relies on artificial discrimination,with lower accuracy,and difficult to extract the general rules of drought characteristics and abnormal years.In order to meet the requirements of automation and intelligent development,wavelet and Palmer index combination was introduced in drought characteristics analysis,including periodical,turbulent and trend features etc.The experiment showd that wavelet decomposition results could reflect the drought characteristics intuitively and the cycle characteristics of drought characteristics change correctly,precipitation showed obvious downward trend,which means that the wavelet function to Palmer index decomposition could effectively realize the in-depth analysis of drought features.
Visualization of a Turbulent Jet Using Wavelets
Institute of Scientific and Technical Information of China (English)
Hui LI
2001-01-01
An application of multiresolution image analysis to turbulence was investigated in this paper, in order to visualize the coherent structure and the most essential scales governing turbulence. The digital imaging photograph of jet slice was decomposed by two-dimensional discrete wavelet transform based on Daubechies, Coifman and Baylkin bases. The best choice of orthogonal wavelet basis for analyzing the image of the turbulent structures was first discussed. It is found that these orthonormal wavelet families with index N＜10 were inappropriate for multiresolution image analysis of turbulent flow. The multiresolution images of turbulent structures were very similar when using the wavelet basis with the higher index number, even though wavelet bases are different functions. From the image components in orthogonal wavelet spaces with different scales, the further evident of the multi-scale structures in jet can be observed, and the edges of the vortices at different resolutions or scales and the coherent structure can be easily extracted.
Directory of Open Access Journals (Sweden)
George P. Papaioannou
2015-10-01
Full Text Available We study the co-evolution of the dynamics or co-movement of two electricity markets, the Italian and Greek, by studying the dynamics of their wholesale day-ahead prices, simultaneously in the time-frequency domain. Co-movement is alternatively referred as market integration in financial economics and markets are internationally integrated if the reward for risk is identical regardless the market one trades in. The innovation of this work is the application of wavelet analysis and more specifically the wavelet coherence to estimate the dynamic interaction between these two prices. Our method is compared to other generic econometric tools used in Economics and Finance namely the dynamic correlation and coherence analysis, to study the co-movement of variables of the type related to these two fields. Our study reveals valuable information that we believe will be extremely useful to the authorities as well as other agents participating in these markets to better prepare the national markets towards the European target model, a framework in which the two markets will be coupled.
Toledo, B A; Chian, A C-L; Rempel, E L; Miranda, R A; Muñoz, P R; Valdivia, J A
2013-02-01
We study general multifractal properties of tidal gauge and long-wave time series which show a well defined transition between two states, as is the case of sea level when a tsunami arrives. We adopt a method based on discrete wavelets, called wavelet leaders, which has been successfully used in a wide range of applications from image analysis to biomedical signals. First, we analyze an empirical time series of tidal gauge from the tsunami event of 27 February 2010 in Chile. Then, we study a numerical solution of the driven-damped regularized long-wave equation (RLWE) which displays on-off intermittency. Both time series are characterized by a sudden change between two sharply distinct dynamical states. Our analysis suggests a correspondence between the pre- and post-tsunami states (ocean background) and the on state in the RLWE, and also between the tsunami state (disturbed ocean) and the off state in the RLWE. A qualitative similarity in their singularity spectra is observed, and since the RLWE is used to model shallow water dynamics, this result could imply an underlying dynamical similarity.
Maury, Augusto; Revilla, Reynier I
2015-08-01
Cosmic rays (CRs) occasionally affect charge-coupled device (CCD) detectors, introducing large spikes with very narrow bandwidth in the spectrum. These CR features can distort the chemical information expressed by the spectra. Consequently, we propose here an algorithm to identify and remove significant spikes in a single Raman spectrum. An autocorrelation analysis is first carried out to accentuate the CRs feature as outliers. Subsequently, with an adequate selection of the threshold, a discrete wavelet transform filter is used to identify CR spikes. Identified data points are then replaced by interpolated values using the weighted-average interpolation technique. This approach only modifies the data in a close vicinity of the CRs. Additionally, robust wavelet transform parameters are proposed (a desirable property for automation) after optimizing them with the application of the method in a great number of spectra. However, this algorithm, as well as all the single-spectrum analysis procedures, is limited to the cases in which CRs have much narrower bandwidth than the Raman bands. This might not be the case when low-resolution Raman instruments are used.
Detecting Impulses in Mechanical Signals by Wavelets
Directory of Open Access Journals (Sweden)
Yang W-X
2004-01-01
Full Text Available The presence of periodical or nonperiodical impulses in vibration signals often indicates the occurrence of machine faults. This knowledge is applied to the fault diagnosis of such machines as engines, gearboxes, rolling element bearings, and so on. The development of an effective impulse detection technique is necessary and significant for evaluating the working condition of these machines, diagnosing their malfunctions, and keeping them running normally over prolong periods. With the aid of wavelet transforms, a wavelet-based envelope analysis method is proposed. In order to suppress any undesired information and highlight the features of interest, an improved soft threshold method has been designed so that the inspected signal is analyzed in a more exact way. Furthermore, an impulse detection technique is developed based on the aforementioned methods. The effectiveness of the proposed technique on the extraction of impulsive features of mechanical signals has been proved by both simulated and practical experiments.
Wavelet transforms and their applications
Debnath, Lokenath
2015-01-01
This textbook is an introduction to wavelet transforms and accessible to a larger audience with diverse backgrounds and interests in mathematics, science, and engineering. Emphasis is placed on the logical development of fundamental ideas and systematic treatment of wavelet analysis and its applications to a wide variety of problems as encountered in various interdisciplinary areas. Numerous standard and challenging topics, applications, and exercises are included in this edition, which will stimulate research interest among senior undergraduate and graduate students. The book contains a large number of examples, which are either directly associated with applications or formulated in terms of the mathematical, physical, and engineering context in which wavelet theory arises. Topics and Features of the Second Edition: · Expanded and revised the historical introduction by including many new topics such as the fractional Fourier transform, and the construction of wavelet bases in various spaces ...
A Class of Bidimensional FMRA Wavelet Frames
Institute of Scientific and Technical Information of China (English)
Yun Zhang LI
2006-01-01
This paper addresses the construction of wavelet frame from a frame multiresolution analysis (FMRA) associated with a dilation matrix of determinant ±2. The dilation matrices of determinant ±2 can be classified as six classes according to integral similarity. In this paper, for four classes of them, the construction of wavelet frame from an FMRA is obtained, and, as examples, Shannon type wavelet frames are constructed, which have an independent value for their optimality in some sense.
Pautomatic Sea Target Detection Based on Wavelet Transform
Institute of Scientific and Technical Information of China (English)
PEI Li-li; LUO Hai-bo
2009-01-01
An effective automatic target detection algorithm based on wavelet transform, which takes advantage of the localization and the orientation of wavelet analysis, is proposed. The algorithm detects the target in the vertical component of the wavelet transformation of the image. After mutual energy combination and sea clutter suppression through spatial weighting and thresholding, the target is located through maximum energy determination and its size is indicated through similarity measurement function of two overlapping windows. Experiment results show that the target can be detected by the algorithm in a single image frame and the better efficiency can be obtained also under the complicated backgrounds of existing the disturbances of cloud layer and fish scale light.
Simons, Frederik J; Nolet, Guust; Daubechies, Ingrid C; Voronin, S; Judd, J S; Vetter, P A; Charlety, J; Vonesch, C
2011-01-01
We propose a class of spherical wavelet bases for the analysis of geophysical models and forthe tomographic inversion of global seismic data. Its multiresolution character allows for modeling with an effective spatial resolution that varies with position within the Earth. Our procedure is numerically efficient and can be implemented with parallel computing. We discuss two possible types of discrete wavelet transforms in the angular dimension of the cubed sphere. We discuss benefits and drawbacks of these constructions and apply them to analyze the information present in two published seismic wavespeed models of the mantle, for the statistics and power of wavelet coefficients across scales. The localization and sparsity properties of wavelet bases allow finding a sparse solution to inverse problems by iterative minimization of a combination of the $\\ell_2$ norm of data fit and the $\\ell_1$ norm on the wavelet coefficients. By validation with realistic synthetic experiments we illustrate the likely gains of our...
A new wavelet-based thin plate element using B-spline wavelet on the interval
Jiawei, Xiang; Xuefeng, Chen; Zhengjia, He; Yinghong, Zhang
2008-01-01
By interacting and synchronizing wavelet theory in mathematics and variational principle in finite element method, a class of wavelet-based plate element is constructed. In the construction of wavelet-based plate element, the element displacement field represented by the coefficients of wavelet expansions in wavelet space is transformed into the physical degree of freedoms in finite element space via the corresponding two-dimensional C1 type transformation matrix. Then, based on the associated generalized function of potential energy of thin plate bending and vibration problems, the scaling functions of B-spline wavelet on the interval (BSWI) at different scale are employed directly to form the multi-scale finite element approximation basis so as to construct BSWI plate element via variational principle. BSWI plate element combines the accuracy of B-spline functions approximation and various wavelet-based elements for structural analysis. Some static and dynamic numerical examples are studied to demonstrate the performances of the present element.
Elzanfaly, Eman S; Hassan, Said A; Salem, Maissa Y; El-Zeany, Badr A
2015-12-05
A comparative study was established between two signal processing techniques showing the theoretical algorithm for each method and making a comparison between them to indicate the advantages and limitations. The methods under study are Numerical Differentiation (ND) and Continuous Wavelet Transform (CWT). These methods were studied as spectrophotometric resolution tools for simultaneous analysis of binary and ternary mixtures. To present the comparison, the two methods were applied for the resolution of Bisoprolol (BIS) and Hydrochlorothiazide (HCT) in their binary mixture and for the analysis of Amlodipine (AML), Aliskiren (ALI) and Hydrochlorothiazide (HCT) as an example for ternary mixtures. By comparing the results in laboratory prepared mixtures, it was proven that CWT technique is more efficient and advantageous in analysis of mixtures with severe overlapped spectra than ND. The CWT was applied for quantitative determination of the drugs in their pharmaceutical formulations and validated according to the ICH guidelines where accuracy, precision, repeatability and robustness were found to be within the acceptable limit.
Shabri, Ani; Samsudin, Ruhaidah
2014-01-01
Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Energy Technology Data Exchange (ETDEWEB)
Sen, Asok K. [Richard G. Lugar Centre for Renewable Energy, and Department of Mathematical Sciences, Indiana University, (United States)], email: asen@iupui.edu; Akif Ceviz, M.; Volkan Oner, I. [Department of Mechanical Engineering, University of Ataturk (Turkey)], email: aceviz@atauni.edu.tr
2011-07-01
The cycle-to-cycle variations (CCV) of the indicated mean effective pressure (IMEP) in a spark ignition engine fuelled by gasoline and gasoline-hydrogen blends is investigated. CCVs are estimated by using the coefficient of variation (COV) and the overall spectral power given by the global wavelet spectrum (GWS). It was found that the addition of hydrogen reduces the CCV of the IMEP. Analysis of the wavelet can also identify the dominant modes of variability and delineate the engine cycles over which these modes can persist. Air-fuel ratio was varied from 1.0 to 1.3, and hydrogen was added up to 7.74% by volume. The engine was operated at 2000 rpm. Results demonstrate that subject to air-fuel ratio and % of hydrogen added, IMEP time series can exhibit multiscale dynamics consisting of persistent oscillations and intermittent fluctuations. These results can help develop effective control strategies to reduce cyclic variability in a spark ignition engine fuelled by gasoline-hydrogen mixtures.
Wavelet analysis of low-frequency variability in oak tree-ring chronologies from east Central Europe
Directory of Open Access Journals (Sweden)
Sen Asok K.
2016-07-01
Full Text Available This study investigates the low-frequency (interannual and longer period variability in three hydroclimatic records from east Central Europe. Two of these records consist of climate proxies derived from oak-tree rings in Bakta forest, and Balaton Highlands in Hungary, for the time interval 1783-2003. The third record consists of homogenized instrumental precipitation data from Budapest, Hungary, from 1842 to 2003. Using wavelet analysis, the three time series are analyzed and compared with one another. It is found that all three time series exhibit strong interannual variability at the 2-4 years timescales, and these variations occur intermittently throughout the length of each record. Significant variability is also observed in all the records at decadal timescales, but these variations persist for only two to three cycles. Wavelet coherence among the various time series is used to explore their time-varying correlation. The results reveal significant coherence at the 2-4 years band. At these timescales, the climatic variations are correlated to the tree-ring signal over different time intervals with changing phase. Increased (decreased contribution of large-scale stratiform precipitation offers a potential explanation for enhanced (faded coherence at the interannual timescale. Strong coherence was also observed occasionally at decadal timescales, however these coherences did not appear uniformly. These results reinforce the earlier assertion that neither the strength nor the rank of the similarity of the local hydroclimate signals is stable throughout the past two centuries.
Directory of Open Access Journals (Sweden)
Ani Shabri
2014-01-01
Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Optical Wavelet Signals Processing and Multiplexing
Cincotti, Gabriella; Moreolo, Michela Svaluto; Neri, Alessandro
2005-12-01
We present compact integrable architectures to perform the discrete wavelet transform (DWT) and the wavelet packet (WP) decomposition of an optical digital signal, and we show that the combined use of planar lightwave circuits (PLC) technology and multiresolution analysis (MRA) can add flexibility to current multiple access optical networks. We furnish the design guidelines to synthesize wavelet filters as two-port lattice-form planar devices, and we give some examples of optical signal denoising and compression/decompression techniques in the wavelet domain. Finally, we present a fully optical wavelet packet division multiplexing (WPDM) scheme where data signals are waveform-coded onto wavelet atom functions for transmission, and numerically evaluate its performances.
Realization of Wavelet Transform Using SAW Devices
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on the characteristics of surface acoustic wave(SAW) devices, the theory for realizing wavelet transform (WT) by SAW is deduced. Simulated experiment shows that the method of implementing WT using SAW devices has virtues of high speed and utility and is compatible with digital technique. It is important to implement wavelet transform.
Directory of Open Access Journals (Sweden)
D. Seidl
1999-06-01
Full Text Available Among a variety of spectrogram methods Short-Time Fourier Transform (STFT and Continuous Wavelet Transform (CWT were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.
Aspiras, Theus H.; Asari, Vijayan K.
2011-06-01
In this paper, we evaluate the feature extraction technique of Recoursing Energy Efficiency on electroencephalograph data for human emotion recognition. A protocol has been established to elicit five distinct emotions (joy, sadness, disgust, fear, surprise, and neutral). EEG signals are collected using a 256-channel system, preprocessed using band-pass filters and Laplacian Montage, and decomposed into five frequency bands using Discrete Wavelet Transform. The Recoursing Energy Efficiency (REE) is calculated and applied to a Multi-Layer Perceptron network for classification. We compare the performance of REE features with conventional energy based features.
Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames.
Depeursinge, Adrien; Van de Ville, Dimitri; Platon, Alexandra; Geissbuhler, Antoine; Poletti, Pierre-Alexandre; Müller, Henning
2012-07-01
We propose near-affine-invariant texture descriptors derived from isotropic wavelet frames for the characterization of lung tissue patterns in high-resolution computed tomography (HRCT) imaging. Affine invariance is desirable to enable learning of nondeterministic textures without a priori localizations, orientations, or sizes. When combined with complementary gray-level histograms, the proposed method allows a global classification accuracy of 76.9% with balanced precision among five classes of lung tissue using a leave-one-patient-out cross validation, in accordance with clinical practice.
Beccar-Varela, Maria P.; Mariani, Maria C.; Tweneboah, Osei K.; Florescu, Ionut
2017-05-01
In this study, we apply a wavelet methodology initially developed for geophysical data to financial data. Specifically, the method distinguishes between natural tectonic earthquakes and man made explosions. We exemplify using time series data from two financial events: the Lehman Brothers collapse and the Flash Crash event. We conclude that the Lehman Brothers collapse behaves like a natural earthquake while the Flash Crash event behaves like a human made explosion. This study may imply that the Lehman Brothers type events may be predicted, while sudden Flash Crash type events are not predictable.