Energy Technology Data Exchange (ETDEWEB)
Leach, R.R.; Schultz, C.; Dowla, F.
1997-07-15
Development of a worldwide network to monitor seismic activity requires deployment of seismic sensors in areas which have not been well studied or may have from available recordings. Development and testing of detection and discrimination algorithms requires a robust representative set of calibrated seismic events for a given region. Utilizing events with poor signal-to-noise (SNR) can add significant numbers to usable data sets, but these events must first be adequately filtered. Source and path effects can make this a difficult task as filtering demands are highly varied as a function of distance, event magnitude, bearing, depth etc. For a given region, conventional methods of filter selection can be quite subjective and may require intensive analysis of many events. In addition, filter parameters are often overly generalized or contain complicated switching. We have developed a method to provide an optimized filter for any regional or teleseismically recorded event. Recorded seismic signals contain arrival energy which is localized in frequency and time. Localized temporal signals whose frequency content is different from the frequency content of the pre-arrival record are identified using rms power measurements. The method is based on the decomposition of a time series into a set of time series signals or scales. Each scale represents a time-frequency band with a constant Q. SNR is calculated for a pre-event noise window and for a window estimated to contain the arrival. Scales with high SNR are used to indicate the band pass limits for the optimized filter.The results offer a significant improvement in SNR particularly for low SNR events. Our method provides a straightforward, optimized filter which can be immediately applied to unknown regions as knowledge of the geophysical characteristics is not required. The filtered signals can be used to map the seismic frequency response of a region and may provide improvements in travel-time picking, bearing estimation
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.
Multidimensional filter banks and wavelets research developments and applications
Levy, Bernard
1997-01-01
Multidimensional Filter Banks and Wavelets: Reserach Developments and Applications brings together in one place important contributions and up-to-date research results in this important area. Multidimensional Filter Banks and Wavelets: Research Developments and Applications serves as an excellent reference, providing insight into some of the most important research issues in the field.
A wavelet phase filter for emission tomography
International Nuclear Information System (INIS)
Olsen, E.T.; Lin, B.
1995-01-01
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, 2π). 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
Standard filter approximations for low power Continuous Wavelet Transforms.
Casson, Alexander J; Rodriguez-Villegas, Esther
2010-01-01
Analogue domain implementations of the Continuous Wavelet Transform (CWT) have proved popular in recent years as they can be implemented at very low power consumption levels. This is essential for use in wearable, long term physiological monitoring systems. Present analogue CWT implementations rely on taking mathematical a approximation of the wanted mother wavelet function to give a filter transfer function that is suitable for circuit implementation. This paper investigates the use of standard filter approximations (Butterworth, Chebyshev, Bessel) as an alternative wavelet approximation technique. This extends the number of approximation techniques available for generating analogue CWT filters. An example ECG analysis shows that signal information can be successfully extracted using these CWT approximations.
On-Line QRS Complex Detection Using Wavelet Filtering
National Research Council Canada - National Science Library
Szilagyi, L
2001-01-01
...: first a wavelet transform filtering is applied to the signal, then QRS complex localization is performed using a maximum detection and peak classification algorithm The algorithm has been tested...
3D Wavelet-Based Filter and Method
Moss, William C.; Haase, Sebastian; Sedat, John W.
2008-08-12
A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.
Option pricing from wavelet-filtered financial series
de Almeida, V. T. X.; Moriconi, L.
2012-10-01
We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most (≃99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.
Improving the quality of brain CT image from Wavelet filters
International Nuclear Information System (INIS)
Pita Machado, Reinaldo; Perez Diaz, Marlen; Bravo Pino, Rolando
2012-01-01
An algorithm to reduce Poisson noise is described using Wavelet filters. Five tomographic images of patients and a head anthropomorphic phantom were used. They were acquired with two different CT machines. Due to the original images contain the acquisition noise; some simulated free noise lesions were added to the images and after that the whole images were contaminated with noise. Contaminated images were filtered with 9 Wavelet filters at different decomposition levels and thresholds. Image quality of filtered and unfiltered images was graded using the Signal to Noise ratio, Normalized Mean Square Error and the Structural Similarity Index, as well as, by the subjective JAFROC methods with 5 observers. Some filters as Bior 3.7 and dB45 improved in a significant way head CT image quality (p<0.05) producing an increment in SNR without visible structural distortions
Multi-dimensional medical images compressed and filtered with wavelets
International Nuclear Information System (INIS)
Boyen, H.; Reeth, F. van; Flerackers, E.
2002-01-01
Full text: Using the standard wavelet decomposition methods, multi-dimensional medical images can be compressed and filtered by repeating the wavelet-algorithm on 1D-signals in an extra loop per extra dimension. In the non-standard decomposition for multi-dimensional images the areas that must be zero-filled in case of band- or notch-filters are more complex than geometric areas such as rectangles or cubes. Adding an additional dimension in this algorithm until 4D (e.g. a 3D beating heart) increases the geometric complexity of those areas even more. The aim of our study was to calculate the boundaries of the formed complex geometric areas, so we can use the faster non-standard decomposition to compress and filter multi-dimensional medical images. Because a lot of 3D medical images taken by PET- or SPECT-cameras have only a few layers in the Z-dimension and compressing images in a dimension with a few voxels is usually not worthwhile, we provided a solution in which one can choose which dimensions will be compressed or filtered. With the proposal of non-standard decomposition on Daubechies' wavelets D2 to D20 by Steven Gollmer in 1992, 1D data can be compressed and filtered. Each additional level works only on the smoothed data, so the transformation-time halves per extra level. Zero-filling a well-defined area alter the wavelet-transform and then performing the inverse transform will do the filtering. To be capable to compress and filter up to 4D-Images with the faster non-standard wavelet decomposition method, we have investigated a new method for calculating the boundaries of the areas which must be zero-filled in case of filtering. This is especially true for band- and notch filtering. Contrary to the standard decomposition method, the areas are no longer rectangles in 2D or cubes in 3D or a row of cubes in 4D: they are rectangles expanded with a half-sized rectangle in the other direction for 2D, cubes expanded with half cubes in one and quarter cubes in the
Modified signed-digit trinary addition using synthetic wavelet filter
Iftekharuddin, K. M.; Razzaque, M. A.
2000-09-01
The modified signed-digit (MSD) number system has been a topic of interest as it allows for parallel carry-free addition of two numbers for digital optical computing. In this paper, harmonic wavelet joint transform (HWJT)-based correlation technique is introduced for optical implementation of MSD trinary adder implementation. The realization of the carry-propagation-free addition of MSD trinary numerals is demonstrated using synthetic HWJT correlator model. It is also shown that the proposed synthetic wavelet filter-based correlator shows high performance in logic processing. Simulation results are presented to validate the performance of the proposed technique.
Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins
Brenner, Marty; Lind, Rick
1998-01-01
Wavelet analysis for filtering and system identification was used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins was reduced with parametric and nonparametric time-frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data was used to reduce the effects of external desirableness and unmodeled dynamics. Parametric estimates of modal stability were also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. F-18 high Alpha Research Vehicle aeroservoelastic flight test data demonstrated improved robust stability prediction by extension of the stability boundary beyond the flight regime.
Wavelet Filter Banks for Super-Resolution SAR Imaging
Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess
2011-01-01
This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.
Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters
Abhayaratne, Charith
2011-07-01
Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.
[Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].
Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang
2008-02-01
The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.
Directory of Open Access Journals (Sweden)
LU Yongle
2014-07-01
Full Text Available This paper demonstrates a method and system for north finding with a low-cost piezoelectricity accelerometer based on the Coriolis acceleration principle. The proposed setup is based on the choice of an accelerometer with residual noise of 35 ng•Hz-1/2. The plane of the north finding system is aligned parallel to the local level, which helps to eliminate the effect of plane error. The Coriolis acceleration caused by the earth’s rotation and the acceleration’s instantaneous velocity is much weaker than the g-sensitivity acceleration. To get a high accuracy and a shorter time for north finding system, in this paper, the Filtering Circuit and the wavelet packet de-nosing algorithm are used as the following. First, the hardware is designed as the alternating currents across by filtering circuit, so the DC will be isolated and the weak AC signal will be amplified. The DC is interfering signal generated by the earth's gravity. Then, we have used a wavelet packet to filter the signal which has been done through the filtering circuit. Finally, compare the north finding results measured by wavelet packet filtering with those measured by a low-pass filter. Wavelet filter de-noise data shows that wavelet packet filtering and wavelet filter measurement have high accuracy. Wavelet Packet filtering has stronger ability to remove burst noise and higher engineering environment adaptability than that of Wavelet filtering. Experimental results prove the effectiveness and project implementation of the accelerometer north finding method based on wavelet packet de-noising algorithm.
Mammographic image enhancement using wavelet transform and homomorphic filter
Directory of Open Access Journals (Sweden)
F Majidi
2015-12-01
Full Text Available Mammography is the most effective method for the early diagnosis of breast cancer diseases. As mammographic images contain low signal to noise ratio and low contrast, it becomes too difficult for radiologists to analyze mammogram. To deal with the above stated problems, it is very important to enhance the mammographic images using image processing methods. This paper introduces a new image enhancement approach for mammographic images which uses the modified mathematical morphology, wavelet transform and homomorphic filter to suppress the noise of images. For performance evaluation of the proposed method, contrast improvement index (CII and edge preservation index (EPI are adopted. Experimental results on mammographic images from Pejvak Digital Imaging Center (PDIC show that the proposed algorithm improves the two indexes, thereby achieving the goal of enhancing mammographic images.
Filtering Performance Comparison of Kernel and Wavelet Filters for Reactivity Signal Noise
International Nuclear Information System (INIS)
Park, Moon Ghu; Shin, Ho Cheol; Lee, Yong Kwan; You, Skin
2006-01-01
Nuclear reactor power deviation from the critical state is a parameter of specific interest defined by the reactivity measuring neutron population. Reactivity is an extremely important quantity used to define many of the reactor startup physics parameters. The time dependent reactivity is normally determined by solving the using inverse neutron kinetics equation. The reactivity computer is a device to provide an on-line solution of the inverse kinetics equation. The measurement signal of the neutron density is normally noise corrupted and the control rods movement typically gives reactivity variation with edge signals like saw teeth. Those edge regions should be precisely preserved since the measured signal is used to estimate the reactivity wroth which is a crucial parameter to assure the safety of the nuclear reactors. In this paper, three kind of edge preserving noise filters are proposed and their performance is demonstrated using stepwise signals. The tested filters are based on the unilateral, bilateral kernel and wavelet filters which are known to be effective in edge preservation. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters
Iris image recognition wavelet filter-banks based iris feature extraction schemes
Rahulkar, Amol D
2014-01-01
This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc. that will...
Lattice functions, wavelet aliasing, and SO(3) mappings of orthonormal filters
John, Sarah
1998-01-01
A formulation of multiresolution in terms of a family of dyadic lattices {Sj;j∈Z} and filter matrices Mj⊂U(2)⊂GL(2,C) illuminates the role of aliasing in wavelets and provides exact relations between scaling and wavelet filters. By showing the {DN;N∈Z+} collection of compactly supported, orthonormal wavelet filters to be strictly SU(2)⊂U(2), its representation in the Euler angles of the rotation group SO(3) establishes several new results: a 1:1 mapping of the {DN} filters onto a set of orbits on the SO(3) manifold; an equivalence of D∞ to the Shannon filter; and a simple new proof for a criterion ruling out pathologically scaled nonorthonormal filters.
Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms
2004-08-06
wavelet transforms. Whereas the term “evolved” pertains only to the altered wavelet coefficients used during the inverse transform process. 2...words, the inverse transform produces the original signal x(t) from the wavelet and scaling coefficients. )()( ,, tdtx nk n nk k ψ...reconstruct the original signal as accurately as possible. The inverse transform reconstructs an approximation of the original signal (Burrus
Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter
Syahputra, M. F.; Situmeang, S. I. G.; Rahmat, R. F.; Budiarto, R.
2017-04-01
The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.
A simple structure wavelet transform circuit employing function link neural networks and SI filters
Mu, Li; Yigang, He
2016-12-01
Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.
Impulse Noise Cancellation of Medical Images Using Wavelet Networks and Median Filters
Sadri, Amir Reza; Zekri, Maryam; Sadri, Saeid; Gheissari, Niloofar
2012-01-01
This paper presents a new two-stage approach to impulse noise removal for medical images based on wavelet network (WN). The first step is noise detection, in which the so-called gray-level difference and average background difference are considered as the inputs of a WN. Wavelet Network is used as a preprocessing for the second stage. The second step is removing impulse noise with a median filter. The wavelet network presented here is a fixed one without learning. Experimental results show that our method acts on impulse noise effectively, and at the same time preserves chromaticity and image details very well. PMID:23493998
Sikora, Andrzej; Rodak, Aleksander; Unold, Olgierd; Klapetek, Petr
2016-12-01
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. Copyright © 2016 Elsevier B.V. All rights reserved.
Use of switched capacitor filters to implement the discrete wavelet transform
Kaiser, Kraig E.; Peterson, James N.
1993-01-01
This paper analyzes the use of IIR switched capacitor filters to implement the discrete wavelet transform and the inverse transform, using quadrature mirror filters (QMF) which have the necessary symmetry for reconstruction of the data. This is done by examining the sensitivity of the QMF transforms to the manufacturing variance in the desired capacitances. The performance is evaluated at the outputs of the separate filter stages and the error in the reconstruction of the inverse transform is compared with the desired results.
Exploring an optimal wavelet-based filter for cryo-ET imaging.
Huang, Xinrui; Li, Sha; Gao, Song
2018-02-07
Cryo-electron tomography (cryo-ET) is one of the most advanced technologies for the in situ visualization of molecular machines by producing three-dimensional (3D) biological structures. However, cryo-ET imaging has two serious disadvantages-low dose and low image contrast-which result in high-resolution information being obscured by noise and image quality being degraded, and this causes errors in biological interpretation. The purpose of this research is to explore an optimal wavelet denoising technique to reduce noise in cryo-ET images. We perform tests using simulation data and design a filter using the optimum selected wavelet parameters (three-level decomposition, level-1 zeroed out, subband-dependent threshold, a soft-thresholding and spline-based discrete dyadic wavelet transform (DDWT)), which we call a modified wavelet shrinkage filter; this filter is suitable for noisy cryo-ET data. When testing using real cryo-ET experiment data, higher quality images and more accurate measures of a biological structure can be obtained with the modified wavelet shrinkage filter processing compared with conventional processing. Because the proposed method provides an inherent advantage when dealing with cryo-ET images, it can therefore extend the current state-of-the-art technology in assisting all aspects of cryo-ET studies: visualization, reconstruction, structural analysis, and interpretation.
Application of wavelet domain wiener filter in denoising of airborne γ-ray data
International Nuclear Information System (INIS)
Luo Yaoyao; Ge Liangquan; Xiong Chao; Xu Lipeng; Hua Yongtao
2012-01-01
The wavelet domain Wiener filter method, which combines the traditional wavelet method and the wiener filter, is established at CUT to reduce noising in as-recorded airborne gamma-ray spectra. It was used to treat an airborne gamma-ray data collected from an area m Inner Mongolia. The results showed that using this method, statistical noise could be greatly removed from the raw airborne gamma-ray spectra, and quality of the processed data is much better than those by conventional spectral denoising methods. (authors)
Energy Technology Data Exchange (ETDEWEB)
Sikora, Andrzej, E-mail: sikora@iel.wroc.pl [Electrotechnical Institute, Division of Electrotechnology and Materials Science, M. Skłodowskiej-Curie 55/61, 50-369 Wrocław (Poland); Rodak, Aleksander [Faculty of Electronics, Wrocław University of Technology, Janiszewskiego 11/17, 50-372 Wrocław (Poland); Unold, Olgierd [Institute of Computer Engineering, Control and Robotics, Faculty of Electronics, Wrocław University of Technology, Janiszewskiego 11/17, 50-372 Wrocław (Poland); Klapetek, Petr [Czech Metrology Institute, Okružní 31, 638 00 Brno (Czech Republic)
2016-12-15
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. - Highlights: • A novel approach to 2D wavelet-based filter for atomic force microscopy is shown. • The additional AFM measurement signal is used to adjust the filter. • Efficient removal of the local interference phenomena caused artifacts is presented.
International Nuclear Information System (INIS)
Sikora, Andrzej; Rodak, Aleksander; Unold, Olgierd; Klapetek, Petr
2016-01-01
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. - Highlights: • A novel approach to 2D wavelet-based filter for atomic force microscopy is shown. • The additional AFM measurement signal is used to adjust the filter. • Efficient removal of the local interference phenomena caused artifacts is presented.
Application of wavelet-based multi-model Kalman filters to real-time flood forecasting
Chou, Chien-Ming; Wang, Ru-Yih
2004-04-01
This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.
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
On the application of optimal wavelet filter banks for ECG signal classification
International Nuclear Information System (INIS)
Hadjiloucas, S; Jannah, N; Hwang, F; Galvão, R K H
2014-01-01
This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier
The use of wavelet filters for reducing noise in posterior fossa Computed Tomography images
International Nuclear Information System (INIS)
Pita-Machado, Reinado; Perez-Diaz, Marlen; Lorenzo-Ginori, Juan V.; Bravo-Pino, Rolando
2014-01-01
Wavelet transform based de-noising like wavelet shrinkage, gives the good results in CT. This procedure affects very little the spatial resolution. Some applications are reconstruction methods, while others are a posteriori de-noising methods. De-noising after reconstruction is very difficult because the noise is non-stationary and has unknown distribution. Therefore, methods which work on the sinogram-space don’t have this problem, because they always work over a known noise distribution at this point. On the other hand, the posterior fossa in a head CT is a very complex region for physicians, because it is commonly affected by artifacts and noise which are not eliminated during the reconstruction procedure. This can leads to some false positive evaluations. The purpose of our present work is to compare different wavelet shrinkage de-noising filters to reduce noise, particularly in images of the posterior fossa within CT scans in the sinogram-space. This work describes an experimental search for the best wavelets, to reduce Poisson noise in Computed Tomography (CT) scans. Results showed that de-noising with wavelet filters improved the quality of posterior fossa region in terms of an increased CNR, without noticeable structural distortions
Wavelet-filtering of symbolic music representations for folk tune segmentation and classification
DEFF Research Database (Denmark)
Velarde, Gissel; Weyde, Tillman; Meredith, David
2013-01-01
The aim of this study is to evaluate a machine-learning method in which symbolic representations of folk songs are segmented and classified into tune families with Haar-wavelet filtering. The method is compared with previously proposed Gestalt based method. Melodies are represented as discrete...... coefficients’ local maxima to indicate local boundaries and classify segments by means of k-nearest neighbours based on standard vector-metrics (Euclidean, cityblock), and compare the results to a Gestalt-based segmentation method and metrics applied directly to the pitch signal. We found that the wavelet...
Paul, Sabyasachi; Sarkar, P K
2013-04-01
Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.
Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang
2016-02-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
International Nuclear Information System (INIS)
Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran
2016-01-01
Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses
A wavelet filtering method for cumulative gamma spectroscopy used in wear measurements
International Nuclear Information System (INIS)
Bianchi, Davide; Lenauer, Claudia; Betz, Gerhard; Vernes, András
2017-01-01
Continuous ultra-mild wear quantification using radioactive isotopes involves measuring very low amounts of activity in limited time intervals. This results in gamma spectra with poor signal-to-noise ratio and hence very scattered wear data, especially during running-in, where wear is intrinsically low. Therefore, advanced filtering methods reducing the wear data scattering and making the calculation of the main peak area more accurate are mandatory. An energy-time dependent threshold for wavelet detail coefficients based on Poisson statistics and using a combined Barwell law for the estimation of the average photon counting rate is then introduced. In this manner, it was shown that the accuracy of running-in wear quantification is enhanced. - Highlights: • Time-dependent Poisson statistics. • Wavelet-based filtering of cumulative gamma spectra. • Improvement of low wear analysis.
International Nuclear Information System (INIS)
Meng, Lingjie; Xiang, Jiawei; Zhong, Yongteng; Song, Wenlei
2015-01-01
Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, espectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.
Directory of Open Access Journals (Sweden)
Bijan Rahmani
2016-08-01
Full Text Available Available photovoltaic (PV systems show a prolonged transient response, when integrated into the power grid via active filters. On one hand, the conventional low-pass filter, employed within the integrated PV system, works with a large delay, particularly in the presence of system’s low-order harmonics. On the other hand, the switching of the DC (direct current–DC converters within PV units also prolongs the transient response of an integrated system, injecting harmonics and distortion through the PV-end current. This paper initially develops a wavelet-based low-pass filter to improve the transient response of the interconnected PV systems to grid lines. Further, a damped input filter is proposed within the PV system to address the raised converter’s switching issue. Finally, Matlab/Simulink simulations validate the effectiveness of the proposed wavelet-based low-pass filter and damped input filter within an integrated PV system.
International Nuclear Information System (INIS)
Paul, Sabyasachi; Sarkar, P.K.
2012-05-01
The characterization of radionuclide in the in-vivo monitoring analysis using gamma spectrometry poses difficulty due to very low activity level in biological systems. The large statistical fluctuations often make identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet based noise filtering methodology has been developed for better detection of gamma peaks while analyzing noisy spectrometric data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for the noise rejection and inverse transform after soft thresholding over the generated coefficients. Analyses of in-vivo monitoring data of 235 U and 238 U have been carried out using this method without disturbing the peak position and amplitude while achieving a threefold improvement in the signal to noise ratio, compared to the original measured spectrum. When compared with other data filtering techniques, the wavelet based method shows better results. (author)
Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation
Directory of Open Access Journals (Sweden)
S.K. Saha
2015-01-01
Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.
International Nuclear Information System (INIS)
Perez Diaz, M.; Ruiz Gonzalez, Y.; Lorenzo Ginori, J. V.
2015-01-01
This paper describes a comparison among some wavelet filters and other most traditional filters in the frequency domain like Median, Wiener and Butter worth to reduce Poisson noise in Computed Tomography (CT) scans. Five slices of CT containing the posterior fossa from an anthropomorphic phantom and from patients were selected. As their original projections contain noise from the acquisition process, some simulated noise-free lesions were added on the images. After that, the whole images were artificially contaminated with Poisson noise over the sinogram-space. The configurations using wavelets drawn from four wavelet families, using various decomposition levels, and different thresholds, were tested in order to determine de-noising performance as well as the rest of the traditional filters. The quality of the resulting images was evaluated by using Contrast to Noise Ratio (CNR), HVS absolute norm (H1), and Structural Similarity Index (SSIM) as quantitative metrics. We have observed that Wavelet filtering is an alternative to be considered for Poisson noise reduction in image processing of posterior fossa images for head CT with similar behavior to Butter worth and better than Median or Wiener filters for the developed experiment. (Author)
A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery
Li, Ting; Chen, Xiao-Mei; Chen, Gang; Xue, Bo; Ni, Guo-Qiang
2009-11-01
Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.
WaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filtering
Liu, Zhi
2012-02-10
Motivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on 15N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY. The Author(s) 2012. Published by Oxford University Press.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
The research purpose of this paper is to show the limitations of the existing radiometric normalization approaches and their disadvantages in change detection of artificial objects by comparing the existing approaches,on the basis of which a preprocessing approach to radiometric consistency,based on wavelet transform and a spatial low-pass filter,has been devised.This approach first separates the high frequency information and low frequency information by wavelet transform.Then,the processing of relative radiometric consistency based on a low-pass filter is conducted on the low frequency parts.After processing,an inverse wavelet transform is conducted to obtain the results image.The experimental results show that this approach can substantially reduce the influence on change detection of linear or nonlinear radiometric differences in multi-temporal images.
Directory of Open Access Journals (Sweden)
Fei Dong
2018-01-01
Full Text Available In order to enhance the performance of bearing fault diagnosis and classification, features extraction and features dimensionality reduction have become more important. The original statistical feature set was calculated from single branch reconstruction vibration signals obtained by using maximal overlap discrete wavelet packet transform (MODWPT. In order to reduce redundancy information of original statistical feature set, features selection by adjusted rand index and sum of within-class mean deviations (FSASD was proposed to select fault sensitive features. Furthermore, a modified features dimensionality reduction method, supervised neighborhood preserving embedding with label information (SNPEL, was proposed to realize low-dimensional representations for high-dimensional feature space. Finally, vibration signals collected from two experimental test rigs were employed to evaluate the performance of the proposed procedure. The results show that the effectiveness, adaptability, and superiority of the proposed procedure can serve as an intelligent bearing fault diagnosis system.
Wan, Renzhi; Zu, Yunxiao; Shao, Lin
2018-04-01
The blood echo signal maintained through Medical ultrasound Doppler devices would always include vascular wall pulsation signal .The traditional method to de-noise wall signal is using high-pass filter, which will also remove the lowfrequency part of the blood flow signal. Some scholars put forward a method based on region selective reduction, which at first estimates of the wall pulsation signals and then removes the wall signal from the mixed signal. Apparently, this method uses the correlation between wavelet coefficients to distinguish blood signal from wall signal, but in fact it is a kind of wavelet threshold de-noising method, whose effect is not so much ideal. In order to maintain a better effect, this paper proposes an improved method based on wavelet coefficient correlation to separate blood signal and wall signal, and simulates the algorithm by computer to verify its validity.
Gómez Valverde, Juan J.; Ortuño, Juan E.; Guerra, Pedro; Hermann, Boris; Zabihian, Behrooz; Rubio-Guivernau, José L.; Santos, Andrés.; Drexler, Wolfgang; Ledesma-Carbayo, Maria J.
2015-07-01
Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we propose a new speckle reduction process and compare it with various denoising filters with high edge-preserving potential, using several sets of dermatological OCT B-scans. To validate the performance we used a custom-designed spectral domain OCT and two different data set groups. The first group consisted in five datasets of a single B-scan captured N times (with N<20), the second were five 3D volumes of 25 Bscans. As quality metrics we used signal to noise (SNR), contrast to noise (CNR) and equivalent number of looks (ENL) ratios. Our results show that a process based on a combination of a 2D enhanced sigma digital filter and a wavelet compounding method achieves the best results in terms of the improvement of the quality metrics. In the first group of individual B-scans we achieved improvements in SNR, CNR and ENL of 16.87 dB, 2.19 and 328 respectively; for the 3D volume datasets the improvements were 15.65 dB, 3.44 and 1148. Our results suggest that the proposed enhancement process may significantly reduce speckle, increasing SNR, CNR and ENL and reducing the number of extra acquisitions of the same frame.
Optimization of wavelet decomposition for image compression and feature preservation.
Lo, Shih-Chung B; Li, Huai; Freedman, Matthew T
2003-09-01
A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.
Ren, Zhong; Liu, Guodong; Huang, Zhen
2012-11-01
The image reconstruction is a key step in medical imaging (MI) and its algorithm's performance determinates the quality and resolution of reconstructed image. Although some algorithms have been used, filter back-projection (FBP) algorithm is still the classical and commonly-used algorithm in clinical MI. In FBP algorithm, filtering of original projection data is a key step in order to overcome artifact of the reconstructed image. Since simple using of classical filters, such as Shepp-Logan (SL), Ram-Lak (RL) filter have some drawbacks and limitations in practice, especially for the projection data polluted by non-stationary random noises. So, an improved wavelet denoising combined with parallel-beam FBP algorithm is used to enhance the quality of reconstructed image in this paper. In the experiments, the reconstructed effects were compared between the improved wavelet denoising and others (directly FBP, mean filter combined FBP and median filter combined FBP method). To determine the optimum reconstruction effect, different algorithms, and different wavelet bases combined with three filters were respectively test. Experimental results show the reconstruction effect of improved FBP algorithm is better than that of others. Comparing the results of different algorithms based on two evaluation standards i.e. mean-square error (MSE), peak-to-peak signal-noise ratio (PSNR), it was found that the reconstructed effects of the improved FBP based on db2 and Hanning filter at decomposition scale 2 was best, its MSE value was less and the PSNR value was higher than others. Therefore, this improved FBP algorithm has potential value in the medical imaging.
Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships.
Silva, Andre A; Gupta, Shalabh; Bazzi, Ali M; Ulatowski, Arthur
2017-09-22
Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Optical Flow of Small Objects Using Wavelets, Bootstrap Methods, and Synthetic Discriminant Filters
National Research Council Canada - National Science Library
Hewer, Gary
1997-01-01
...) targets in highly cluttered and noisy environments. In this paper; we present a novel wavelet detection algorithm which incorporates adaptive CFAR detection statistics using the bootstrap method...
Hui, Z.; Cheng, P.; Ziggah, Y. Y.; Nie, Y.
2018-04-01
Filtering is a key step for most applications of airborne LiDAR point clouds. Although lots of filtering algorithms have been put forward in recent years, most of them suffer from parameters setting or thresholds adjusting, which will be time-consuming and reduce the degree of automation of the algorithm. To overcome this problem, this paper proposed a threshold-free filtering algorithm based on expectation-maximization. The proposed algorithm is developed based on an assumption that point clouds are seen as a mixture of Gaussian models. The separation of ground points and non-ground points from point clouds can be replaced as a separation of a mixed Gaussian model. Expectation-maximization (EM) is applied for realizing the separation. EM is used to calculate maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or object can be computed. After several iterations, point clouds can be labelled as the component with a larger likelihood. Furthermore, intensity information was also utilized to optimize the filtering results acquired using the EM method. The proposed algorithm was tested using two different datasets used in practice. Experimental results showed that the proposed method can filter non-ground points effectively. To quantitatively evaluate the proposed method, this paper adopted the dataset provided by the ISPRS for the test. The proposed algorithm can obtain a 4.48 % total error which is much lower than most of the eight classical filtering algorithms reported by the ISPRS.
Arzhantsev, Sergey; Li, Xiang; Kauffman, John F
2011-02-01
We introduce a new method for analysis of X-ray fluorescence (XRF) spectra based on continuous wavelet transform filters, and the method is applied to the determination of toxic metals in pharmaceutical materials using hand-held XRF spectrometers. The method uses the continuous wavelet transform to filter the signal and noise components of the spectrum. We present a limit test that compares the wavelet domain signal-to-noise ratios at the energies of the elements of interest to an empirically determined signal-to-noise decision threshold. The limit test is advantageous because it does not require the user to measure calibration samples prior to measurement, though system suitability tests are still recommended. The limit test was evaluated in a collaborative study that involved five different hand-held XRF spectrometers used by multiple analysts in six separate laboratories across the United States. In total, more than 1200 measurements were performed. The detection limits estimated for arsenic, lead, mercury, and chromium were 8, 14, 20, and 150 μg/g, 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...
Directory of Open Access Journals (Sweden)
Rongxiao Wang
2017-09-01
Full Text Available The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.
Wavelets and triple difference as a mathematical method for filtering and mitigation of DGPS errors
Directory of Open Access Journals (Sweden)
Aly M. El-naggar
2015-12-01
Wavelet spectral techniques can separate GPS signals into sub-bands where different errors can be separated and mitigated. The main goal of this paper was the development and implementation of DGPS error mitigation techniques using triple difference and wavelet. This paper studies, analyzes and provides new techniques that will help mitigate these errors in the frequency domain. The proposed technique applied to smooth noise for GPS receiver positioning data is based upon the analysis of wavelet transform (WT. The technique is applied using wavelet as a de-noising tool to tackle the high-frequency errors in the triple difference domain and to obtain a de-noised triple difference signal that can be used in a positioning calculation.
Directory of Open Access Journals (Sweden)
M. Akhoondzadeh
2011-04-01
Full Text Available Thermal anomaly is known as a significant precursor of strong earthquakes, therefore Land Surface Temperature (LST time series have been analyzed in this study to locate relevant anomalous variations prior to the Bam (26 December 2003, Zarand (22 February 2005 and Borujerd (31 March 2006 earthquakes. The duration of the three datasets which are comprised of MODIS LST images is 44, 28 and 46 days for the Bam, Zarand and Borujerd earthquakes, respectively. In order to exclude variations of LST from temperature seasonal effects, Air Temperature (AT data derived from the meteorological stations close to the earthquakes epicenters have been taken into account. The detection of thermal anomalies has been assessed using interquartile, wavelet transform and Kalman filter methods, each presenting its own independent property in anomaly detection. The interquartile method has been used to construct the higher and lower bounds in LST data to detect disturbed states outside the bounds which might be associated with impending earthquakes. The wavelet transform method has been used to locate local maxima within each time series of LST data for identifying earthquake anomalies by a predefined threshold. Also, the prediction property of the Kalman filter has been used in the detection process of prominent LST anomalies. The results concerning the methodology indicate that the interquartile method is capable of detecting the highest intensity anomaly values, the wavelet transform is sensitive to sudden changes, and the Kalman filter method significantly detects the highest unpredictable variations of LST. The three methods detected anomalous occurrences during 1 to 20 days prior to the earthquakes showing close agreement in results found between the different applied methods on LST data in the detection of pre-seismic anomalies. The proposed method for anomaly detection was also applied on regions irrelevant to earthquakes for which no anomaly was detected
Tu, Yiheng; Huang, Gan; Hung, Yeung Sam; Hu, Li; Hu, Yong; Zhang, Zhiguo
2013-01-01
Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems as input signals conveying a subject's intention. A fast and reliable single-trial ERP detection method can be used to develop a BCI system with both high speed and high accuracy. However, most of single-trial ERP detection methods are developed for offline EEG analysis and thus have a high computational complexity and need manual operations. Therefore, they are not applicable to practical BCI systems, which require a low-complexity and automatic ERP detection method. This work presents a joint spatial-time-frequency filter that combines common spatial patterns (CSP) and wavelet filtering (WF) for improving the signal-to-noise (SNR) of visual evoked potentials (VEP), which can lead to a single-trial ERP-based BCI.
Scott, Robert C.; Perry, Boyd, III; Pototzky, Anthony S.
1991-01-01
This paper describes and illustrates two matched-filter-theory based schemes for obtaining maximized and time-correlated gust-loads for a nonlinear airplane. The first scheme is computationally fast because it uses a simple one-dimensional search procedure to obtain its answers. The second scheme is computationally slow because it uses a more complex multidimensional search procedure to obtain its answers, but it consistently provides slightly higher maximum loads than the first scheme. Both schemes are illustrated with numerical examples involving a nonlinear control system.
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2015-10-01
Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.
SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation
Energy Technology Data Exchange (ETDEWEB)
Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D [Washington University School of Medicine, St Louis, MO (United States)
2016-06-15
Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and
SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation
International Nuclear Information System (INIS)
Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D
2016-01-01
Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and
International Nuclear Information System (INIS)
Floberg, J M; Holden, J E
2013-01-01
We introduce a method for denoising dynamic PET data, spatio-temporal expectation-maximization (STEM) filtering, that combines four-dimensional Gaussian filtering with EM deconvolution. The initial Gaussian filter suppresses noise at a broad range of spatial and temporal frequencies and EM deconvolution quickly restores the frequencies most important to the signal. We aim to demonstrate that STEM filtering can improve variance in both individual time frames and in parametric images without introducing significant bias. We evaluate STEM filtering with a dynamic phantom study, and with simulated and human dynamic PET studies of a tracer with reversible binding behaviour, [C-11]raclopride, and a tracer with irreversible binding behaviour, [F-18]FDOPA. STEM filtering is compared to a number of established three and four-dimensional denoising methods. STEM filtering provides substantial improvements in variance in both individual time frames and in parametric images generated with a number of kinetic analysis techniques while introducing little bias. STEM filtering does bias early frames, but this does not affect quantitative parameter estimates. STEM filtering is shown to be superior to the other simple denoising methods studied. STEM filtering is a simple and effective denoising method that could be valuable for a wide range of dynamic PET applications. (paper)
Energy Technology Data Exchange (ETDEWEB)
Luengo Hendriks, Cris L.; Knowles, David W.
2006-02-04
Moss et al.(2005) describe, in a recent paper, a filter thatthey use to detect lines. We noticed that the wavelet on which thisfilter is based is a difference of uniform filters. This filter is anapproximation to the second derivative operator, which is commonlyimplemented as the Laplace of Gaussian (or Marr-Hildreth) operator (Marr&Hildreth, 1980; Jahne, 2002), Figure 1. We have compared Moss'filter with 1) the Laplace of Gaussian operator, 2) an approximation ofthe Laplace of Gaussian using uniform filters, and 3) a few common noisereduction filters. The Laplace-like operators detect lines by suppressingimage features both larger and smaller than the filter size. The noisereduction filters only suppress image features smaller than the filtersize. By estimating the signal to noise ratio (SNR) and mean squaredifference (MSD) of the filtered results, we found that the filterproposed by Moss et al. does not outperform the Laplace of Gaussianoperator. We also found that for images with extreme noise content, linedetection filters perform better than the noise reduction filters whentrying to enhance line structures. In less extreme cases of noise, thestandard noise reduction filters perform significantly better than boththe Laplace of Gaussian and Moss' filter.
Directory of Open Access Journals (Sweden)
Debesh Jha
2017-01-01
Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.
Directory of Open Access Journals (Sweden)
P.B. Chopade
2014-05-01
Full Text Available This paper presents image super-resolution scheme based on sub-pixel image registration by the design of a specific class of dyadic-integer-coefficient based wavelet filters derived from the construction of a half-band polynomial. First, the integer-coefficient based half-band polynomial is designed by the splitting approach. Next, this designed half-band polynomial is factorized and assigned specific number of vanishing moments and roots to obtain the dyadic-integer coefficients low-pass analysis and synthesis filters. The possibility of these dyadic-integer coefficients based wavelet filters is explored in the field of image super-resolution using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by CCD array of camera. The discrete wavelet transform (DWT obtained from the designed coefficients is applied on these two low-resolution images to obtain the high resolution image. The developed approach is validated by comparing the quality metrics with existing filter banks.
Do wavelet filters provide more accurate estimates of reverberation times at low frequencies
DEFF Research Database (Denmark)
Sobreira Seoane, Manuel A.; Pérez Cabo, David; Agerkvist, Finn T.
2016-01-01
It has been amply demonstrated in the literature that it is not possible to measure acoustic decays without significant errors for low BT values (narrow filters and or low reverberation times). Recently, it has been shown how the main source of distortion in the time envelope of the acoustic deca...
Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel'farb, Georgy; El-Baz, Ayman; Ovechkin, Alexander
2013-07-18
Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.
Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki
2015-01-01
In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532
Sánchez-Úbeda, Juan Pedro; Calvache, María Luisa; Duque, Carlos; López-Chicano, Manuel
2016-11-01
A new methodology has been developed to obtain tidal-filtered time series of groundwater levels in coastal aquifers. Two methods used for oceanography processing and forecasting of sea level data were adapted for this purpose and compared: HA (Harmonic Analysis) and CWT (Continuous Wavelet Transform). The filtering process is generally comprised of two main steps: the detection and fitting of the major tide constituents through the decomposition of the original signal and the subsequent extraction of the complete tidal oscillations. The abilities of the optional HA and CWT methods to decompose and extract the tidal oscillations were assessed by applying them to the data from two piezometers at different depths close to the shoreline of a Mediterranean coastal aquifer (Motril-Salobreña, SE Spain). These methods were applied to three time series of different lengths (one month, one year, and 3.7 years of hourly data) to determine the range of detected frequencies. The different lengths of time series were also used to determine the fit accuracies of the tidal constituents for both the sea level and groundwater heads measurements. The detected tidal constituents were better resolved with increasing depth in the aquifer. The application of these methods yielded a detailed resolution of the tidal components, which enabled the extraction of the major tidal constituents of the sea level measurements from the groundwater heads (e.g., semi-diurnal, diurnal, fortnightly, monthly, semi-annual and annual). In the two wells studied, the CWT method was shown to be a more effective method than HA for extracting the tidal constituents of highest and lowest frequencies from groundwater head measurements.
Health Assessment of Cooling Fan Bearings Using Wavelet-Based Filtering
Directory of Open Access Journals (Sweden)
Qiang Miao
2012-12-01
Full Text Available As commonly used forced convection air cooling devices in electronics, cooling fans are crucial for guaranteeing the reliability of electronic systems. In a cooling fan assembly, fan bearing failure is a major failure mode that causes excessive vibration, noise, reduction in rotation speed, locked rotor, failure to start, and other problems; therefore, it is necessary to conduct research on the health assessment of cooling fan bearings. This paper presents a vibration-based fan bearing health evaluation method using comblet filtering and exponentially weighted moving average. A new health condition indicator (HCI for fan bearing degradation assessment is proposed. In order to collect the vibration data for validation of the proposed method, a cooling fan accelerated life test was conducted to simulate the lubricant starvation of fan bearings. A comparison between the proposed method and methods in previous studies (i.e., root mean square, kurtosis, and fault growth parameter was carried out to assess the performance of the HCI. The analysis results suggest that the HCI can identify incipient fan bearing failures and describe the bearing degradation process. Overall, the work presented in this paper provides a promising method for fan bearing health evaluation and prognosis.
International Nuclear Information System (INIS)
Williams, D.
2004-01-01
Recent steps taken by the petroleum industry to control end-of-pipe pollution and to minimize waste at the source by changing over to self-cleaning, backwashable filters instead of the traditional disposable filters such as bags or cartridges, are discussed. Various self-cleaning filter systems and their advantages are described, using REACTOGARD which provides total protection for catalysts in fixed-bed reactors and EROSIONGARD, used in fluidized catalytic crackers, as examples. Both filter systems have been developed by RPA Process Technologies, Inc., a global leader in industrial filtration systems. Major advantages attributed to self-cleaning filters include significantly reduced costs through reducing the number of catalyst change-out cycles, maximized platform productivity through virtually eliminating the risk of plugged wells, reduced waste through ability to recycle cleaning liquids, increased profits through shorter return-on-investment cycles, reduced emissions and reduced future liability through higher safety in the workplace; also reduced pollution of landfill sites. 1 photo
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
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Fonarev, Alexander
2017-02-07
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation - a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method\\'s error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.
International Nuclear Information System (INIS)
Senin, Nicola; Leach, Richard K; Pini, Stefano; Blunt, Liam A
2015-01-01
Areal topography segmentation plays a fundamental role in those surface metrology applications concerned with the characterisation of individual topography features. Typical scenarios include the dimensional inspection and verification of micro-structured surface features, and the identification and characterisation of localised defects and other random singularities. While morphological segmentation into hills or dales is the only partitioning operation currently endorsed by the ISO specification standards on surface texture metrology, many other approaches are possible, in particular adapted from the literature on digital image segmentation. In this work an original segmentation approach is introduced and discussed, where topography partitioning is driven by information collected through the application of texture characterisation transforms popular in digital image processing. Gabor filters, wavelets and pyramid decompositions are investigated and applied to a selected set of test cases. The behaviour, performance and limitations of the proposed approach are discussed from the viewpoint of the identification and extraction of individual surface topography features. (paper)
Discrete wavelet transform: a tool in smoothing kinematic data.
Ismail, A R; Asfour, S S
1999-03-01
Motion analysis systems typically introduce noise to the displacement data recorded. Butterworth digital filters have been used to smooth the displacement data in order to obtain smoothed velocities and accelerations. However, this technique does not yield satisfactory results, especially when dealing with complex kinematic motions that occupy the low- and high-frequency bands. The use of the discrete wavelet transform, as an alternative to digital filters, is presented in this paper. The transform passes the original signal through two complementary low- and high-pass FIR filters and decomposes the signal into an approximation function and a detail function. Further decomposition of the signal results in transforming the signal into a hierarchy set of orthogonal approximation and detail functions. A reverse process is employed to perfectly reconstruct the signal (inverse transform) back from its approximation and detail functions. The discrete wavelet transform was applied to the displacement data recorded by Pezzack et al., 1977. The smoothed displacement data were twice differentiated and compared to Pezzack et al.'s acceleration data in order to choose the most appropriate filter coefficients and decomposition level on the basis of maximizing the percentage of retained energy (PRE) and minimizing the root mean square error (RMSE). Daubechies wavelet of the fourth order (Db4) at the second decomposition level showed better results than both the biorthogonal and Coiflet wavelets (PRE = 97.5%, RMSE = 4.7 rad s-2). The Db4 wavelet was then used to compress complex displacement data obtained from a noisy mathematically generated function. Results clearly indicate superiority of this new smoothing approach over traditional filters.
Yan, Xin-Guo; Xie, Chi; Wang, Gang-Jin
2015-08-01
We study the topological stability of stock market network by investigating the topological robustness, namely the ability of the network to resist structural or topological changes. The stock market network is extracted by minimal spanning tree (MST) and planar maximally filtered graph (PMFG). We find that the specific delisting thresholds of the listed companies exist in both MST and PMFG networks. In comparison with MST, PMFG provides more information and is better for the aim of exploring stock market network’s robustness. The PMFG before the US sub-prime crisis (i.e., from June 2005 to May 2007) has a stronger robustness against the intentional topological damage than the other two sub-periods (i.e., from June 2007 to May 2009 and from June 2009 to May 2011). We also find that the nonfractal property exists in MSTs of S&P 500, i.e., the highly connected nodes link with each other directly, which indicates that the MSTs are vulnerable to the removal of such important nodes. Moreover, the financial institutions and high technology companies are important in maintaining the stability of S&P 500 network.
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...
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...
Hu, L; Liang, M; Mouraux, A; Wise, R G; Hu, Y; Iannetti, G D
2011-12-01
Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLR(d)) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLR(d) method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLR(d) approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLR(d) effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLR(d) can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.
Texture analysis using Gabor wavelets
Naghdy, Golshah A.; Wang, Jian; Ogunbona, Philip O.
1996-04-01
Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for texture classification. The 'rosette' spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles.
Fast reversible wavelet image compressor
Kim, HyungJun; Li, Ching-Chung
1996-10-01
We present a unified image compressor with spline biorthogonal wavelets and dyadic rational filter coefficients which gives high computational speed and excellent compression performance. Convolutions with these filters can be preformed by using only arithmetic shifting and addition operations. Wavelet coefficients can be encoded with an arithmetic coder which also uses arithmetic shifting and addition operations. Therefore, from the beginning to the end, the while encoding/decoding process can be done within a short period of time. The proposed method naturally extends form the lossless compression to the lossy but high compression range and can be easily adapted to the progressive reconstruction.
National Research Council Canada - National Science Library
Xiaojuan, Ou; Wei, Zhou; Jianguo, Yu
2005-01-01
In this paper, we pay our attention to the multiscale kalman algorithm based on correlation structure of the discrete wavelet coefficients for the restoration of the GPS common-view observation data...
Kong, Yun; Wang, Tianyang; Li, Zheng; Chu, Fulei
2017-09-01
Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.
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
Online Wavelet Complementary velocity Estimator.
Righettini, Paolo; Strada, Roberto; KhademOlama, Ehsan; Valilou, Shirin
2018-02-01
In this paper, we have proposed a new online Wavelet Complementary velocity Estimator (WCE) over position and acceleration data gathered from an electro hydraulic servo shaking table. This is a batch estimator type that is based on the wavelet filter banks which extract the high and low resolution of data. The proposed complementary estimator combines these two resolutions of velocities which acquired from numerical differentiation and integration of the position and acceleration sensors by considering a fixed moving horizon window as input to wavelet filter. Because of using wavelet filters, it can be implemented in a parallel procedure. By this method the numerical velocity is estimated without having high noise of differentiators, integration drifting bias and with less delay which is suitable for active vibration control in high precision Mechatronics systems by Direct Velocity Feedback (DVF) methods. This method allows us to make velocity sensors with less mechanically moving parts which makes it suitable for fast miniature structures. We have compared this method with Kalman and Butterworth filters over stability, delay and benchmarked them by their long time velocity integration for getting back the initial position data. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Optimization design of biorthogonal wavelets for embedded image coding
Lin, Z.; Zheng, N.; Liu, Y.; Wetering, van de H.M.M.
2007-01-01
We present here a simple technique for parametrization of popular biorthogonal wavelet filter banks (BWFBs) having vanishing moments (VMs) of arbitrary multiplicity. Given a prime wavelet filter with VMs of arbitrary multiplicity, after formulating it as a trigonometric polynomial depending on two
A novel optimization design approach for Contourlet directional filter banks
Zhang, Songjun; Yang, Guoan; Cheng, Zhengxing; van de Wetering, H.M.M.; Ikuta, Chihiro; Nishio, Yoshifumi
2014-01-01
A Contourlet transform, an expansion of a wavelet transform, is a double filter bank structure composed of Laplacian Pyramid and directional filter banks. Several wavelet filters of preferable performance have been developed for wavelet transforms, e.g. CDF (Cohen, Daubechies and Feauveau) 9/7
Certain problems concerning wavelets and wavelets packets
International Nuclear Information System (INIS)
Siddiqi, A.H.
1995-09-01
Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs
Certain problems concerning wavelets and wavelets packets
Energy Technology Data Exchange (ETDEWEB)
Siddiqi, A H
1995-09-01
Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs.
International Nuclear Information System (INIS)
Mito, Suzuko; Magota, Keiichi; Arai, Hiroshi; Omote, Hidehiko; Katsuura, Hidenori; Suzuki, Kotaro; Kubo Naoki
2005-01-01
Image registration technique is becoming an increasingly important tool in SPECT. Recently, software based on mutual-information maximization has been developed for automatic multimodality image registration. The accuracy of the software is important for its application to image registration. During SPECT reconstruction, the projection data are pre-filtered in order to reduce Poisson noise, commonly using a Butterworth filter. We have investigated the dependence of the absolute accuracy of MRI-SPECT registration on the cut-off frequencies of a range of Butterworth filters. This study used a 3D Hoffman phantom (Model No. 9000, Data-spectrum Co.). For the reference volume, an magnetization prepared rapid gradient echo (MPRage) sequence was performed on a Vision MRI (Siemence, 1.5 T). For the floating volumes, SPECT data of a phantom including 99m Tc 85 kBq/mL were acquired by a GCA-9300 (Toshiba Medical Systems Co.). During SPECT, the orbito-meatal (OM) line of the phantom was tilted by 5 deg and 15 deg to mimic the incline of a patient's head. The projection data were pre-filtered with Butterworth filters (cut-off frequency varying between 0.24 to 0.94 cycles/cm in 0.02 steps, order 8). The automated registrations were performed using iNRT β version software (Nihon Medi. Co.) and the rotation angles of SPECT for registration were noted. In this study, the registrations of all SPECT data were successful. Graphs of registration rotation angles against cut-off frequencies were scattered and showed no correlation between the two. The registration rotation angles ranged with changing cut-off frequency from -0.4 deg to +3.8 deg at a 5 deg tilt and from +12.7 deg to +19.6 deg at a 15 deg tilt. The registration rotation angles showed variation even for slight differences in cut-off frequencies. The absolute errors were a few degrees for any cut-off frequency. Regardless of the cut-off frequency, automatic registration using this software provides similar results. (author)
Coogan, A.; Avanzi, F.; Akella, R.; Conklin, M. H.; Bales, R. C.; Glaser, S. D.
2017-12-01
Automatic meteorological and snow stations provide large amounts of information at dense temporal resolution, but data quality is often compromised by noise and missing values. We present a new gap-filling and cleaning procedure for networks of these stations based on Kalman filtering and expectation maximization. Our method utilizes a multi-sensor, regime-switching Kalman filter to learn a latent process that captures dependencies between nearby stations and handles sharp changes in snowfall rate. Since the latent process is inferred using observations across working stations in the network, it can be used to fill in large data gaps for a malfunctioning station. The procedure was tested on meteorological and snow data from Wireless Sensor Networks (WSN) in the American River basin of the Sierra Nevada. Data include air temperature, relative humidity, and snow depth from dense networks of 10 to 12 stations within 1 km2 swaths. Both wet and dry water years have similar data issues. Data with artificially created gaps was used to quantify the method's performance. Our multi-sensor approach performs better than a single-sensor one, especially with large data gaps, as it learns and exploits the dominant underlying processes in snowpack at each site.
Parsimonious Wavelet Kernel Extreme Learning Machine
Directory of Open Access Journals (Sweden)
Wang Qin
2015-11-01
Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.
Multidimensional signaling via wavelet packets
Lindsey, Alan R.
1995-04-01
This work presents a generalized signaling strategy for orthogonally multiplexed communication. Wavelet packet modulation (WPM) employs the basis functions from an arbitrary pruning of a full dyadic tree structured filter bank as orthogonal pulse shapes for conventional QAM symbols. The multi-scale modulation (MSM) and M-band wavelet modulation (MWM) schemes which have been recently introduced are handled as special cases, with the added benefit of an entire library of potentially superior sets of basis functions. The figures of merit are derived and it is shown that the power spectral density is equivalent to that for QAM (in fact, QAM is another special case) and hence directly applicable in existing systems employing this standard modulation. Two key advantages of this method are increased flexibility in time-frequency partitioning and an efficient all-digital filter bank implementation, making the WPM scheme more robust to a larger set of interferences (both temporal and sinusoidal) and computationally attractive as well.
Multiresolution signal decomposition transforms, subbands, and wavelets
Akansu, Ali N; Haddad, Paul R
2001-01-01
The uniqueness of this book is that it covers such important aspects of modern signal processing as block transforms from subband filter banks and wavelet transforms from a common unifying standpoint, thus demonstrating the commonality among these decomposition techniques. In addition, it covers such ""hot"" areas as signal compression and coding, including particular decomposition techniques and tables listing coefficients of subband and wavelet filters and other important properties.The field of this book (Electrical Engineering/Computer Science) is currently booming, which is, of course
Wavelet library for constrained devices
Ehlers, Johan Hendrik; Jassim, Sabah A.
2007-04-01
The wavelet transform is a powerful tool for image and video processing, useful in a range of applications. This paper is concerned with the efficiency of a certain fast-wavelet-transform (FWT) implementation and several wavelet filters, more suitable for constrained devices. Such constraints are typically found on mobile (cell) phones or personal digital assistants (PDA). These constraints can be a combination of; limited memory, slow floating point operations (compared to integer operations, most often as a result of no hardware support) and limited local storage. Yet these devices are burdened with demanding tasks such as processing a live video or audio signal through on-board capturing sensors. In this paper we present a new wavelet software library, HeatWave, that can be used efficiently for image/video processing/analysis tasks on mobile phones and PDA's. We will demonstrate that HeatWave is suitable for realtime applications with fine control and range to suit transform demands. We shall present experimental results to substantiate these claims. Finally this library is intended to be of real use and applied, hence we considered several well known and common embedded operating system platform differences; such as a lack of common routines or functions, stack limitations, etc. This makes HeatWave suitable for a range of applications and research projects.
Visibility of wavelet quantization noise
Watson, A. B.; Yang, G. Y.; Solomon, J. A.; Villasenor, J.
1997-01-01
The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.
Implementational Aspects of the Contourlet Filter Bank and Application in Image Coding
Directory of Open Access Journals (Sweden)
Truong T. Nguyen
2009-02-01
Full Text Available This paper analyzed the implementational aspects of the contourlet filter bank (or the pyramidal directional filter bank (PDFB, and considered its application in image coding. First, details of the binary tree-structured directional filter bank (DFB are presented, including a modification to minimize the phase delay factor and necessary steps for handling rectangular images. The PDFB is viewed as an overcomplete filter bank, and the directional filters are expressed in terms of polyphase components of the pyramidal filter bank and the conventional DFB. The aliasing effect of the conventional DFB and the Laplacian pyramid to the directional filters is then considered, and the conditions for reducing this effect are presented. The new filters obtained by redesigning the PDFBs satisfying these requirements have much better frequency responses. A hybrid multiscale filter bank consisting of the PDFB at higher scales and the traditional maximally decimated wavelet filter bank at lower scales is constructed to provide a sparse image representation. A novel embedded image coding system based on the image decomposition and a morphological dilation algorithm is then presented. The coding algorithm efficiently clusters the significant coefficients using progressive morphological operations. Context models for arithmetic coding are designed to exploit the intraband dependency and the correlation existing among the neighboring directional subbands. Experimental results show that the proposed coding algorithm outperforms the current state-of-the-art wavelet-based coders, such as JPEG2000, for images with directional features.
Digital transceiver implementation for wavelet packet modulation
Lindsey, Alan R.; Dill, Jeffrey C.
1998-03-01
Current transceiver designs for wavelet-based communication systems are typically reliant on analog waveform synthesis, however, digital processing is an important part of the eventual success of these techniques. In this paper, a transceiver implementation is introduced for the recently introduced wavelet packet modulation scheme which moves the analog processing as far as possible toward the antenna. The transceiver is based on the discrete wavelet packet transform which incorporates level and node parameters for generalized computation of wavelet packets. In this transform no particular structure is imposed on the filter bank save dyadic branching, and a maximum level which is specified a priori and dependent mainly on speed and/or cost considerations. The transmitter/receiver structure takes a binary sequence as input and, based on the desired time- frequency partitioning, processes the signal through demultiplexing, synthesis, analysis, multiplexing and data determination completely in the digital domain - with exception of conversion in and out of the analog domain for transmission.
Constructing New Biorthogonal Wavelet Type which Matched for Extracting the Iris Image Features
International Nuclear Information System (INIS)
Isnanto, R Rizal; Suhardjo; Susanto, Adhi
2013-01-01
Some former research have been made for obtaining a new type of wavelet. In case of iris recognition using orthogonal or biorthogonal wavelets, it had been obtained that Haar filter is most suitable to recognize the iris image. However, designing the new wavelet should be done to find a most matched wavelet to extract the iris image features, for which we can easily apply it for identification, recognition, or authentication purposes. In this research, a new biorthogonal wavelet was designed based on Haar filter properties and Haar's orthogonality conditions. As result, it can be obtained a new biorthogonal 5/7 filter type wavelet which has a better than other types of wavelets, including Haar, to extract the iris image features based on its mean-squared error (MSE) and Euclidean distance parameters.
Energy Technology Data Exchange (ETDEWEB)
Szu, H.; Hsu, C. [Univ. of Southwestern Louisiana, Lafayette, LA (United States)
1996-12-31
Human sensors systems (HSS) may be approximately described as an adaptive or self-learning version of the Wavelet Transforms (WT) that are capable to learn from several input-output associative pairs of suitable transform mother wavelets. Such an Adaptive WT (AWT) is a redundant combination of mother wavelets to either represent or classify inputs.
Information theoretical assessment of visual communication with wavelet coding
Rahman, Zia-ur
1995-06-01
A visual communication channel can be characterized by the efficiency with which it conveys information, and the quality of the images restored from the transmitted data. Efficient data representation requires the use of constraints of the visual communication channel. Our information theoretic analysis combines the design of the wavelet compression algorithm with the design of the visual communication channel. Shannon's communication theory, Wiener's restoration filter, and the critical design factors of image gathering and display are combined to provide metrics for measuring the efficiency of data transmission, and for quantitatively assessing the visual quality of the restored image. These metrics are: a) the mutual information (Eta) between the radiance the radiance field and the restored image, and b) the efficiency of the channel which can be roughly measured by as the ratio (Eta) /H, where H is the average number of bits being used to transmit the data. Huck, et al. (Journal of Visual Communication and Image Representation, Vol. 4, No. 2, 1993) have shown that channels desinged to maximize (Eta) , also maximize. Our assessment provides a framework for designing channels which provide the highest possible visual quality for a given amount of data under the critical design limitations of the image gathering and display devices. Results show that a trade-off exists between the maximum realizable information of the channel and its efficiency: an increase in one leads to a decrease in the other. The final selection of which of these quantities to maximize is, of course, application dependent.
Complex Wavelet Based Modulation Analysis
DEFF Research Database (Denmark)
Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt
2008-01-01
Low-frequency modulation of sound carry important information for speech and music. The modulation spectrum i commonly obtained by spectral analysis of the sole temporal envelopes of the sub-bands out of a time-frequency analysis. Processing in this domain usually creates undesirable distortions...... 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...
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 ...
Skopina, Maria; Protasov, Vladimir
2016-01-01
This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...
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...
Wavelets in scientific computing
DEFF Research Database (Denmark)
Nielsen, Ole Møller
1998-01-01
the FWT can be used as a front-end for efficient image compression schemes. Part II deals with vector-parallel implementations of several variants of the Fast Wavelet Transform. We develop an efficient and scalable parallel algorithm for the FWT and derive a model for its performance. Part III...... supported wavelets in the context of multiresolution analysis. These wavelets are particularly attractive because they lead to a stable and very efficient algorithm, namely the fast wavelet transform (FWT). We give estimates for the approximation characteristics of wavelets and demonstrate how and why...... is an investigation of the potential for using the special properties of wavelets for solving partial differential equations numerically. Several approaches are identified and two of them are described in detail. The algorithms developed are applied to the nonlinear Schrödinger equation and Burgers' equation...
International Nuclear Information System (INIS)
Dremin, Igor M; Ivanov, Oleg V; Nechitailo, Vladimir A
2001-01-01
This review paper is intended to give a useful guide for those who want to apply the discrete wavelet transform in practice. The notion of wavelets and their use in practical computing and various applications are briefly described, but rigorous proofs of mathematical statements are omitted, and the reader is just referred to the corresponding literature. The multiresolution analysis and fast wavelet transform have become a standard procedure for dealing with discrete wavelets. The proper choice of a wavelet and use of nonstandard matrix multiplication are often crucial for the achievement of a goal. Analysis of various functions with the help of wavelets allows one to reveal fractal structures, singularities etc. The wavelet transform of operator expressions helps solve some equations. In practical applications one often deals with the discretized functions, and the problem of stability of the wavelet transform and corresponding numerical algorithms becomes important. After discussing all these topics we turn to practical applications of the wavelet machinery. They are so numerous that we have to limit ourselves to a few examples only. The authors would be grateful for any comments which would move us closer to the goal proclaimed in the first phrase of the abstract. (reviews of topical problems)
International Nuclear Information System (INIS)
Rodrigues, Miesher L.; Serra, Andre da S.; He, Zhong; Zhu, Yuefeng
2009-01-01
3-D pixelated semiconductor detectors are used in radiation detection applications requiring spectroscopic and imaging information from radiation sources. Reconstruction algorithms used to determine direction and energy of incoming gamma rays can be improved by reducing electronic noise and using optimum filtering techniques. Position information can be improved by achieving sub-pixel resolution. Electronic noise is the limiting factor. Achieving sub-pixel resolution - position of the interaction better than one pixel pitch - in 3-D pixelated semiconductor detectors is a challenging task due to the fast transient characteristics of these signals. This work addresses two fundamental questions: the first is to determine the optimum filter, while the second is to estimate the achievable sub-pixel resolution using this filter. It is shown that the matched filter is the optimum filter when applying the signal-to-noise ratio criteria. Also, non-optimum filters are studied. The framework of 3-D waveform simulation using the Shockley-Ramo Theorem and the Hecht Equation for electron and hole trapping is presented in this work. This waveform simulator can be used to analyze current detectors as well as explore new ideas and concepts in future work. Numerical simulations show that assuming an electronic noise of 3.3 keV it is possible to subdivide the pixel region into 5x5 sub-pixels. After analyzing these results, it is suggested that sub-pixel information can also improve energy resolution. Current noise levels present the major drawback to both achieve sub-pixel resolution as well as improve energy resolution below the current limits. (author)
Application of wavelet transform to seismic data; Wavelet henkan no jishin tansa eno tekiyo
Energy Technology Data Exchange (ETDEWEB)
Nakagami, K; Murayama, R; Matsuoka, T [Japan National Oil Corp., Tokyo (Japan)
1996-05-01
Introduced herein is the use of the wavelet transform in the field of seismic exploration. Among applications so far made, there are signal filtering, break point detection, data compression, and the solution of finite differential equations in the wavelet domain. In the field of data compression in particular, some examples of practical application have been introduced already. In seismic exploration, it is expected that the wavelet transform will separate signals and noises in data in a way different from the Fourier transform. The continuous wavelet transform displays time change in frequency easy to read, but is not suitable for the analysis and processing large quantities of data. On the other hand, the discrete wavelet transform, being an orthogonal transform, can handle large quantities of data. As compared with the conventional Fourier transform that handles only the frequency domain, the wavelet transform handles the time domain as well as the frequency domain, and therefore is more convenient in handling unsteady signals. 9 ref., 8 figs.
Wavelet based methods for improved wind profiler signal processing
Directory of Open Access Journals (Sweden)
V. Lehmann
2001-08-01
Full Text Available In this paper, we apply wavelet thresholding for removing automatically ground and intermittent clutter (airplane echoes from wind profiler radar data. Using the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which allow us to identify the coefficients relevant for clutter and to suppress them in order to obtain filtered reconstructions.Key words. Meteorology and atmospheric dynamics (instruments and techniques – Radio science (remote sensing; signal processing
Digital Correlation based on Wavelet Transform for Image Detection
International Nuclear Information System (INIS)
Barba, L; Vargas, L; Torres, C; Mattos, L
2011-01-01
In this work is presented a method for the optimization of digital correlators to improve the characteristic detection on images using wavelet transform as well as subband filtering. It is proposed an approach of wavelet-based image contrast enhancement in order to increase the performance of digital correlators. The multiresolution representation is employed to improve the high frequency content of images taken into account the input contrast measured for the original image. The energy of correlation peaks and discrimination level of several objects are improved with this technique. To demonstrate the potentiality in extracting characteristics using the wavelet transform, small objects inside reference images are detected successfully.
Wavelet-domain de-noising of OCT images of human brain malignant glioma
Dolganova, I. N.; Aleksandrova, P. V.; Beshplav, S.-I. T.; Chernomyrdin, N. V.; Dubyanskaya, E. N.; Goryaynov, S. A.; Kurlov, V. N.; Reshetov, I. V.; Potapov, A. A.; Tuchin, V. V.; Zaytsev, K. I.
2018-04-01
We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes - i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.
Indian Academy of Sciences (India)
Abstract. It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf f that satisfy. ∫ fhi dμ = λi for i = 1, 2,...,...k the maximizer of entropy is an f0 that is pro- portional to exp(. ∑ ci hi ) for some choice of ci . An extension of this to a continuum of.
Indian Academy of Sciences (India)
It is shown that (i) every probability density is the unique maximizer of relative entropy in an appropriate class and (ii) in the class of all pdf that satisfy ∫ f h i d = i for i = 1 , 2 , … , … k the maximizer of entropy is an f 0 that is proportional to exp ( ∑ c i h i ) for some choice of c i . An extension of this to a continuum of ...
Chianese, D.; Colangelo, G.; D'Emilio, M.; Lanfredi, M.; Lapenna, V.; Ragosta, M.; Macchiato, M. F.
2004-01-01
Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti...
Denoising solar radiation data using coiflet wavelets
Energy Technology Data Exchange (ETDEWEB)
Karim, Samsul Ariffin Abdul, E-mail: samsul-ariffin@petronas.com.my; Janier, Josefina B., E-mail: josefinajanier@petronas.com.my; Muthuvalu, Mohana Sundaram, E-mail: mohana.muthuvalu@petronas.com.my [Department of Fundamental and Applied Sciences, Faculty of Sciences and Information Technology, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, 31750 Tronoh, Perak Darul Ridzuan (Malaysia); Hasan, Mohammad Khatim, E-mail: khatim@ftsm.ukm.my [Jabatan Komputeran Industri, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor (Malaysia); Sulaiman, Jumat, E-mail: jumat@ums.edu.my [Program Matematik dengan Ekonomi, Universiti Malaysia Sabah, Beg Berkunci 2073, 88999 Kota Kinabalu, Sabah (Malaysia); Ismail, Mohd Tahir [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM Minden, Penang (Malaysia)
2014-10-24
Signal denoising and smoothing plays an important role in processing the given signal either from experiment or data collection through observations. Data collection usually was mixed between true data and some error or noise. This noise might be coming from the apparatus to measure or collect the data or human error in handling the data. Normally before the data is use for further processing purposes, the unwanted noise need to be filtered out. One of the efficient methods that can be used to filter the data is wavelet transform. Due to the fact that the received solar radiation data fluctuates according to time, there exist few unwanted oscillation namely noise and it must be filtered out before the data is used for developing mathematical model. In order to apply denoising using wavelet transform (WT), the thresholding values need to be calculated. In this paper the new thresholding approach is proposed. The coiflet2 wavelet with variation diminishing 4 is utilized for our purpose. From numerical results it can be seen clearly that, the new thresholding approach give better results as compare with existing approach namely global thresholding value.
Fractional Calculus and Shannon Wavelet
Directory of Open Access Journals (Sweden)
Carlo Cattani
2012-01-01
Full Text Available An explicit analytical formula for the any order fractional derivative of Shannon wavelet is given as wavelet series based on connection coefficients. So that for any 2(ℝ function, reconstructed by Shannon wavelets, we can easily define its fractional derivative. The approximation error is explicitly computed, and the wavelet series is compared with Grünwald fractional derivative by focusing on the many advantages of the wavelet method, in terms of rate of convergence.
Wavelet analysis in neurodynamics
International Nuclear Information System (INIS)
Pavlov, Aleksei N; Hramov, Aleksandr E; Koronovskii, Aleksei A; Sitnikova, Evgenija Yu; Makarov, Valeri A; Ovchinnikov, Alexey A
2012-01-01
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. (reviews of topical problems)
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.
Castro, Liliana Raquel; Castro, Silvia Mabel
1995-01-01
Se presenta una introducción a la teorfa de wavelets. Ademas, se da una revisión histórica de cómo fueron introducidas las wavelets para la representación de funciones. Se efectúa una comparación entre la transformada wavelet y la transformada de Fourier. Por último, se presentan también algunas de los múltiples aplicaciones de esta nueva herramienta de análisis armónico.
Filtering, Coding, and Compression with Malvar Wavelets
1993-12-01
speech coding techniques being investigated by the military (38). Imagery: Space imagery often requires adaptive restoration to deblur out-of-focus...and blurred image, find an estimate of the ideal image using a priori information about the blur, noise , and the ideal image" (12). The research for...recording can be described as the original signal convolved with impulses , which appear as echoes in the seismic event. The term deconvolution indicates
The discrete Kalman filtering approach for seismic signals deconvolution
International Nuclear Information System (INIS)
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-01-01
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
Blatter, Christian
1998-01-01
The Wavelet Transform has stimulated research that is unparalleled since the invention of the Fast Fourier Transform and has opened new avenues of applications in signal processing, image compression, radiology, cardiology, and many other areas. This book grew out of a short course for mathematics students at the ETH in Zurich; it provides a solid mathematical foundation for the broad range of applications enjoyed by the wavelet transform. Numerous illustrations and fully worked out examples enhance the book.
Wavelet-Based Bayesian Methods for Image Analysis and Automatic Target Recognition
National Research Council Canada - National Science Library
Nowak, Robert
2001-01-01
.... We have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution problems using Bayesian image models and an alternating-maximation method...
Directional dual-tree rational-dilation complex wavelet transform.
Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin
2014-01-01
Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.
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 ...
The De-Noising of Sonic Echo Test Data through Wavelet Transform Reconstruction
Directory of Open Access Journals (Sweden)
J.N. Watson
1999-01-01
Full Text Available This paper presents the results of feasibility study into the application of the wavelet transform signal processing method to sonic based non-destructive testing techniques. Finite element generated data from cast in situ foundation piles were collated and processed using both continuous and discrete wavelet transform techniques. Results were compared with conventional Fourier based methods. The discrete Daubechies wavelets and the continuous Mexican hat wavelet were used and their relative merits investigated. It was found that both the continuous Mexican hat and discrete Daubechies D8 wavelets were significantly better at locating the pile toe compared than the Fourier filtered case. The wavelet transform method was then applied to field test data and found to be successful in facilitating the detection of the pile toe.
Auditory ERB like admissible wavelet packet features for TIMIT phoneme recognition
Directory of Open Access Journals (Sweden)
P.K. Sahu
2014-09-01
Full Text Available In recent years wavelet transform has been found to be an effective tool for time–frequency analysis. Wavelet transform has been used as feature extraction in speech recognition applications and it has proved to be an effective technique for unvoiced phoneme classification. In this paper a new filter structure using admissible wavelet packet is analyzed for English phoneme recognition. These filters have the benefit of having frequency bands spacing similar to the auditory Equivalent Rectangular Bandwidth (ERB scale. Central frequencies of ERB scale are equally distributed along the frequency response of human cochlea. A new sets of features are derived using wavelet packet transform's multi-resolution capabilities and found to be better than conventional features for unvoiced phoneme problems. Some of the noises from NOISEX-92 database has been used for preparing the artificial noisy database to test the robustness of wavelet based features.
Target recognition by wavelet transform
International Nuclear Information System (INIS)
Li Zhengdong; He Wuliang; Zheng Xiaodong; Cheng Jiayuan; Peng Wen; Pei Chunlan; Song Chen
2002-01-01
Wavelet transform has an important character of multi-resolution power, which presents pyramid structure, and this character coincides the way by which people distinguish object from coarse to fineness and from large to tiny. In addition to it, wavelet transform benefits to reducing image noise, simplifying calculation, and embodying target image characteristic point. A method of target recognition by wavelet transform is provided
Casson, Alexander J
2015-12-17
Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g(m)C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.
Directory of Open Access Journals (Sweden)
Alexander J. Casson
2015-12-01
Full Text Available Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via g m C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram and EEG (electroencephalogram signals recorded from humans.
International Nuclear Information System (INIS)
Ludu, A.; Greiner, M.
1995-09-01
A non-linear associative algebra is realized in terms of translation and dilation operators, and a wavelet structure generating algebra is obtained. We show that this algebra is a q-deformation of the Fourier series generating algebra, and reduces to this for certain value of the deformation parameter. This algebra is also homeomorphic with the q-deformed su q (2) algebra and some of its extensions. Through this algebraic approach new methods for obtaining the wavelets are introduced. (author). 20 refs
Electromagnetic spatial coherence wavelets
International Nuclear Information System (INIS)
Castaneda, R.; Garcia-Sucerquia, J.
2005-10-01
The recently introduced concept of spatial coherence wavelets is generalized for describing the propagation of electromagnetic fields in the free space. For this aim, the spatial coherence wavelet tensor is introduced as an elementary amount, in terms of which the formerly known quantities for this domain can be expressed. It allows analyzing the relationship between the spatial coherence properties and the polarization state of the electromagnetic wave. This approach is completely consistent with the recently introduced unified theory of coherence and polarization for random electromagnetic beams, but it provides a further insight about the causal relationship between the polarization states at different planes along the propagation path. (author)
Wavelets in functional data analysis
Morettin, Pedro A; Vidakovic, Brani
2017-01-01
Wavelet-based procedures are key in many areas of statistics, applied mathematics, engineering, and science. This book presents wavelets in functional data analysis, offering a glimpse of problems in which they can be applied, including tumor analysis, functional magnetic resonance and meteorological data. Starting with the Haar wavelet, the authors explore myriad families of wavelets and how they can be used. High-dimensional data visualization (using Andrews' plots), wavelet shrinkage (a simple, yet powerful, procedure for nonparametric models) and a selection of estimation and testing techniques (including a discussion on Stein’s Paradox) make this a highly valuable resource for graduate students and experienced researchers alike.
Wavelet-LMS algorithm-based echo cancellers
Seetharaman, Lalith K.; Rao, Sathyanarayana S.
2002-12-01
This paper presents Echo Cancellers based on the Wavelet-LMS Algorithm. The performance of the Least Mean Square Algorithm in Wavelet transform domain is observed and its application in Echo cancellation is analyzed. The Widrow-Hoff Least Mean Square Algorithm is most widely used algorithm for Adaptive filters that function as Echo Cancellers. The present day communication signals are widely non-stationary in nature and some errors crop up when Least Mean Square Algorithm is used for the Echo Cancellers handling such signals. The analysis of non-stationary signals often involves a compromise between how well transitions or discontinuities can be located. The multi-scale or multi-resolution of signal analysis, which is the essence of wavelet transform, makes Wavelets popular in non-stationary signal analysis. In this paper, we present a Wavelet-LMS algorithm wherein the wavelet coefficients of a signal are modified adaptively using the Least Mean Square Algorithm and then reconstructed to give an Echo-free signal. The Echo Canceller based on this Algorithm is found to have a better convergence and a comparatively lesser MSE (Mean Square error).
Quality Variation Control for Three-Dimensional Wavelet-Based Video Coders
Directory of Open Access Journals (Sweden)
Vidhya Seran
2007-02-01
Full Text Available The fluctuation of quality in time is a problem that exists in motion-compensated-temporal-filtering (MCTF- based video coding. The goal of this paper is to design a solution for overcoming the distortion fluctuation challenges faced by wavelet-based video coders. We propose a new technique for determining the number of bits to be allocated to each temporal subband in order to minimize the fluctuation in the quality of the reconstructed video. Also, the wavelet filter properties are explored to design suitable scaling coefficients with the objective of smoothening the temporal PSNR. The biorthogonal 5/3 wavelet filter is considered in this paper and experimental results are presented for 2D+t and t+2D MCTF wavelet coders.
Quality Variation Control for Three-Dimensional Wavelet-Based Video Coders
Directory of Open Access Journals (Sweden)
Seran Vidhya
2007-01-01
Full Text Available The fluctuation of quality in time is a problem that exists in motion-compensated-temporal-filtering (MCTF- based video coding. The goal of this paper is to design a solution for overcoming the distortion fluctuation challenges faced by wavelet-based video coders. We propose a new technique for determining the number of bits to be allocated to each temporal subband in order to minimize the fluctuation in the quality of the reconstructed video. Also, the wavelet filter properties are explored to design suitable scaling coefficients with the objective of smoothening the temporal PSNR. The biorthogonal 5/3 wavelet filter is considered in this paper and experimental results are presented for 2D+t and t+2D MCTF wavelet coders.
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.
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...
Singh, Jaskaran; Darpe, A. K.; Singh, S. P.
2018-02-01
Local damage in rolling element bearings usually generates periodic impulses in vibration signals. The severity, repetition frequency and the fault excited resonance zone by these impulses are the key indicators for diagnosing bearing faults. In this paper, a methodology based on over complete rational dilation wavelet transform (ORDWT) is proposed, as it enjoys a good shift invariance. ORDWT offers flexibility in partitioning the frequency spectrum to generate a number of subbands (filters) with diverse bandwidths. The selection of the optimal filter that perfectly overlaps with the bearing fault excited resonance zone is based on the maximization of a proposed impulse detection measure "Temporal energy operated auto correlated kurtosis". The proposed indicator is robust and consistent in evaluating the impulsiveness of fault signals in presence of interfering vibration such as heavy background noise or sporadic shocks unrelated to the fault or normal operation. The structure of the proposed indicator enables it to be sensitive to fault severity. For enhanced fault classification, an autocorrelation of the energy time series of the signal filtered through the optimal subband is proposed. The application of the proposed methodology is validated on simulated and experimental data. The study shows that the performance of the proposed technique is more robust and consistent in comparison to the original fast kurtogram and wavelet kurtogram.
Zhang, Hong; Zhang, Sheng; Wang, Ping; Qin, Yuzhe; Wang, Huifeng
2017-07-01
Particulate matter with aerodynamic diameter below 10 μm (PM 10 ) 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-term series of the PM 10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM 10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM 10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM 10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM 10 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 PM 10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM 10 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 multiple-time-scale prediction. Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM 10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM 10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM 10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM 10 forecasting field.
Generalized exact holographic mapping with wavelets
Lee, Ching Hua
2017-12-01
The idea of renormalization and scale invariance is pervasive across disciplines. It has not only drawn numerous surprising connections between physical systems under the guise of holographic duality, but has also inspired the development of wavelet theory now widely used in signal processing. Synergizing on these two developments, we describe in this paper a generalized exact holographic mapping that maps a generic N -dimensional lattice system to a (N +1 )-dimensional holographic dual, with the emergent dimension representing scale. In previous works, this was achieved via the iterations of the simplest of all unitary mappings, the Haar mapping, which fails to preserve the form of most Hamiltonians. By taking advantage of the full generality of biorthogonal wavelets, our new generalized holographic mapping framework is able to preserve the form of a large class of lattice Hamiltonians. By explicitly separating features that are fundamentally associated with the physical system from those that are basis specific, we also obtain a clearer understanding of how the resultant bulk geometry arises. For instance, the number of nonvanishing moments of the high-pass wavelet filter is revealed to be proportional to the radius of the dual anti-de Sitter space geometry. We conclude by proposing modifications to the mapping for systems with generic Fermi pockets.
Adaptive Image Transmission Scheme over Wavelet-Based OFDM System
Institute of Scientific and Technical Information of China (English)
GAOXinying; YUANDongfeng; ZHANGHaixia
2005-01-01
In this paper an adaptive image transmission scheme is proposed over Wavelet-based OFDM (WOFDM) system with Unequal error protection (UEP) by the design of non-uniform signal constellation in MLC. Two different data division schemes: byte-based and bitbased, are analyzed and compared. Different bits are protected unequally according to their different contribution to the image quality in bit-based data division scheme, which causes UEP combined with this scheme more powerful than that with byte-based scheme. Simulation results demonstrate that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality. Furthermore, by considering the tradeoff of complexity and BER performance, Haar wavelet with the shortest compactly supported filter length is the most suitable one among orthogonal Daubechies wavelet series in our proposed system.
International Nuclear Information System (INIS)
Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H.
2012-01-01
The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.
Energy Technology Data Exchange (ETDEWEB)
Zahra, Noor e; Sevindir, Huliya A.; Aslan, Zafar; Siddiqi, A. H. [Sharda University, SET, Department of Electronics and Communication, Knowledge Park 3rd, Gr. Noida (India); University of Kocaeli, Department of Mathematics, 41380 Kocaeli (Turkey); Istanbul Aydin University, Department of Computer Engineering, 34295 Istanbul (Turkey); Sharda University, SET, Department of Mathematics, 32-34 Knowledge Park 3rd, Greater Noida (India)
2012-07-17
The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.
Kernel-based noise filtering of neutron detector signals
International Nuclear Information System (INIS)
Park, Moon Ghu; Shin, Ho Cheol; Lee, Eun Ki
2007-01-01
This paper describes recently developed techniques for effective filtering of neutron detector signal noise. In this paper, three kinds of noise filters are proposed and their performance is demonstrated for the estimation of reactivity. The tested filters are based on the unilateral kernel filter, unilateral kernel filter with adaptive bandwidth and bilateral filter to show their effectiveness in edge preservation. Filtering performance is compared with conventional low-pass and wavelet filters. The bilateral filter shows a remarkable improvement compared with unilateral kernel and wavelet filters. The effectiveness and simplicity of the unilateral kernel filter with adaptive bandwidth is also demonstrated by applying it to the reactivity measurement performed during reactor start-up physics tests
Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging.
Directory of Open Access Journals (Sweden)
Andrew M Huettner
Full Text Available A new method for designing radiofrequency (RF pulses with numerical optimization in the wavelet domain is presented. Numerical optimization may yield solutions that might otherwise have not been discovered with analytic techniques alone. Further, processing in the wavelet domain reduces the number of unknowns through compression properties inherent in wavelet transforms, providing a more tractable optimization problem. This algorithm is demonstrated with simultaneous multi-slice (SMS spin echo refocusing pulses because reduced peak RF power is necessary for SMS diffusion imaging with high acceleration factors. An iterative, nonlinear, constrained numerical minimization algorithm was developed to generate an optimized RF pulse waveform. Wavelet domain coefficients were modulated while iteratively running a Bloch equation simulator to generate the intermediate slice profile of the net magnetization. The algorithm minimizes the L2-norm of the slice profile with additional terms to penalize rejection band ripple and maximize the net transverse magnetization across each slice. Simulations and human brain imaging were used to demonstrate a new RF pulse design that yields an optimized slice profile and reduced peak energy deposition when applied to a multiband single-shot echo planar diffusion acquisition. This method may be used to optimize factors such as magnitude and phase spectral profiles and peak RF pulse power for multiband simultaneous multi-slice (SMS acquisitions. Wavelet-based RF pulse optimization provides a useful design method to achieve a pulse waveform with beneficial amplitude reduction while preserving appropriate magnetization response for magnetic resonance imaging.
Study of Denoising in TEOAE Signals Using an Appropriate Mother Wavelet Function
Directory of Open Access Journals (Sweden)
Habib Alizadeh Dizaji
2007-06-01
Full Text Available Background and Aim: Matching a mother wavelet to class of signals can be of interest in signal analysis and denoising based on wavelet multiresolution analysis and decomposition. As transient evoked otoacoustic emissions (TEOAES are contaminated with noise, the aim of this work was to provide a quantitative approach to the problem of matching a mother wavelet to TEOAE signals by using tuning curves and to use it for analysis and denoising TEOAE signals. Approximated mother wavelet for TEOAE signals was calculated using an algorithm for designing wavelet to match a specified signal.Materials and Methods: In this paper a tuning curve has used as a template for designing a mother wavelet that has maximum matching to the tuning curve. The mother wavelet matching was performed on tuning curves spectrum magnitude and phase independent of one another. The scaling function was calculated from the matched mother wavelet and by using these functions, lowpass and highpass filters were designed for a filter bank and otoacoustic emissions signal analysis and synthesis. After signal analyzing, denoising was performed by time windowing the signal time-frequency component.Results: Aanalysis indicated more signal reconstruction improvement in comparison with coiflets mother wavelet and by using the purposed denoising algorithm it is possible to enhance signal to noise ratio up to dB.Conclusion: The wavelet generated from this algorithm was remarkably similar to the biorthogonal wavelets. Therefore, by matching a biorthogonal wavelet to the tuning curve and using wavelet packet analysis, a high resolution time-frequency analysis for the otoacoustic emission signals is possible.
Goodman, Roe W
2016-01-01
This textbook for undergraduate mathematics, science, and engineering students introduces the theory and applications of discrete Fourier and wavelet transforms using elementary linear algebra, without assuming prior knowledge of signal processing or advanced analysis.It explains how to use the Fourier matrix to extract frequency information from a digital signal and how to use circulant matrices to emphasize selected frequency ranges. It introduces discrete wavelet transforms for digital signals through the lifting method and illustrates through examples and computer explorations how these transforms are used in signal and image processing. Then the general theory of discrete wavelet transforms is developed via the matrix algebra of two-channel filter banks. Finally, wavelet transforms for analog signals are constructed based on filter bank results already presented, and the mathematical framework of multiresolution analysis is examined.
Computerized detection of masses on mammograms by entropy maximization thresholding
International Nuclear Information System (INIS)
Kom, Guillaume; Tiedeu, Alain; Feudjio, Cyrille; Ngundam, J.
2010-03-01
In many cases, masses in X-ray mammograms are subtle and their detection can benefit from an automated system serving as a diagnostic aid. It is to this end that the authors propose in this paper, a new computer aided mass detection for breast cancer diagnosis. The first step focuses on wavelet filters enhancement which removes bright background due to dense breast tissues and some film artifacts while preserving features and patterns related to the masses. In the second step, enhanced image is computed by Entropy Maximization Thresholding (EMT) to obtain segmented masses. The efficiency of 98,181% is achieved by analyzing a database of 84 mammograms previously marked by radiologists and digitized at a pixel size of 343μmm x 343μ mm. The segmentation results, in terms of size of detected masses, give a relative error on mass area that is less than 8%. The performance of the proposed method has also been evaluated by means of the receiver operating-characteristics (ROC) analysis. This yielded respectively, an area (Az) of 0.9224 and 0.9295 under the ROC curve whether enhancement step is applied or not. Furthermore, we observe that the EMT yields excellent segmentation results compared to those found in literature. (author)
Medical image compression by using three-dimensional wavelet transformation
International Nuclear Information System (INIS)
Wang, J.; Huang, H.K.
1996-01-01
This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB. In general, the smaller the slice distance, the better the 3-D compression performance
Application of Cubic Box Spline Wavelets in the Analysis of Signal Singularities
Directory of Open Access Journals (Sweden)
Rakowski Waldemar
2015-12-01
Full Text Available In the subject literature, wavelets such as the Mexican hat (the second derivative of a Gaussian or the quadratic box spline are commonly used for the task of singularity detection. The disadvantage of the Mexican hat, however, is its unlimited support; the disadvantage of the quadratic box spline is a phase shift introduced by the wavelet, making it difficult to locate singular points. The paper deals with the construction and properties of wavelets in the form of cubic box splines which have compact and short support and which do not introduce a phase shift. The digital filters associated with cubic box wavelets that are applied in implementing the discrete dyadic wavelet transform are defined. The filters and the algorithme à trous of the discrete dyadic wavelet transform are used in detecting signal singularities and in calculating the measures of signal singularities in the form of a Lipschitz exponent. The article presents examples illustrating the use of cubic box spline wavelets in the analysis of signal singularities.
Noise reduction by wavelet thresholding
National Research Council Canada - National Science Library
Jansen, Maarten
2001-01-01
.... I rather present new material and own insights in the que stions involved with wavelet based noise reduction . On the other hand , the presented material does cover a whole range of methodologies, and in that sense, the book may serve as an introduction into the domain of wavelet smoothing. Throughout the text, three main properties show up ever again: spar...
A generalized wavelet extrema representation
Energy Technology Data Exchange (ETDEWEB)
Lu, Jian; Lades, M.
1995-10-01
The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.
Wavelet frames and their duals
DEFF Research Database (Denmark)
Lemvig, Jakob
2008-01-01
frames with good time localization and other attractive properties. Furthermore, the dual wavelet frames are constructed in such a way that we are guaranteed that both frames will have the same desirable features. The construction procedure works for any real, expansive dilation. A quasi-affine system....... The signals are then represented by linear combinations of the building blocks with coefficients found by an associated frame, called a dual frame. A wavelet frame is a frame where the building blocks are stretched (dilated) and translated versions of a single function; such a frame is said to have wavelet...... structure. The dilation of the wavelet building blocks in higher dimension is done via a square matrix which is usually taken to be integer valued. In this thesis we step away from the "usual" integer, expansive dilation and consider more general, expansive dilations. In most applications of wavelet frames...
Wavelet Enhanced Appearance Modelling
DEFF Research Database (Denmark)
Stegmann, Mikkel Bille; Forchhammer, Søren; Cootes, Timothy F.
2004-01-01
Generative segmentation methods such as the Active Appearance Models (AAM) establish dense correspondences by modelling variation of shape and pixel intensities. Alas, for 3D and high-resolution 2D images typical in medical imaging, this approach is rendered infeasible due to excessive storage......-7 wavelets on face images have shown that segmentation accuracy degrades gracefully with increasing compression ratio. Further, a proposed weighting scheme emphasizing edges was shown to be significantly more accurate at compression ratio 1:1, than a conventional AAM. At higher compression ratios the scheme...
Grating geophone signal processing based on wavelet transform
Li, Shuqing; Zhang, Huan; Tao, Zhifei
2008-12-01
Grating digital geophone is designed based on grating measurement technique benefiting averaging-error effect and wide dynamic range to improve weak signal detected precision. This paper introduced the principle of grating digital geophone and its post signal processing system. The signal acquisition circuit use Atmega 32 chip as core part and display the waveform on the Labwindows through the RS232 data link. Wavelet transform is adopted this paper to filter the grating digital geophone' output signal since the signal is unstable. This data processing method is compared with the FIR filter that widespread use in current domestic. The result indicates that the wavelet algorithm has more advantages and the SNR of seismic signal improve obviously.
Multifractal Cross Wavelet Analysis
Jiang, Zhi-Qiang; Gao, Xing-Lu; Zhou, Wei-Xing; Stanley, H. Eugene
Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using wavelet analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross wavelet analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.
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.
Wavelet transform approach for fitting financial time series data
Ahmed, Amel Abdoullah; Ismail, Mohd Tahir
2015-10-01
This study investigates a newly developed technique; a combined wavelet filtering and VEC model, to study the dynamic relationship among financial time series. Wavelet filter has been used to annihilate noise data in daily data set of NASDAQ stock market of US, and three stock markets of Middle East and North Africa (MENA) region, namely, Egypt, Jordan, and Istanbul. The data covered is from 6/29/2001 to 5/5/2009. After that, the returns of generated series by wavelet filter and original series are analyzed by cointegration test and VEC model. The results show that the cointegration test affirms the existence of cointegration between the studied series, and there is a long-term relationship between the US, stock markets and MENA stock markets. A comparison between the proposed model and traditional model demonstrates that, the proposed model (DWT with VEC model) outperforms traditional model (VEC model) to fit the financial stock markets series well, and shows real information about these relationships among the stock markets.
Image Compression using Haar and Modified Haar Wavelet Transform
Directory of Open Access Journals (Sweden)
Mohannad Abid Shehab Ahmed
2013-04-01
Full Text Available Efficient image compression approaches can provide the best solutions to the recent growth of the data intensive and multimedia based applications. As presented in many papers the Haar matrix–based methods and wavelet analysis can be used in various areas of image processing such as edge detection, preserving, smoothing or filtering. In this paper, color image compression analysis and synthesis based on Haar and modified Haar is presented. The standard Haar wavelet transformation with N=2 is composed of a sequence of low-pass and high-pass filters, known as a filter bank, the vertical and horizontal Haar filters are composed to construct four 2-dimensional filters, such filters applied directly to the image to speed up the implementation of the Haar wavelet transform. Modified Haar technique is studied and implemented for odd based numbers i.e. (N=3 & N=5 to generate many solution sets, these sets are tested using the energy function or numerical method to get the optimum one.The Haar transform is simple, efficient in memory usage due to high zero value spread (it can use sparse principle, and exactly reversible without the edge effects as compared to DCT (Discrete Cosine Transform. The implemented Matlab simulation results prove the effectiveness of DWT (Discrete Wave Transform algorithms based on Haar and Modified Haar techniques in attaining an efficient compression ratio (C.R, achieving higher peak signal to noise ratio (PSNR, and the resulting images are of much smoother as compared to standard JPEG especially for high C.R. A comparison between standard JPEG, Haar, and Modified Haar techniques is done finally which approves the highest capability of Modified Haar between others.
Dual-tree complex wavelet for medical image watermarking
International Nuclear Information System (INIS)
Mavudila, K.R.; Ndaye, B.M.; Masmoudi, L.; Hassanain, N.; Cherkaoui, M.
2010-01-01
In order to transmit medical data between hospitals, we insert the information for each patient in the image and its diagnosis, the watermarking consist to insert a message in the image and try to find it with the maximum possible fidelity. This paper presents a blind watermarking scheme in wavelet transform domain dual tree (DTT), who increasing the robustness and preserves the image quality. This system is transparent to the user and allows image integrity control. In addition, it provides information on the location of potential alterations and an evaluation of image modifications which is of major importance in a medico-legal framework. An example using head magnetic resonance and mammography imaging illustrates the overall method. Wavelet techniques can be successfully applied in various image processing methods, namely in image de noising, segmentation, classification, watermarking and others. In this paper we discussed the application of dual tree complex wavelet transform (D T-CWT), which has significant advantages over classic discrete wavelet transform (DWT), for certain image processing problems. The D T-CWT is a form of discreet wavelet transform which generates complex coefficients by using a dual tree of wavelet filters to obtain their real and imaginary parts. The main part of the paper is devoted to profit the exceptional quality for D T-CWT, compared to classical DWT, for a blind medical image watermarking, our schemes are using for the performance bivariate shrinkage with local variance estimation and are robust of attacks and favourably preserves the visual quality. Experimental results show that embedded watermarks using CWT give good image quality and are robust in comparison with the classical DWT.
Wavelet spectra of JACEE events
International Nuclear Information System (INIS)
Suzuki, Naomichi; Biyajima, Minoru; Ohsawa, Akinori.
1995-01-01
Pseudo-rapidity distributions of two high multiplicity events Ca-C and Si-AgBr observed by the JACEE are analyzed by a wavelet transform. Wavelet spectra of those events are calculated and compared with the simulation calculations. The wavelet spectrum of the Ca-C event somewhat resembles that simulated with the uniform random numbers. That of Si-AgBr event, however, is not reproduced by simulation calculations with Poisson random numbers, uniform random numbers, or a p-model. (author)
A Time-Frequency Auditory Model Using Wavelet Packets
DEFF Research Database (Denmark)
Agerkvist, Finn
1996-01-01
A time-frequency auditory model is presented. The model uses the wavelet packet analysis as the preprocessor. The auditory filters are modelled by the rounded exponential filters, and the excitation is smoothed by a window function. By comparing time-frequency excitation patterns it is shown...... that the change in the time-frequency excitation pattern introduced when a test tone at masked threshold is added to the masker is approximately equal to 7 dB for all types of maskers. The classic detection ratio therefore overrates the detection efficiency of the auditory system....
Wavelet Denoising of Mobile Radiation Data
International Nuclear Information System (INIS)
Campbell, D.B.
2008-01-01
The FY08 phase of this project investigated the merits of video fusion as a method for mitigating the false alarms encountered by vehicle borne detection systems in an effort to realize performance gains associated with wavelet denoising. The fusion strategy exploited the significant correlations which exist between data obtained from radiation detectors and video systems with coincident fields of view. The additional information provided by optical systems can greatly increase the capabilities of these detection systems by reducing the burden of false alarms and through the generation of actionable information. The investigation into the use of wavelet analysis techniques as a means of filtering the gross-counts signal obtained from moving radiation detectors showed promise for vehicle borne systems. However, the applicability of these techniques to man-portable systems is limited due to minimal gains in performance over the rapid feedback available to system operators under walking conditions. Furthermore, the fusion of video holds significant promise for systems operating from vehicles or systems organized into stationary arrays; however, the added complexity and hardware required by this technique renders it infeasible for man-portable systems
Iris Recognition Using Wavelet
Directory of Open Access Journals (Sweden)
Khaliq Masood
2013-08-01
Full Text Available Biometric systems are getting more attention in the present era. Iris recognition is one of the most secure and authentic among the other biometrics and this field demands more authentic, reliable and fast algorithms to implement these biometric systems in real time. In this paper, an efficient localization technique is presented to identify pupil and iris boundaries using histogram of the iris image. Two small portions of iris have been used for polar transformation to reduce computational time and to increase the efficiency of the system. Wavelet transform is used for feature vector generation. Rotation of iris is compensated without shifts in the iris code. System is tested on Multimedia University Iris Database and results show that proposed system has encouraging performance.
Directory of Open Access Journals (Sweden)
Hannu Olkkonen
2013-01-01
Full Text Available In this work we introduce a new family of splines termed as gamma splines for continuous signal approximation and multiresolution analysis. The gamma splines are born by -times convolution of the exponential by itself. We study the properties of the discrete gamma splines in signal interpolation and approximation. We prove that the gamma splines obey the two-scale equation based on the polyphase decomposition. to introduce the shift invariant gamma spline wavelet transform for tree structured subscale analysis of asymmetric signal waveforms and for systems with asymmetric impulse response. Especially we consider the applications in biomedical signal analysis (EEG, ECG, and EMG. Finally, we discuss the suitability of the gamma spline signal processing in embedded VLSI environment.
Wavelet theory and its applications
Energy Technology Data Exchange (ETDEWEB)
Faber, V.; Bradley, JJ.; Brislawn, C.; Dougherty, R.; Hawrylycz, M.
1996-07-01
This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). We investigated the theory of wavelet transforms and their relation to Laboratory applications. The investigators have had considerable success in the past applying wavelet techniques to the numerical solution of optimal control problems for distributed- parameter systems, nonlinear signal estimation, and compression of digital imagery and multidimensional data. Wavelet theory involves ideas from the fields of harmonic analysis, numerical linear algebra, digital signal processing, approximation theory, and numerical analysis, and the new computational tools arising from wavelet theory are proving to be ideal for many Laboratory applications. 10 refs.
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 new fractional wavelet transform
Dai, Hongzhe; Zheng, Zhibao; Wang, Wei
2017-03-01
The fractional Fourier transform (FRFT) is a potent tool to analyze the time-varying signal. However, it fails in locating the fractional Fourier domain (FRFD)-frequency contents which is required in some applications. A novel fractional wavelet transform (FRWT) is proposed to solve this problem. It displays the time and FRFD-frequency information jointly in the time-FRFD-frequency plane. The definition, basic properties, inverse transform and reproducing kernel of the proposed FRWT are considered. It has been shown that an FRWT with proper order corresponds to the classical wavelet transform (WT). The multiresolution analysis (MRA) associated with the developed FRWT, together with the construction of the orthogonal fractional wavelets are also presented. Three applications are discussed: the analysis of signal with time-varying frequency content, the FRFD spectrum estimation of signals that involving noise, and the construction of fractional Harr wavelet. Simulations verify the validity of the proposed FRWT.
Wavelet analysis for nonstationary signals
International Nuclear Information System (INIS)
Penha, Rosani Maria Libardi da
1999-01-01
Mechanical vibration signals play an important role in anomalies identification resulting of equipment malfunctioning. Traditionally, Fourier spectral analysis is used where the signals are assumed to be stationary. However, occasional transient impulses and start-up process are examples of nonstationary signals that can be found in mechanical vibrations. These signals can provide important information about the equipment condition, as early fault detection. The Fourier analysis can not adequately be applied to nonstationary signals because the results provide data about the frequency composition averaged over the duration of the signal. In this work, two methods for nonstationary signal analysis are used: Short Time Fourier Transform (STFT) and wavelet transform. The STFT is a method of adapting Fourier spectral analysis for nonstationary application to time-frequency domain. To have a unique resolution throughout the entire time-frequency domain is its main limitation. The wavelet transform is a new analysis technique suitable to nonstationary signals, which handles the STFT drawbacks, providing multi-resolution frequency analysis and time localization in a unique time-scale graphic. The multiple frequency resolutions are obtained by scaling (dilatation/compression) the wavelet function. A comparison of the conventional Fourier transform, STFT and wavelet transform is made applying these techniques to: simulated signals, arrangement rotor rig vibration signal and rotate machine vibration signal Hanning window was used to STFT analysis. Daubechies and harmonic wavelets were used to continuos, discrete and multi-resolution wavelet analysis. The results show the Fourier analysis was not able to detect changes in the signal frequencies or discontinuities. The STFT analysis detected the changes in the signal frequencies, but with time-frequency resolution problems. The wavelet continuos and discrete transform demonstrated to be a high efficient tool to detect
Wavelet Based Hilbert Transform with Digital Design and Application to QCM-SS Watermarking
Directory of Open Access Journals (Sweden)
S. P. Maity
2008-04-01
Full Text Available In recent time, wavelet transforms are used extensively for efficient storage, transmission and representation of multimedia signals. Hilbert transform pairs of wavelets is the basic unit of many wavelet theories such as complex filter banks, complex wavelet and phaselet etc. Moreover, Hilbert transform finds various applications in communications and signal processing such as generation of single sideband (SSB modulation, quadrature carrier multiplexing (QCM and bandpass representation of a signal. Thus wavelet based discrete Hilbert transform design draws much attention of researchers for couple of years. This paper proposes an (i algorithm for generation of low computation cost Hilbert transform pairs of symmetric filter coefficients using biorthogonal wavelets, (ii approximation to its rational coefficients form for its efficient hardware realization and without much loss in signal representation, and finally (iii development of QCM-SS (spread spectrum image watermarking scheme for doubling the payload capacity. Simulation results show novelty of the proposed Hilbert transform design and its application to watermarking compared to existing algorithms.
Night Vision Image De-Noising of Apple Harvesting Robots Based on the Wavelet Fuzzy Threshold
Directory of Open Access Journals (Sweden)
Chengzhi Ruan
2015-12-01
Full Text Available In this paper, the de-noising problem of night vision images is studied for apple harvesting robots working at night. The wavelet threshold method is applied to the de-noising of night vision images. Due to the fact that the choice of wavelet threshold function restricts the effect of the wavelet threshold method, the fuzzy theory is introduced to construct the fuzzy threshold function. We then propose the de-noising algorithm based on the wavelet fuzzy threshold. This new method can reduce image noise interferences, which is conducive to further image segmentation and recognition. To demonstrate the performance of the proposed method, we conducted simulation experiments and compared the median filtering and the wavelet soft threshold de-noising methods. It is shown that this new method can achieve the highest relative PSNR. Compared with the original images, the median filtering de-noising method and the classical wavelet threshold de-noising method, the relative PSNR increases 24.86%, 13.95%, and 11.38% respectively. We carry out comparisons from various aspects, such as intuitive visual evaluation, objective data evaluation, edge evaluation and artificial light evaluation. The experimental results show that the proposed method has unique advantages for the de-noising of night vision images, which lay the foundation for apple harvesting robots working at night.
Joint Markov Blankets in Feature Sets Extracted from Wavelet Packet Decompositions
Directory of Open Access Journals (Sweden)
Gert Van Dijck
2011-07-01
Full Text Available Since two decades, wavelet packet decompositions have been shown effective as a generic approach to feature extraction from time series and images for the prediction of a target variable. Redundancies exist between the wavelet coefficients and between the energy features that are derived from the wavelet coefficients. We assess these redundancies in wavelet packet decompositions by means of the Markov blanket filtering theory. We introduce the concept of joint Markov blankets. It is shown that joint Markov blankets are a natural extension of Markov blankets, which are defined for single features, to a set of features. We show that these joint Markov blankets exist in feature sets consisting of the wavelet coefficients. Furthermore, we prove that wavelet energy features from the highest frequency resolution level form a joint Markov blanket for all other wavelet energy features. The joint Markov blanket theory indicates that one can expect an increase of classification accuracy with the increase of the frequency resolution level of the energy features.
A New Wavelet Threshold Function and Denoising Application
Directory of Open Access Journals (Sweden)
Lu Jing-yi
2016-01-01
Full Text Available In order to improve the effects of denoising, this paper introduces the basic principles of wavelet threshold denoising and traditional structures threshold functions. Meanwhile, it proposes wavelet threshold function and fixed threshold formula which are both improved here. First, this paper studies the problems existing in the traditional wavelet threshold functions and introduces the adjustment factors to construct the new threshold function basis on soft threshold function. Then, it studies the fixed threshold and introduces the logarithmic function of layer number of wavelet decomposition to design the new fixed threshold formula. Finally, this paper uses hard threshold, soft threshold, Garrote threshold, and improved threshold function to denoise different signals. And the paper also calculates signal-to-noise (SNR and mean square errors (MSE of the hard threshold functions, soft thresholding functions, Garrote threshold functions, and the improved threshold function after denoising. Theoretical analysis and experimental results showed that the proposed approach could improve soft threshold functions with constant deviation and hard threshold with discontinuous function problems. The proposed approach could improve the different decomposition scales that adopt the same threshold value to deal with the noise problems, also effectively filter the noise in the signals, and improve the SNR and reduce the MSE of output signals.
Kalman Filtering with Real-Time Applications
Chui, Charles K
2009-01-01
Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
Spline and spline wavelet methods with applications to signal and image processing
Averbuch, Amir Z; Zheludev, Valery A
This volume provides universal methodologies accompanied by Matlab software to manipulate numerous signal and image processing applications. It is done with discrete and polynomial periodic splines. Various contributions of splines to signal and image processing from a unified perspective are presented. This presentation is based on Zak transform and on Spline Harmonic Analysis (SHA) methodology. SHA combines approximation capabilities of splines with the computational efficiency of the Fast Fourier transform. SHA reduces the design of different spline types such as splines, spline wavelets (SW), wavelet frames (SWF) and wavelet packets (SWP) and their manipulations by simple operations. Digital filters, produced by wavelets design process, give birth to subdivision schemes. Subdivision schemes enable to perform fast explicit computation of splines' values at dyadic and triadic rational points. This is used for signals and images upsampling. In addition to the design of a diverse library of splines, SW, SWP a...
Profit maximization mitigates competition
DEFF Research Database (Denmark)
Dierker, Egbert; Grodal, Birgit
1996-01-01
We consider oligopolistic markets in which the notion of shareholders' utility is well-defined and compare the Bertrand-Nash equilibria in case of utility maximization with those under the usual profit maximization hypothesis. Our main result states that profit maximization leads to less price...... competition than utility maximization. Since profit maximization tends to raise prices, it may be regarded as beneficial for the owners as a whole. Moreover, if profit maximization is a good proxy for utility maximization, then there is no need for a general equilibrium analysis that takes the distribution...... of profits among consumers fully into account and partial equilibrium analysis suffices...
Signal Analysis by New Mother Wavelets
International Nuclear Information System (INIS)
Niu Jinbo; Qi Kaiguo; Fan Hongyi
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. (general)
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
Smart-phone based electrocardiogram wavelet decomposition and neural network classification
International Nuclear Information System (INIS)
Jannah, N; Hadjiloucas, S; Hwang, F; Galvão, R K H
2013-01-01
This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.
Wavelets: Applications to Image Compression-II
Indian Academy of Sciences (India)
Wavelets: Applications to Image Compression-II. Sachin P ... successful application of wavelets in image com- ... b) Soft threshold: In this case, all the coefficients x ..... [8] http://www.jpeg.org} Official site of the Joint Photographic Experts Group.
Wavelet Transforms using VTK-m
Energy Technology Data Exchange (ETDEWEB)
Li, Shaomeng [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sewell, Christopher Meyer [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-09-27
These are a set of slides that deal with the topics of wavelet transforms using VTK-m. First, wavelets are discussed and detailed, then VTK-m is discussed and detailed, then wavelets and VTK-m are looked at from a performance comparison, then from an accuracy comparison, and finally lessons learned, conclusion, and what is next. Lessons learned are the following: Launching worklets is expensive; Natural logic of performing 2D wavelet transform: Repeat the same 1D wavelet transform on every row, repeat the same 1D wavelet transform on every column, invoke the 1D wavelet worklet every time: num_rows x num_columns; VTK-m approach of performing 2D wavelet transform: Create a worklet for 2D that handles both rows and columns, invoke this new worklet only one time; Fast calculation, but cannot reuse 1D implementations.
From Calculus to Wavelets: ANew Mathematical Technique
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 2; Issue 4. From Calculus to Wavelets: A New Mathematical Technique Wavelet Analysis Physical Properties. Gerald B Folland. General Article Volume 2 Issue 4 April 1997 pp 25-37 ...
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...
Analysis of transient signals by Wavelet transform
International Nuclear Information System (INIS)
Penha, Rosani Libardi da; Silva, Aucyone A. da; Ting, Daniel K.S.; Oliveira Neto, Jose Messias de
2000-01-01
The objective of this work is to apply the Wavelet Transform in transient signals. The Wavelet technique can outline the short time events that are not easily detected using traditional techniques. In this work, the Wavelet Transform is compared with Fourier Transform, by using simulated data and rotor rig data. This data contain known transients. The wavelet could follow all the transients, what do not happen to the Fourier techniques. (author)
Kalman filtering with real-time applications
Chui, Charles K
2017-01-01
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...
International Nuclear Information System (INIS)
Chaibi, S.; Lajnef, T.; Samet, M.; Kachouri, A.
2011-01-01
Many natural signals EEG are comprised frequency overlapping of oscillatory and transient components. In our study the intracranial EEG signals of epilepsy are composed of the superposition of oscillatory signals (HFOs: High Frequency oscillations) and a transient signals (spikes and sharp waves, etc.). The oscillatory components (HFOs) exist in the frequency band 80-500Hz. The transient components comes from nonrhythmic brain activities (spikes, sharp waves and vertex waves of varying amplitude, shape and duration) and cover a continuous wide bandwidth from low to high frequencies and resemble an HFOs events when filtered using a band pass classical filter. The classical filtering methods based on FIR filters, Wavelet transforms and the Matching Pursuit cannot separate the oscillatory from transient activities. This paper describes an approach for decomposing an iEEG signals of epilepsy into the sum of oscillatory components and a transient components based on overcomplete rational dilation wavelet transforms (overcomplete RADWT) in conjunction with morphological component analysis (MCA).
Energy Technology Data Exchange (ETDEWEB)
Zeng, X; Yamazaki, K [Tokyo Gakugei University, Tokyo (Japan); Oguchi, Y [Hosei University, Tokyo (Japan)
1997-10-22
A study has been performed on wavelet analysis of seismic waves. In the wavelet analysis of seismic waves, there is a possibility that the results according to different wavelet functions may come out with great difference. The study has carried out the following analyses: an analysis of amplitude and phase using wavelet transform which uses wavelet function of Morlet on P- and S-waves generated by natural earthquakes and P-wave generated by an artificial earthquake, and an analysis using continuous wavelet transform, which uses a constitution of complex wavelet function constructed by a completely diagonal scaling function of Daubechies and the wavelet function. As a result, the following matters were made clear: the result of detection of abnormal components or discontinuity depends on the wavelet function; if the Morlet wavelet function is used to properly select angular frequency and scale, equiphase lines in a phase scalogram concentrate on the discontinuity; and the result of applying the complex wavelet function is superior to that of applying the wavelet function of Morlet. 2 refs., 5 figs.
WAVELET TRANSFORM AND LIP MODEL
Directory of Open Access Journals (Sweden)
Guy Courbebaisse
2011-05-01
Full Text Available The Fourier transform is well suited to the study of stationary functions. Yet, it is superseded by the Wavelet transform for the powerful characterizations of function features such as singularities. On the other hand, the LIP (Logarithmic Image Processing model is a mathematical framework developed by Jourlin and Pinoli, dedicated to the representation and processing of gray tones images called hereafter logarithmic images. This mathematically well defined model, comprising a Fourier Transform "of its own", provides an effective tool for the representation of images obtained by transmitted light, such as microscope images. This paper presents a Wavelet transform within the LIP framework, with preservation of the classical Wavelet Transform properties. We show that the fast computation algorithm due to Mallat can be easily used. An application is given for the detection of crests.
A New Method for Multisensor Data Fusion Based on Wavelet Transform in a Chemical Plant
Directory of Open Access Journals (Sweden)
Karim Salahshoor
2014-07-01
Full Text Available This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT and extended Kalman filter (EKF. Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR and minimum square error (MSE criteria.
Fusion of multispectral and panchromatic images using multirate filter banks
Institute of Scientific and Technical Information of China (English)
Wang Hong; Jing Zhongliang; Li Jianxun
2005-01-01
In this paper, an image fusion method based on the filter banks is proposed for merging a high-resolution panchromatic image and a low-resolution multispectral image. Firstly, the filter banks are designed to merge different signals with minimum distortion by using cosine modulation. Then, the filter banks-based image fusion is adopted to obtain a high-resolution multispectral image that combines the spectral characteristic of low-resolution data with the spatial resolution of the panchromatic image. Finally, two different experiments and corresponding performance analysis are presented. Experimental results indicate that the proposed approach outperforms the HIS transform, discrete wavelet transform and discrete wavelet frame.
Maximally incompatible quantum observables
Energy Technology Data Exchange (ETDEWEB)
Heinosaari, Teiko, E-mail: teiko.heinosaari@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Schultz, Jussi, E-mail: jussi.schultz@gmail.com [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Toigo, Alessandro, E-mail: alessandro.toigo@polimi.it [Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Milano, Via Celoria 16, I-20133 Milano (Italy); Ziman, Mario, E-mail: ziman@savba.sk [RCQI, Institute of Physics, Slovak Academy of Sciences, Dúbravská cesta 9, 84511 Bratislava (Slovakia); Faculty of Informatics, Masaryk University, Botanická 68a, 60200 Brno (Czech Republic)
2014-05-01
The existence of maximally incompatible quantum observables in the sense of a minimal joint measurability region is investigated. Employing the universal quantum cloning device it is argued that only infinite dimensional quantum systems can accommodate maximal incompatibility. It is then shown that two of the most common pairs of complementary observables (position and momentum; number and phase) are maximally incompatible.
Maximally incompatible quantum observables
International Nuclear Information System (INIS)
Heinosaari, Teiko; Schultz, Jussi; Toigo, Alessandro; Ziman, Mario
2014-01-01
The existence of maximally incompatible quantum observables in the sense of a minimal joint measurability region is investigated. Employing the universal quantum cloning device it is argued that only infinite dimensional quantum systems can accommodate maximal incompatibility. It is then shown that two of the most common pairs of complementary observables (position and momentum; number and phase) are maximally incompatible.
On tempo tracking: Tempogram representation and Kalman filtering
Cemgil, A.T.; Kappen, H.J.; Desain, P.W.M.; Honing, H.J.
2001-01-01
We formulate tempo tracking in a Bayesian framework where a tempo tracker is modeled as a stochastic dynamical system. The tempo is modeled as a hidden state variable of the system and is estimated by a Kalman filter. The Kalman filter operates on a Tempogram, a wavelet-like multiscale expansion of
Fundamental papers in wavelet theory
Walnut, David F
2006-01-01
This book traces the prehistory and initial development of wavelet theory, a discipline that has had a profound impact on mathematics, physics, and engineering. Interchanges between these fields during the last fifteen years have led to a number of advances in applications such as image compression, turbulence, machine vision, radar, and earthquake prediction. This book contains the seminal papers that presented the ideas from which wavelet theory evolved, as well as those major papers that developed the theory into its current form. These papers originated in a variety of journals from differ
A CMOS Morlet Wavelet Generator
Directory of Open Access Journals (Sweden)
A. I. Bautista-Castillo
2017-04-01
Full Text Available The design and characterization of a CMOS circuit for Morlet wavelet generation is introduced. With the proposed Morlet wavelet circuit, it is possible to reach a~low power consumption, improve standard deviation (σ control and also have a small form factor. A prototype in a double poly, three metal layers, 0.5 µm CMOS process from MOSIS foundry was carried out in order to verify the functionality of the proposal. However, the design methodology can be extended to different CMOS processes. According to the performance exhibited by the circuit, may be useful in many different signal processing tasks such as nonlinear time-variant systems.
Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography
Laine, Andrew F.; Koren, Iztok; Yang, Wuhai; Taylor, Fred J.
1995-04-01
This paper describes two approaches for accomplishing interactive feature analysis by overcomplete multiresolution representations. We show quantitatively that transform coefficients, modified by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a filter bank representing a steerable dyadic wavelet transform that can be used for multiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicion) areas (lesions). Features extracted from multiscale representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology can improve changes of early detection while requiring less time to evaluate mammograms for most patients.
Wavelet series approximation using wavelet function with compactly ...
African Journals Online (AJOL)
The Wavelets generated by Scaling Function with Compactly Support are useful in various applications especially for reconstruction of functions. Generally, the computational process will be faster if Scaling Function support descends, so computational errors are summarized from one level to another level. In this article, the ...
Wavelets a tutorial in theory and applications
1992-01-01
Wavelets: A Tutorial in Theory and Applications is the second volume in the new series WAVELET ANALYSIS AND ITS APPLICATIONS. As a companion to the first volume in this series, this volume covers several of the most important areas in wavelets, ranging from the development of the basic theory such as construction and analysis of wavelet bases to an introduction of some of the key applications, including Mallat's local wavelet maxima technique in second generation image coding. A fairly extensive bibliography is also included in this volume.Key Features* Covers several of the
Wavelet entropy characterization of elevated intracranial pressure.
Xu, Peng; Scalzo, Fabien; Bergsneider, Marvin; Vespa, Paul; Chad, Miller; Hu, Xiao
2008-01-01
Intracranial Hypertension (ICH) often occurs for those patients with traumatic brain injury (TBI), stroke, tumor, etc. Pathology of ICH is still controversial. In this work, we used wavelet entropy and relative wavelet entropy to study the difference existed between normal and hypertension states of ICP for the first time. The wavelet entropy revealed the similar findings as the approximation entropy that entropy during ICH state is smaller than that in normal state. Moreover, with wavelet entropy, we can see that ICH state has the more focused energy in the low wavelet frequency band (0-3.1 Hz) than the normal state. The relative wavelet entropy shows that the energy distribution in the wavelet bands between these two states is actually different. Based on these results, we suggest that ICH may be formed by the re-allocation of oscillation energy within brain.
32Still Image Compression Algorithm Based on Directional Filter Banks
Chunling Yang; Duanwu Cao; Li Ma
2010-01-01
Hybrid wavelet and directional filter banks (HWD) is an effective multi-scale geometrical analysis method. Compared to wavelet transform, it can better capture the directional information of images. But the ringing artifact, which is caused by the coefficient quantization in transform domain, is the biggest drawback of image compression algorithms in HWD domain. In this paper, by researching on the relationship between directional decomposition and ringing artifact, an improved decomposition ...
Andrew M. Parker; Wandi Bruine de Bruin; Baruch Fischhoff
2007-01-01
Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007). Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002), we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions...
Maximal combustion temperature estimation
International Nuclear Information System (INIS)
Golodova, E; Shchepakina, E
2006-01-01
This work is concerned with the phenomenon of delayed loss of stability and the estimation of the maximal temperature of safe combustion. Using the qualitative theory of singular perturbations and canard techniques we determine the maximal temperature on the trajectories located in the transition region between the slow combustion regime and the explosive one. This approach is used to estimate the maximal temperature of safe combustion in multi-phase combustion models
ON THE FOURIER AND WAVELET ANALYSIS OF CORONAL TIME SERIES
International Nuclear Information System (INIS)
Auchère, F.; Froment, C.; 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 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 and 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 and 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.
ON THE FOURIER AND WAVELET ANALYSIS OF CORONAL TIME SERIES
Energy Technology Data Exchange (ETDEWEB)
Auchère, F.; Froment, C.; Bocchialini, K.; Buchlin, E.; Solomon, J., E-mail: frederic.auchere@ias.u-psud.fr [Institut d’Astrophysique Spatiale, CNRS, Univ. Paris-Sud, Université Paris-Saclay, Bât. 121, F-91405 Orsay (France)
2016-07-10
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 and 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 and 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.
Implementation of the 2-D Wavelet Transform into FPGA for Image
León, M.; Barba, L.; Vargas, L.; Torres, C. O.
2011-01-01
This paper presents a hardware system implementation of the of discrete wavelet transform algoritm in two dimensions for FPGA, using the Daubechies filter family of order 2 (db2). The decomposition algorithm of this transform is designed and simulated with the Hardware Description Language VHDL and is implemented in a programmable logic device (FPGA) XC3S1200E reference, Spartan IIIE family, by Xilinx, take advantage the parallels properties of these gives us and speeds processing that can reach them. The architecture is evaluated using images input of different sizes. This implementation is done with the aim of developing a future images encryption hardware system using wavelet transform for security information.
Implementation of the 2-D Wavelet Transform into FPGA for Image
Energy Technology Data Exchange (ETDEWEB)
Leon, M; Barba, L; Vargas, L; Torres, C O, E-mail: madeleineleon@unicesar.edu.co [Laboratorio de Optica e Informatica, Universidad Popular del Cesar, Sede balneario Hurtado, Valledupar, Cesar (Colombia)
2011-01-01
This paper presents a hardware system implementation of the of discrete wavelet transform algorithm in two dimensions for FPGA, using the Daubechies filter family of order 2 (db2). The decomposition algorithm of this transform is designed and simulated with the Hardware Description Language VHDL and is implemented in a programmable logic device (FPGA) XC3S1200E reference, Spartan IIIE family, by Xilinx, take advantage the parallels properties of these gives us and speeds processing that can reach them. The architecture is evaluated using images input of different sizes. This implementation is done with the aim of developing a future images encryption hardware system using wavelet transform for security information.
Equiripple Digital Filter in Quadrature Mirror Filter Banks for Nuclear Magnetic Tomography
Czech Academy of Sciences Publication Activity Database
Gescheidtová, E.; Kubásek, R.; Smékal, Z.; Bartušek, Karel
2007-01-01
Roč. 37, č. 1 (2007), s. 141-149 ISSN 1738-6438 R&D Projects: GA ČR(CZ) GA102/07/0389; GA ČR(CZ) GA102/07/1086 Institutional research plan: CEZ:AV0Z20650511 Keywords : wavelet transform * digital filter * MR tomography Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering
Maximally multipartite entangled states
Facchi, Paolo; Florio, Giuseppe; Parisi, Giorgio; Pascazio, Saverio
2008-06-01
We introduce the notion of maximally multipartite entangled states of n qubits as a generalization of the bipartite case. These pure states have a bipartite entanglement that does not depend on the bipartition and is maximal for all possible bipartitions. They are solutions of a minimization problem. Examples for small n are investigated, both analytically and numerically.
Directory of Open Access Journals (Sweden)
Andrew M. Parker
2007-12-01
Full Text Available Our previous research suggests that people reporting a stronger desire to maximize obtain worse life outcomes (Bruine de Bruin et al., 2007. Here, we examine whether this finding may be explained by the decision-making styles of self-reported maximizers. Expanding on Schwartz et al. (2002, we find that self-reported maximizers are more likely to show problematic decision-making styles, as evidenced by self-reports of less behavioral coping, greater dependence on others when making decisions, more avoidance of decision making, and greater tendency to experience regret. Contrary to predictions, self-reported maximizers were more likely to report spontaneous decision making. However, the relationship between self-reported maximizing and worse life outcomes is largely unaffected by controls for measures of other decision-making styles, decision-making competence, and demographic variables.
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.
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.
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.
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.
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.
Huang, Yan; Wang, Zhihui
2015-12-01
With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.
Wavelet Analysis for Molecular Dynamics
2015-06-01
Our method takes as input the topology and sparsity of the bonding structure of a molecular system, and returns a hierarchical set of system-specific...problems, such as modeling crack initiation and propagation, or interfacial phenomena. In the present work, we introduce a wavelet-based approach to extend...Several functional forms are common for angle poten- tials complicating not only implementation but also choice of approximation. In all cases, the
Wavelet analysis in two-dimensional tomography
Burkovets, Dimitry N.
2002-02-01
The diagnostic possibilities of wavelet-analysis of coherent images of connective tissue in its pathological changes diagnostics. The effectiveness of polarization selection in obtaining wavelet-coefficients' images is also shown. The wavelet structures, characterizing the process of skin psoriasis, bone-tissue osteoporosis have been analyzed. The histological sections of physiological normal and pathologically changed samples of connective tissue of human skin and spongy bone tissue have been analyzed.
Wavelet Radiosity on Arbitrary Planar Surfaces
Holzschuch , Nicolas; Cuny , François; Alonso , Laurent
2000-01-01
Colloque avec actes et comité de lecture. internationale.; International audience; Wavelet radiosity is, by its nature, restricted to parallelograms or triangles. This paper presents an innovative technique enabling wavelet radiosity computations on planar surfaces of arbitrary shape, including concave contours or contours with holes. This technique replaces the need for triangulating such complicated shapes, greatly reducing the complexity of the wavelet radiosity algorithm and the computati...
Directory of Open Access Journals (Sweden)
M. Ragosta
2004-06-01
Full Text Available Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti-transforming the retained coefficients. Such methodological approach might be applied to characterise unwanted environmental noise. It also can be considered as a practical technique to remove noise that can hamper the correct assessment and use of electrical techniques for the monitoring of geophysical phenomena.
Wavelet approach to accelerator problems. 3: Melnikov functions and symplectic topology
International Nuclear Information System (INIS)
Fedorova, A.; Zeitlin, M.; Parsa, Z.
1997-05-01
This is the third part of a series of talks in which the authors present applications of methods of wavelet analysis to polynomial approximations for a number of accelerator physics problems. They consider the generalization of the variational wavelet approach to nonlinear polynomial problems to the case of Hamiltonian systems for which they need to preserve underlying symplectic or Poissonian or quasicomplex structures in any type of calculations. They use the approach for the problem of explicit calculations of Arnold-Weinstein curves via Floer variational approach from symplectic topology. The loop solutions are parameterized by the solutions of reduced algebraical problem--matrix Quadratic Mirror Filters equations. Also they consider wavelet approach to the calculations of Melnikov functions in the theory of homoclinic chaos in perturbed Hamiltonian systems
Global spectral graph wavelet signature for surface analysis of carpal bones
Masoumi, Majid; Rezaei, Mahsa; Ben Hamza, A.
2018-02-01
Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. We employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute, and combines the advantages of both low-pass and band-pass filters. We perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature embedding approach for comparing shapes of the carpal bones across populations.
Detection of short-term anomaly using parasitic discrete wavelet transform
International Nuclear Information System (INIS)
Nagamatsu, Takashi; Gofuku, Akio
2013-01-01
A parasitic discrete wavelet transform (P-DWT) that has a large flexibility in design of the mother wavelet (MW) and a high processing speed was applied for simulation and measured anomalies. First, we applied the P-DWT to detection of the short-term anomalies. Second, we applied the P-DWT to detection of the collision of pump using the pump diagnostic experiment equipment that was designed taking into consideration the structure of the pump used for the water-steam system of the fast breeder reactor 'Monju'. The vibration signals were measured by the vibration sensor attached to the pump when injecting four types of small objects (sphere, small sphere, cube, and rectangular parallelepiped). Anomaly detection was performed by calculating the fast wavelet instantaneous correlation using the parasitic filter that was constructed on the basis of the measured signals. The results suggested that the anomalies could be detected for all types of the supposed anomalies. (author)
Bouganssa, Issam; Sbihi, Mohamed; Zaim, Mounia
2017-07-01
The 2D Discrete Wavelet Transform (DWT) is a computationally intensive task that is usually implemented on specific architectures in many imaging systems in real time. In this paper, a high throughput edge or contour detection algorithm is proposed based on the discrete wavelet transform. A technique for applying the filters on the three directions (Horizontal, Vertical and Diagonal) of the image is used to present the maximum of the existing contours. The proposed architectures were designed in VHDL and mapped to a Xilinx Sparten6 FPGA. The results of the synthesis show that the proposed architecture has a low area cost and can operate up to 100 MHz, which can perform 2D wavelet analysis for a sequence of images while maintaining the flexibility of the system to support an adaptive algorithm.
Directory of Open Access Journals (Sweden)
Jean Pierre Astruc
2007-01-01
Full Text Available This paper investigates the mathematical framework of multiresolution analysis based on irregularly spaced knots sequence. Our presentation is based on the construction of nested nonuniform spline multiresolution spaces. From these spaces, we present the construction of orthonormal scaling and wavelet basis functions on bounded intervals. For any arbitrary degree of the spline function, we provide an explicit generalization allowing the construction of the scaling and wavelet bases on the nontraditional sequences. We show that the orthogonal decomposition is implemented using filter banks where the coefficients depend on the location of the knots on the sequence. Examples of orthonormal spline scaling and wavelet bases are provided. This approach can be used to interpolate irregularly sampled signals in an efficient way, by keeping the multiresolution approach.
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.
Wavelets for Sparse Representation of Music
DEFF Research Database (Denmark)
Endelt, Line Ørtoft; Harbo, Anders La-Cour
2004-01-01
We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...
Wavelet-based prediction of oil prices
International Nuclear Information System (INIS)
Yousefi, Shahriar; Weinreich, Ilona; Reinarz, Dominik
2005-01-01
This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced
Composite Wavelet Filters for Enhanced Automated Target Recognition
Chiang, Jeffrey N.; Zhang, Yuhan; Lu, Thomas T.; Chao, Tien-Hsin
2012-01-01
Automated Target Recognition (ATR) systems aim to automate target detection, recognition, and tracking. The current project applies a JPL ATR system to low-resolution sonar and camera videos taken from unmanned vehicles. These sonar images are inherently noisy and difficult to interpret, and pictures taken underwater are unreliable due to murkiness and inconsistent lighting. The ATR system breaks target recognition into three stages: 1) Videos of both sonar and camera footage are broken into frames and preprocessed to enhance images and detect Regions of Interest (ROIs). 2) Features are extracted from these ROIs in preparation for classification. 3) ROIs are classified as true or false positives using a standard Neural Network based on the extracted features. Several preprocessing, feature extraction, and training methods are tested and discussed in this paper.
International Nuclear Information System (INIS)
Gronau, M.
1984-01-01
Two ambiguities are noted in the definition of the concept of maximal CP violation. The phase convention ambiguity is overcome by introducing a CP violating phase in the quark mixing matrix U which is invariant under rephasing transformations. The second ambiguity, related to the parametrization of U, is resolved by finding a single empirically viable definition of maximal CP violation when assuming that U does not single out one generation. Considerable improvement in the calculation of nonleptonic weak amplitudes is required to test the conjecture of maximal CP violation. 21 references
Optical Aperture Synthesis Object's Information Extracting Based on Wavelet Denoising
International Nuclear Information System (INIS)
Fan, W J; Lu, Y
2006-01-01
Wavelet denoising is studied to improve OAS(optical aperture synthesis) object's Fourier information extracting. Translation invariance wavelet denoising based on Donoho wavelet soft threshold denoising is researched to remove Pseudo-Gibbs in wavelet soft threshold image. OAS object's information extracting based on translation invariance wavelet denoising is studied. The study shows that wavelet threshold denoising can improve the precision and the repetition of object's information extracting from interferogram, and the translation invariance wavelet denoising information extracting is better than soft threshold wavelet denoising information extracting
Complex Wavelet transform for MRI
International Nuclear Information System (INIS)
Junor, P.; Janney, P.
2004-01-01
Full text: There is a perpetual compromise encountered in magnetic resonance (MRl) image reconstruction, between the traditional elements of image quality (noise, spatial resolution and contrast). Additional factors exacerbating this trade-off include various artifacts, computational (and hence time-dependent) overhead, and financial expense. This paper outlines a new approach to the problem of minimizing MRI image acquisition and reconstruction time without compromising resolution and noise reduction. The standard approaches for reconstructing magnetic resonance (MRI) images from raw data (which rely on relatively conventional signal processing) have matured but there are a number of challenges which limit their use. A major one is the 'intrinsic' signal-to-noise ratio (SNR) of the reconstructed image that depends on the strength of the main field. A typical clinical MRI almost invariably uses a super-cooled magnet in order to achieve a high field strength. The ongoing running cost of these super-cooled magnets prompts consideration of alternative magnet systems for use in MRIs for developing countries and in some remote regional installations. The decrease in image quality from using lower field strength magnets can be addressed by improvements in signal processing strategies. Conversely, improved signal processing will obviously benefit the current conventional field strength MRI machines. Moreover, the 'waiting time' experienced in many MR sequences (due to the relaxation time delays) can be exploited by more rigorous processing of the MR signals. Acquisition often needs to be repeated so that coherent averaging may partially redress the shortfall in SNR, at the expense of further delay. Wavelet transforms have been used in MRI as an alternative for encoding and denoising for over a decade. These have not supplanted the traditional Fourier transform methods that have long been the mainstay of MRI reconstruction, but have some inflexibility. The dual
An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.
Kim, Jinkwon; Min, Se Dong; Lee, Myoungho
2011-06-27
Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.
An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects
Directory of Open Access Journals (Sweden)
Min Se Dong
2011-06-01
Full Text Available Abstract Background Numerous studies have been conducted regarding a heartbeat classification algorithm over the past several decades. However, many algorithms have also been studied to acquire robust performance, as biosignals have a large amount of variation among individuals. Various methods have been proposed to reduce the differences coming from personal characteristics, but these expand the differences caused by arrhythmia. Methods In this paper, an arrhythmia classification algorithm using a dedicated wavelet adapted to individual subjects is proposed. We reduced the performance variation using dedicated wavelets, as in the ECG morphologies of the subjects. The proposed algorithm utilizes morphological filtering and a continuous wavelet transform with a dedicated wavelet. A principal component analysis and linear discriminant analysis were utilized to compress the morphological data transformed by the dedicated wavelets. An extreme learning machine was used as a classifier in the proposed algorithm. Results A performance evaluation was conducted with the MIT-BIH arrhythmia database. The results showed a high sensitivity of 97.51%, specificity of 85.07%, accuracy of 97.94%, and a positive predictive value of 97.26%. Conclusions The proposed algorithm achieves better accuracy than other state-of-the-art algorithms with no intrasubject between the training and evaluation datasets. And it significantly reduces the amount of intervention needed by physicians.
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Application of wavelets in speech processing
Farouk, Mohamed Hesham
2014-01-01
This book provides a survey on wide-spread of employing wavelets analysis in different applications of speech processing. The author examines development and research in different application of speech processing. The book also summarizes the state of the art research on wavelet in speech processing.
Construction of wavelets with composite dilations
International Nuclear Information System (INIS)
Wu Guochang; Li Zhiqiang; Cheng Zhengxing
2009-01-01
In order to overcome classical wavelets' shortcoming in image processing problems, people developed many producing systems, which built up wavelet family. In this paper, the notion of AB-multiresolution analysis is generalized, and the corresponding theory is developed. For an AB-multiresolution analysis associated with any expanding matrices, we deduce that there exists a singe scaling function in its reducing subspace. Under some conditions, wavelets with composite dilations can be gotten by AB-multiresolution analysis, which permits the existence of fast implementation algorithm. Then, we provide an approach to design the wavelets with composite dilations by classic wavelets. Our way consists of separable and partly nonseparable cases. In each section, we construct all kinds of examples with nice properties to prove our theory.
Some applications of wavelets to physics
International Nuclear Information System (INIS)
Thompson, C.R.
1992-01-01
A thorough description of a fast wavelet transform algorithm (FWT) and its inverse (IFWT) are given. The effects of noise in the wavelet transform are studied, in particular the effects on signal reconstruction. A model for additive white noise on the coefficients is presented along with two methods that can help to suppress the effects of noise corruption of the signal. Problems of improper sampling are studied, including the propagation of uncertainty through the FWT and IFWT. Interpolation techniques and data compression are also studied. The FWT and IFWT are generalized for analysis of two dimensional images. Methods for edge detection are discussed as well as contrast improvement and data compression. Finally, wavelets are applied to electromagnetic wave propagation problems. Formulas relating the wavelet and Fourier transforms are given, and expansions of time-dependent electromagnetic fields using both fixed and moving wavelet bases are studied
Wavelets and the Lifting Scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Wavelets and the lifting scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
2012-01-01
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Wavelets and the lifting scheme
DEFF Research Database (Denmark)
la Cour-Harbo, Anders; Jensen, Arne
2009-01-01
The objective of this article is to give a concise introduction to the discrete wavelet transform (DWT) based on a technique called lifting. The lifting technique allows one to give an elementary, but rigorous, definition of the DWT, with modest requirements on the reader. A basic knowledge...... of linear algebra and signal processing will suffice. The lifting based definition is equivalent to the usual filer bank based definition of the DWT. The article does not discuss applications in any detail. The reader is referred to other articles in this collection....
Mammography image compression using Wavelet
International Nuclear Information System (INIS)
Azuhar Ripin; Md Saion Salikin; Wan Hazlinda Ismail; Asmaliza Hashim; Norriza Md Isa
2004-01-01
Image compression plays an important role in many applications like medical imaging, televideo conferencing, remote sensing, document and facsimile transmission, which depend on the efficient manipulation, storage, and transmission of binary, gray scale, or color images. In Medical imaging application such Picture Archiving and Communication System (PACs), the image size or image stream size is too large and requires a large amount of storage space or high bandwidth for communication. Image compression techniques are divided into two categories namely lossy and lossless data compression. Wavelet method used in this project is a lossless compression method. In this method, the exact original mammography image data can be recovered. In this project, mammography images are digitized by using Vider Sierra Plus digitizer. The digitized images are compressed by using this wavelet image compression technique. Interactive Data Language (IDLs) numerical and visualization software is used to perform all of the calculations, to generate and display all of the compressed images. Results of this project are presented in this paper. (Author)
Wavelet based Image Registration Technique for Matching Dental x-rays
P. Ramprasad; H. C. Nagaraj; M. K. Parasuram
2008-01-01
Image registration plays an important role in the diagnosis of dental pathologies such as dental caries, alveolar bone loss and periapical lesions etc. This paper presents a new wavelet based algorithm for registering noisy and poor contrast dental x-rays. Proposed algorithm has two stages. First stage is a preprocessing stage, removes the noise from the x-ray images. Gaussian filter has been used. Second stage is a geometric transformation stage. Proposed work uses two l...
DEFF Research Database (Denmark)
Andersen, Klaus Ejner
1985-01-01
Guinea pig maximization tests (GPMT) with chlorocresol were performed to ascertain whether the sensitization rate was affected by minor changes in the Freund's complete adjuvant (FCA) emulsion used. Three types of emulsion were evaluated: the oil phase was mixed with propylene glycol, saline...
Wavelet analysis of the nuclear phase space
International Nuclear Information System (INIS)
Jouault, B.; Sebille, F.; De La Mota, V.
1997-01-01
The description of complex systems requires to select and to compact the relevant information. The wavelet theory constitutes an appropriate framework for defining adapted representation bases obtained from a controlled hierarchy of approximations. The optimization of the wavelet analysis depend mainly on the chosen analysis method and wavelet family. Here the analysis of the harmonic oscillator wave function was carried out by considering a Spline bi-orthogonal wavelet base which satisfy the symmetry requirements and can be approximated by simple analytical functions. The goal of this study was to determine a selection criterion allowing to minimize the number of elements considered for an optimal description of the analysed functions. An essential point consists in utilization of the wavelet complementarity and of the scale functions in order to reproduce the oscillating and peripheral parts of the wave functions. The wavelet base representation allows defining a sequence of approximations of the density matrix. Thus, this wavelet representation of the density matrix offers an optimal base for describing both the static nuclear configurations and their time evolution. This information compacting procedure is performed in a controlled manner and preserves the structure of the system wave functions and consequently some of its quantum properties
Applications of a fast, continuous wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Dress, W.B.
1997-02-01
A fast, continuous, wavelet transform, based on Shannon`s sampling theorem in frequency space, has been developed for use with continuous mother wavelets and sampled data sets. The method differs from the usual discrete-wavelet approach and the continuous-wavelet transform in that, here, the wavelet is sampled in the frequency domain. Since Shannon`s sampling theorem lets us view the Fourier transform of the data set as a continuous function in frequency space, the continuous nature of the functions is kept up to the point of sampling the scale-translation lattice, so the scale-translation grid used to represent the wavelet transform is independent of the time- domain sampling of the signal under analysis. Computational cost and nonorthogonality aside, the inherent flexibility and shift invariance of the frequency-space wavelets has advantages. The method has been applied to forensic audio reconstruction speaker recognition/identification, and the detection of micromotions of heavy vehicles associated with ballistocardiac impulses originating from occupants` heart beats. Audio reconstruction is aided by selection of desired regions in the 2-D representation of the magnitude of the transformed signal. The inverse transform is applied to ridges and selected regions to reconstruct areas of interest, unencumbered by noise interference lying outside these regions. To separate micromotions imparted to a mass-spring system (e.g., a vehicle) by an occupants beating heart from gross mechanical motions due to wind and traffic vibrations, a continuous frequency-space wavelet, modeled on the frequency content of a canonical ballistocardiogram, was used to analyze time series taken from geophone measurements of vehicle micromotions. By using a family of mother wavelets, such as a set of Gaussian derivatives of various orders, features such as the glottal closing rate and word and phrase segmentation may be extracted from voice data.
Tri-maximal vs. bi-maximal neutrino mixing
International Nuclear Information System (INIS)
Scott, W.G
2000-01-01
It is argued that data from atmospheric and solar neutrino experiments point strongly to tri-maximal or bi-maximal lepton mixing. While ('optimised') bi-maximal mixing gives an excellent a posteriori fit to the data, tri-maximal mixing is an a priori hypothesis, which is not excluded, taking account of terrestrial matter effects
Directory of Open Access Journals (Sweden)
Y. A. Bladyko
2010-01-01
Full Text Available The paper contains definition of a smoothing factor which is suitable for any rectifier filter. The formulae of complex smoothing factors have been developed for simple and complex passive filters. The paper shows conditions for application of calculation formulae and filters.
Research of generalized wavelet transformations of Haar correctness in remote sensing of the Earth
Kazaryan, Maretta; Shakhramanyan, Mihail; Nedkov, Roumen; Richter, Andrey; Borisova, Denitsa; Stankova, Nataliya; Ivanova, Iva; Zaharinova, Mariana
2017-10-01
In this paper, Haar's generalized wavelet functions are applied to the problem of ecological monitoring by the method of remote sensing of the Earth. We study generalized Haar wavelet series and suggest the use of Tikhonov's regularization method for investigating them for correctness. In the solution of this problem, an important role is played by classes of functions that were introduced and described in detail by I.M. Sobol for studying multidimensional quadrature formulas and it contains functions with rapidly convergent series of wavelet Haar. A theorem on the stability and uniform convergence of the regularized summation function of the generalized wavelet-Haar series of a function from this class with approximate coefficients is proved. The article also examines the problem of using orthogonal transformations in Earth remote sensing technologies for environmental monitoring. Remote sensing of the Earth allows to receive from spacecrafts information of medium, high spatial resolution and to conduct hyperspectral measurements. Spacecrafts have tens or hundreds of spectral channels. To process the images, the device of discrete orthogonal transforms, and namely, wavelet transforms, was used. The aim of the work is to apply the regularization method in one of the problems associated with remote sensing of the Earth and subsequently to process the satellite images through discrete orthogonal transformations, in particular, generalized Haar wavelet transforms. General methods of research. In this paper, Tikhonov's regularization method, the elements of mathematical analysis, the theory of discrete orthogonal transformations, and methods for decoding of satellite images are used. Scientific novelty. The task of processing of archival satellite snapshots (images), in particular, signal filtering, was investigated from the point of view of an incorrectly posed problem. The regularization parameters for discrete orthogonal transformations were determined.
[Research on electrocardiogram de-noising algorithm based on wavelet neural networks].
Wan, Xiangkui; Zhang, Jun
2010-12-01
In this paper, the ECG de-noising technology based on wavelet neural networks (WNN) is used to deal with the noises in Electrocardiogram (ECG) signal. The structure of WNN, which has the outstanding nonlinear mapping capability, is designed as a nonlinear filter used for ECG to cancel the baseline wander, electromyo-graphical interference and powerline interference. The network training algorithm and de-noising experiments results are presented, and some key points of the WNN filter using ECG de-noising are discussed.
Significance tests for the wavelet cross spectrum and wavelet linear coherence
Directory of Open Access Journals (Sweden)
Z. Ge
2008-12-01
Full Text Available This work attempts to develop significance tests for the wavelet cross spectrum and the wavelet linear coherence as a follow-up study on Ge (2007. Conventional approaches that are used by Torrence and Compo (1998 based on stationary background noise time series were used here in estimating the sampling distributions of the wavelet cross spectrum and the wavelet linear coherence. The sampling distributions are then used for establishing significance levels for these two wavelet-based quantities. In addition to these two wavelet quantities, properties of the phase angle of the wavelet cross spectrum of, or the phase difference between, two Gaussian white noise series are discussed. It is found that the tangent of the principal part of the phase angle approximately has a standard Cauchy distribution and the phase angle is uniformly distributed, which makes it impossible to establish significance levels for the phase angle. The simulated signals clearly show that, when there is no linear relation between the two analysed signals, the phase angle disperses into the entire range of [−π,π] with fairly high probabilities for values close to ±π to occur. Conversely, when linear relations are present, the phase angle of the wavelet cross spectrum settles around an associated value with considerably reduced fluctuations. When two signals are linearly coupled, their wavelet linear coherence will attain values close to one. The significance test of the wavelet linear coherence can therefore be used to complement the inspection of the phase angle of the wavelet cross spectrum. The developed significance tests are also applied to actual data sets, simultaneously recorded wind speed and wave elevation series measured from a NOAA buoy on Lake Michigan. Significance levels of the wavelet cross spectrum and the wavelet linear coherence between the winds and the waves reasonably separated meaningful peaks from those generated by randomness in the data set. As
Swain, Sushree Diptimayee; Ray, Pravat Kumar; Mohanty, K. B.
2016-06-01
This research paper discover the design of a shunt Passive Power Filter (PPF) in Hybrid Series Active Power Filter (HSAPF) that employs a novel analytic methodology which is superior than FFT analysis. This novel approach consists of the estimation, detection and classification of the signals. The proposed method is applied to estimate, detect and classify the power quality (PQ) disturbance such as harmonics. This proposed work deals with three methods: the harmonic detection through wavelet transform method, the harmonic estimation by Kalman Filter algorithm and harmonic classification by decision tree method. From different type of mother wavelets in wavelet transform method, the db8 is selected as suitable mother wavelet because of its potency on transient response and crouched oscillation at frequency domain. In harmonic compensation process, the detected harmonic is compensated through Hybrid Series Active Power Filter (HSAPF) based on Instantaneous Reactive Power Theory (IRPT). The efficacy of the proposed method is verified in MATLAB/SIMULINK domain and as well as with an experimental set up. The obtained results confirm the superiority of the proposed methodology than FFT analysis. This newly proposed PPF is used to make the conventional HSAPF more robust and stable.
An image adaptive, wavelet-based watermarking of digital images
Agreste, Santa; Andaloro, Guido; Prestipino, Daniela; Puccio, Luigia
2007-12-01
In digital management, multimedia content and data can easily be used in an illegal way--being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario digital watermark techniques are emerging as a valid solution. In this paper, we describe an algorithm--called WM2.0--for an invisible watermark: private, strong, wavelet-based and developed for digital images protection and authenticity. Using discrete wavelet transform (DWT) is motivated by good time-frequency features and well-matching with human visual system directives. These two combined elements are important in building an invisible and robust watermark. WM2.0 works on a dual scheme: watermark embedding and watermark detection. The watermark is embedded into high frequency DWT components of a specific sub-image and it is calculated in correlation with the image features and statistic properties. Watermark detection applies a re-synchronization between the original and watermarked image. The correlation between the watermarked DWT coefficients and the watermark signal is calculated according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has shown to be resistant against geometric, filtering and StirMark attacks with a low rate of false alarm.
Nazarzadeh, Kimia; Arjunan, Sridhar P; Kumar, Dinesh K; Das, Debi Prasad
2016-08-01
In this study, we have analyzed the accelerometer data recorded during gait analysis of Parkinson disease patients for detecting freezing of gait (FOG) episodes. The proposed method filters the recordings for noise reduction of the leg movement changes and computes the wavelet coefficients to detect FOG events. Publicly available FOG database was used and the technique was evaluated using receiver operating characteristic (ROC) analysis. Results show a higher performance of the wavelet feature in discrimination of the FOG events from the background activity when compared with the existing technique.
Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel
Energy Technology Data Exchange (ETDEWEB)
Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P [College of Mechatronic Engineering and Automation, National University of Defense Technology, Changsha (China)
2006-10-15
Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method.
Automatic Modulation Recognition by Support Vector Machines Using Wavelet Kernel
International Nuclear Information System (INIS)
Feng, X Z; Yang, J; Luo, F L; Chen, J Y; Zhong, X P
2006-01-01
Automatic modulation identification plays a significant role in electronic warfare, electronic surveillance systems and electronic counter measure. The task of modulation recognition of communication signals is to determine the modulation type and signal parameters. In fact, automatic modulation identification can be range to an application of pattern recognition in communication field. The support vector machines (SVM) is a new universal learning machine which is widely used in the fields of pattern recognition, regression estimation and probability density. In this paper, a new method using wavelet kernel function was proposed, which maps the input vector xi into a high dimensional feature space F. In this feature space F, we can construct the optimal hyperplane that realizes the maximal margin in this space. That is to say, we can use SVM to classify the communication signals into two groups, namely analogue modulated signals and digitally modulated signals. In addition, computer simulation results are given at last, which show good performance of the method
Numerical shaping of the ultrasonic wavelet
International Nuclear Information System (INIS)
Bonis, M.
1991-01-01
Improving the performance and the quality of ultrasonic testing requires the numerical control of the shape of the driving signal applied to the piezoelectric transducer. This allows precise shaping of the ultrasonic field wavelet and corrections for the physical defects of the transducer, which are mainly due to the damper or the lens. It also does away with the need for an accurate electric matching. It then becomes feasible to characterize, a priori, the ultrasonic wavelet by means of temporal and/or spectral specifications and to use, subsequently, an adaptative algorithm to calculate the corresponding driving wavelet. Moreover, the versatility resulting from the numerical control of this wavelet allows it to be changed in real time during a test
Building nonredundant adaptive wavelets by update lifting
H.J.A.M. Heijmans (Henk); B. Pesquet-Popescu; G. Piella (Gema)
2002-01-01
textabstractAdaptive wavelet decompositions appear useful in various applications in image and video processing, such as image analysis, compression, feature extraction, denoising and deconvolution, or optic flow estimation. For such tasks it may be important that the multiresolution representations
Scalets, wavelets and (complex) turning point quantization
Handy, C. R.; Brooks, H. A.
2001-05-01
Despite the many successes of wavelet analysis in image and signal processing, the incorporation of continuous wavelet transform theory within quantum mechanics has lacked a compelling, first principles, motivating analytical framework, until now. For arbitrary one-dimensional rational fraction Hamiltonians, we develop a simple, unified formalism, which clearly underscores the complementary, and mutually interdependent, role played by moment quantization theory (i.e. via scalets, as defined herein) and wavelets. This analysis involves no approximation of the Hamiltonian within the (equivalent) wavelet space, and emphasizes the importance of (complex) multiple turning point contributions in the quantization process. We apply the method to three illustrative examples. These include the (double-well) quartic anharmonic oscillator potential problem, V(x) = Z2x2 + gx4, the quartic potential, V(x) = x4, and the very interesting and significant non-Hermitian potential V(x) = -(ix)3, recently studied by Bender and Boettcher.
Using wavelet features for analyzing gamma lines
International Nuclear Information System (INIS)
Medhat, M.E.; Abdel-hafiez, A.; Hassan, M.F.; Ali, M.A.; Uzhinskii, V.V.
2004-01-01
Data processing methods for analyzing gamma ray spectra with symmetric bell-shaped peaks form are considered. In many cases the peak form is symmetrical bell shaped in particular a Gaussian case is the most often used due to many physical reasons. The problem is how to evaluate parameters of such peaks, i.e. their positions, amplitudes and also their half-widths, that is for a single peak and overlapped peaks. Through wavelet features by using Marr wavelet (Mexican Hat) as a correlation method, it could be to estimate the optimal wavelet parameters and to locate peaks in the spectrum. The performance of the proposed method and others shows a better quality of wavelet transform method
Effective implementation of wavelet Galerkin method
Finěk, Václav; Šimunková, Martina
2012-11-01
It was proved by W. Dahmen et al. that an adaptive wavelet scheme is asymptotically optimal for a wide class of elliptic equations. This scheme approximates the solution u by a linear combination of N wavelets and a benchmark for its performance is the best N-term approximation, which is obtained by retaining the N largest wavelet coefficients of the unknown solution. Moreover, the number of arithmetic operations needed to compute the approximate solution is proportional to N. The most time consuming part of this scheme is the approximate matrix-vector multiplication. In this contribution, we will introduce our implementation of wavelet Galerkin method for Poisson equation -Δu = f on hypercube with homogeneous Dirichlet boundary conditions. In our implementation, we identified nonzero elements of stiffness matrix corresponding to the above problem and we perform matrix-vector multiplication only with these nonzero elements.
Framelets and wavelets algorithms, analysis, and applications
Han, Bin
2017-01-01
Marking a distinct departure from the perspectives of frame theory and discrete transforms, this book provides a comprehensive mathematical and algorithmic introduction to wavelet theory. As such, it can be used as either a textbook or reference guide. As a textbook for graduate mathematics students and beginning researchers, it offers detailed information on the basic theory of framelets and wavelets, complemented by self-contained elementary proofs, illustrative examples/figures, and supplementary exercises. Further, as an advanced reference guide for experienced researchers and practitioners in mathematics, physics, and engineering, the book addresses in detail a wide range of basic and advanced topics (such as multiwavelets/multiframelets in Sobolev spaces and directional framelets) in wavelet theory, together with systematic mathematical analysis, concrete algorithms, and recent developments in and applications of framelets and wavelets. Lastly, the book can also be used to teach on or study selected spe...
Image Registration Using Redundant Wavelet Transforms
National Research Council Canada - National Science Library
Brown, Richard
2001-01-01
.... In our research, we present a fundamentally new wavelet-based registration algorithm utilizing redundant transforms and a masking process to suppress the adverse effects of noise and improve processing efficiency...
Thin film description by wavelet coefficients statistics
Czech Academy of Sciences Publication Activity Database
Boldyš, Jiří; Hrach, R.
2005-01-01
Roč. 55, č. 1 (2005), s. 55-64 ISSN 0011-4626 Grant - others:GA UK(CZ) 173/2003 Institutional research plan: CEZ:AV0Z10750506 Keywords : thin films * wavelet transform * descriptors * histogram model Subject RIV: BD - Theory of Information Impact factor: 0.360, year: 2005 http://library.utia.cas.cz/separaty/2009/ZOI/boldys-thin film description by wavelet coefficients statistics .pdf
Wavelet and Blend maps for texture synthesis
Du Jin-Lian; Wang Song; Meng Xianhai
2011-01-01
blending is now a popular technology for large realtime texture synthesis .Nevertheless, creating blend map during rendering is time and computation consuming work. In this paper, we exploited a method to create a kind of blend tile which can be tile together seamlessly. Note that blend map is in fact a kind of image, which is Markov Random Field, contains multiresolution signals, while wavelet is a powerful way to process multiresolution signals, we use wavelet to process the traditional ble...
Coherent multiscale image processing using dual-tree quaternion wavelets.
Chan, Wai Lam; Choi, Hyeokho; Baraniuk, Richard G
2008-07-01
The dual-tree quaternion wavelet transform (QWT) is a new multiscale analysis tool for geometric image features. The QWT is a near shift-invariant tight frame representation whose coefficients sport a magnitude and three phases: two phases encode local image shifts while the third contains image texture information. The QWT is based on an alternative theory for the 2-D Hilbert transform and can be computed using a dual-tree filter bank with linear computational complexity. To demonstrate the properties of the QWT's coherent magnitude/phase representation, we develop an efficient and accurate procedure for estimating the local geometrical structure of an image. We also develop a new multiscale algorithm for estimating the disparity between a pair of images that is promising for image registration and flow estimation applications. The algorithm features multiscale phase unwrapping, linear complexity, and sub-pixel estimation accuracy.
Infrared Image Segmentation by Combining Fractal Geometry with Wavelet Transformation
Directory of Open Access Journals (Sweden)
Xionggang Tu
2014-11-01
Full Text Available An infrared image is decomposed into three levels by discrete stationary wavelet transform (DSWT. Noise is reduced by wiener filter in the high resolution levels in the DSWT domain. Nonlinear gray transformation operation is used to enhance details in the low resolution levels in the DSWT domain. Enhanced infrared image is obtained by inverse DSWT. The enhanced infrared image is divided into many small blocks. The fractal dimensions of all the blocks are computed. Region of interest (ROI is extracted by combining all the blocks, which have similar fractal dimensions. ROI is segmented by global threshold method. The man-made objects are efficiently separated from the infrared image by the proposed method.
Wavelets, ridgelets, and curvelets for Poisson noise removal.
Zhang, Bo; Fadili, Jalal M; Starck, Jean-Luc
2008-07-01
In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST) applied on a filtered discrete Poisson process, yielding a near Gaussian process with asymptotic constant variance. This new transform, which can be deemed as an extension of the Anscombe transform to filtered data, is simple, fast, and efficient in (very) low-count situations. We combine this VST with the filter banks of wavelets, ridgelets and curvelets, leading to multiscale VSTs (MS-VSTs) and nonlinear decomposition schemes. By doing so, the noise-contaminated coefficients of these MS-VST-modified transforms are asymptotically normally distributed with known variances. A classical hypothesis-testing framework is adopted to detect the significant coefficients, and a sparsity-driven iterative scheme reconstructs properly the final estimate. A range of examples show the power of this MS-VST approach for recovering important structures of various morphologies in (very) low-count images. These results also demonstrate that the MS-VST approach is competitive relative to many existing denoising methods.
Block-classified bidirectional motion compensation scheme for wavelet-decomposed digital video
Energy Technology Data Exchange (ETDEWEB)
Zafar, S. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.; Zhang, Y.Q. [David Sarnoff Research Center, Princeton, NJ (United States); Jabbari, B. [George Mason Univ., Fairfax, VA (United States)
1997-08-01
In this paper the authors introduce a block-classified bidirectional motion compensation scheme for the previously developed wavelet-based video codec, where multiresolution motion estimation is performed in the wavelet domain. The frame classification structure described in this paper is similar to that used in the MPEG standard. Specifically, the I-frames are intraframe coded, the P-frames are interpolated from a previous I- or a P-frame, and the B-frames are bidirectional interpolated frames. They apply this frame classification structure to the wavelet domain with variable block sizes and multiresolution representation. They use a symmetric bidirectional scheme for the B-frames and classify the motion blocks as intraframe, compensated either from the preceding or the following frame, or bidirectional (i.e., compensated based on which type yields the minimum energy). They also introduce the concept of F-frames, which are analogous to P-frames but are predicted from the following frame only. This improves the overall quality of the reconstruction in a group of pictures (GOP) but at the expense of extra buffering. They also study the effect of quantization of the I-frames on the reconstruction of a GOP, and they provide intuitive explanation for the results. In addition, the authors study a variety of wavelet filter-banks to be used in a multiresolution motion-compensated hierarchical video codec.
High-resolution time-frequency representation of EEG data using multi-scale wavelets
Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina
2017-09-01
An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.
Optimal wavelet transform for the detection of microaneurysms in retina photographs.
Quellec, Gwénolé; Lamard, Mathieu; Josselin, Pierre Marie; Cazuguel, Guy; Cochener, Béatrice; Roux, Christian
2008-09-01
In this paper, we propose an automatic method to detect microaneurysms in retina photographs. Microaneurysms are the most frequent and usually the first lesions to appear as a consequence of diabetic retinopathy. So, their detection is necessary for both screening the pathology and follow up (progression measurement). Automating this task, which is currently performed manually, would bring more objectivity and reproducibility. We propose to detect them by locally matching a lesion template in subbands of wavelet transformed images. To improve the method performance, we have searched for the best adapted wavelet within the lifting scheme framework. The optimization process is based on a genetic algorithm followed by Powell's direction set descent. Results are evaluated on 120 retinal images analyzed by an expert and the optimal wavelet is compared to different conventional mother wavelets. These images are of three different modalities: there are color photographs, green filtered photographs, and angiographs. Depending on the imaging modality, microaneurysms were detected with a sensitivity of respectively 89.62%, 90.24%, and 93.74% and a positive predictive value of respectively 89.50%, 89.75%, and 91.67%, which is better than previously published methods.
Gendreau, Keith; Cash, Webster; Gorenstein, Paul; Windt, David; Kaaret, Phil; Reynolds, Chris
2004-01-01
The Beyond Einstein Program in NASA's Office of Space Science Structure and Evolution of the Universe theme spells out the top level scientific requirements for a Black Hole Imager in its strategic plan. The MAXIM mission will provide better than one tenth of a microarcsecond imaging in the X-ray band in order to satisfy these requirements. We will overview the driving requirements to achieve these goals and ultimately resolve the event horizon of a supermassive black hole. We will present the current status of this effort that includes a study of a baseline design as well as two alternative approaches.
Social group utility maximization
Gong, Xiaowen; Yang, Lei; Zhang, Junshan
2014-01-01
This SpringerBrief explains how to leverage mobile users' social relationships to improve the interactions of mobile devices in mobile networks. It develops a social group utility maximization (SGUM) framework that captures diverse social ties of mobile users and diverse physical coupling of mobile devices. Key topics include random access control, power control, spectrum access, and location privacy.This brief also investigates SGUM-based power control game and random access control game, for which it establishes the socially-aware Nash equilibrium (SNE). It then examines the critical SGUM-b
Application of Improved Wavelet Thresholding Function in Image Denoising Processing
Directory of Open Access Journals (Sweden)
Hong Qi Zhang
2014-07-01
Full Text Available Wavelet analysis is a time – frequency analysis method, time-frequency localization problems are well solved, this paper analyzes the basic principles of the wavelet transform and the relationship between the signal singularity Lipschitz exponent and the local maxima of the wavelet transform coefficients mold, the principles of wavelet transform in image denoising are analyzed, the disadvantages of traditional wavelet thresholding function are studied, wavelet threshold function, the discontinuity of hard threshold and constant deviation of soft threshold are improved, image is denoised through using the improved threshold function.
Cannistraci, Carlo Vittorio
2015-01-26
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet\\'s performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
International Nuclear Information System (INIS)
Yang, Wei; Yang, Yuanhong; Yang, Mingwei
2014-01-01
We propose a fast digital envelope detector (DED) based on the generalized harmonic wavelet transform to improve the performance of coherent heterodyne Brillouin optical time domain reflectometry. The proposed DED can obtain undistorted envelopes due to the zero phase-shift ideal bandpass filter (BPF) characteristics of the generalized harmonic wavelet (GHW). Its envelope average ability benefits from the passband designing flexibility of the GHW, and its demodulation speed can be accelerated by using a fast algorithm that only analyses signals of interest within the passband of the GHW with reduced computational complexity. The feasibility and advantage of the proposed DED are verified by simulations and experiments. With an optimized bandwidth, Brillouin frequency shift accuracy improvements of 19.4% and 11.14%, as well as envelope demodulation speed increases of 39.1% and 24.9%, are experimentally attained by the proposed DED over Hilbert transform (HT) and Morlet wavelet transform (MWT) based DEDs, respectively. Spatial resolution by the proposed DED is undegraded, which is identical to the undegraded value by HT-DED with an allpass filter characteristic and better than the degraded value by MWT-DED with a Gaussian BPF characteristic. (paper)
International Nuclear Information System (INIS)
Chouakri, S A; Djaafri, O; Taleb-Ahmed, A
2013-01-01
We present in this work an algorithm for electrocardiogram (ECG) signal compression aimed to its transmission via telecommunication channel. Basically, the proposed ECG compression algorithm is articulated on the use of wavelet transform, leading to low/high frequency components separation, high order statistics based thresholding, using level adjusted kurtosis value, to denoise the ECG signal, and next a linear predictive coding filter is applied to the wavelet coefficients producing a lower variance signal. This latter one will be coded using the Huffman encoding yielding an optimal coding length in terms of average value of bits per sample. At the receiver end point, with the assumption of an ideal communication channel, the inverse processes are carried out namely the Huffman decoding, inverse linear predictive coding filter and inverse discrete wavelet transform leading to the estimated version of the ECG signal. The proposed ECG compression algorithm is tested upon a set of ECG records extracted from the MIT-BIH Arrhythmia Data Base including different cardiac anomalies as well as the normal ECG signal. The obtained results are evaluated in terms of compression ratio and mean square error which are, respectively, around 1:8 and 7%. Besides the numerical evaluation, the visual perception demonstrates the high quality of ECG signal restitution where the different ECG waves are recovered correctly
Ji, Zhan-Huai; Yan, Sheng-Gang
2017-12-01
This paper presents an analytical study of the complete transform of improved Gabor wavelets (IGWs), and discusses its application to the processing and interpretation of seismic signals. The complete Gabor wavelet transform has the following properties. First, unlike the conventional transform, the improved Gabor wavelet transform (IGWT) maps time domain signals to the time-frequency domain instead of the time-scale domain. Second, the IGW's dominant frequency is fixed, so the transform can perform signal frequency division, where the dominant frequency components of the extracted sub-band signal carry essentially the same information as the corresponding components of the original signal, and the subband signal bandwidth can be regulated effectively by the transform's resolution factor. Third, a time-frequency filter consisting of an IGWT and its inverse transform can accurately locate target areas in the time-frequency field and perform filtering in a given time-frequency range. The complete IGW transform's properties are investigated using simulation experiments and test cases, showing positive results for seismic signal processing and interpretation, such as enhancing seismic signal resolution, permitting signal frequency division, and allowing small faults to be identified.
Detecting microcalcifications in digital mammogram using wavelets
International Nuclear Information System (INIS)
Yang Jucheng; Park Dongsun
2004-01-01
Breast cancer is still one of main mortality causes in women, but the early detection can increase the chance of cure. Microcalcifications are small size structures, which can indicate the presence of cancer since they are often associated to the most different types of breast tumors. However, they very small size and the X-ray systems limitations lead to constraints to the adequate visualization of such structures, which means that the microcalcifications can be missed many times in mammogram visual examination. In addition, the human eyes are not able to distinguish minimal tonality differences, which can be another constraint when mammogram image presents poor contrast between microcalcifications and the tissues around them. Computer-aided diagnosis (CAD) schemes are being developed in order to increase the probabilities of early detection. To enhance and detect the microcalcifications in the mammograms we use the wavelets transform. From a signal processing point of view, microcalcifications are high frequency components in mammograms. Due to the multi-resolution decomposition capacity of the wavelet transform, we can decompose the image into different resolution levels which sensitive to different frequency bands. By choosing an appropriate wavelet and a right resolution level, we can effectively enhance and detect the microcalcifications in digital mammogram. In this work, we describe a new four-step method for the detection of microcalcifications: segmentation, wavelets transform processing, labeling and post-processing. The segmentation step is to split the breast area into 256x256 segments. For each segmented sub-image, wavelet transform is operated on it. For comparing study wavelet transform method, 4 typical family wavelets and 4 decomposing levels is discussed. We choose four family wavelets for detecting microcalcifications, that is, Daubechies, Biothgonai, Coieflets and Symlets wavelets, for simply, bd4, bior3.7, coif3, sym2 are chosen as the
International Nuclear Information System (INIS)
Butterworth, D.J.
1980-01-01
This invention relates to liquid filters, precoated by replaceable powders, which are used in the production of ultra pure water required for steam generation of electricity. The filter elements are capable of being installed and removed by remote control so that they can be used in nuclear power reactors. (UK)
International Nuclear Information System (INIS)
Soumia, Sid Ahmed; Messali, Zoubeida; Ouahabi, Abdeldjalil; Trepout, Sylvain; Messaoudi, Cedric; Marco, Sergio
2015-01-01
The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the
Wen, Shaobo; An, Haizhong; Chen, Zhihua; Liu, Xueyong
2017-08-01
In traditional econometrics, a time series must be in a stationary sequence. However, it usually shows time-varying fluctuations, and it remains a challenge to execute a multiscale analysis of the data and discover the topological characteristics of conduction in different scales. Wavelet analysis and complex networks in physical statistics have special advantages in solving these problems. We select the exchange rate variable from the Chinese market and the commodity price index variable from the world market as the time series of our study. We explore the driving factors behind the behavior of the two markets and their topological characteristics in three steps. First, we use the Kalman filter to find the optimal estimation of the relationship between the two markets. Second, wavelet analysis is used to extract the scales of the relationship that are driven by different frequency wavelets. Meanwhile, we search for the actual economic variables corresponding to different frequency wavelets. Finally, a complex network is used to search for the transfer characteristics of the combination of states driven by different frequency wavelets. The results show that statistical physics have a unique advantage over traditional econometrics. The Chinese market has time-varying impacts on the world market: it has greater influence when the world economy is stable and less influence in times of turmoil. The process of forming the state combination is random. Transitions between state combinations have a clustering feature. Based on these characteristics, we can effectively reduce the information burden on investors and correctly respond to the government's policy mix.
Directory of Open Access Journals (Sweden)
Levi Lopes Teixeira
2015-12-01
Full Text Available Time series forecasting is widely used in various areas of human knowledge, especially in the planning and strategic direction of companies. The success of this task depends on the forecasting techniques applied. In this paper, a hybrid approach to project time series is suggested. To validate the methodology, a time series already modeled by other authors was chosen, allowing the comparison of results. The proposed methodology includes the following techniques: wavelet shrinkage, wavelet decomposition at level r, and artificial neural networks (ANN. Firstly, a time series to be forecasted is submitted to the proposed wavelet filtering method, which decomposes it to components of trend and linear residue. Then, both are decomposed via level r wavelet decomposition, generating r + 1 Wavelet Components (WCs for each one; and then each WC is individually modeled by an ANN. Finally, the predictions for all WCs are linearly combined, producing forecasts to the underlying time series. For evaluating purposes, the time series of Canadian Lynx has been used, and all results achieved by the proposed method were better than others in existing literature.
Multiresolution wavelet-ANN model for significant wave height forecasting.
Digital Repository Service at National Institute of Oceanography (India)
Deka, P.C.; Mandal, S.; Prahlada, R.
Hybrid wavelet artificial neural network (WLNN) has been applied in the present study to forecast significant wave heights (Hs). Here Discrete Wavelet Transformation is used to preprocess the time series data (Hs) prior to Artificial Neural Network...
A New Formula for the Inverse Wavelet Transform
Sun, Wenchang
2010-01-01
Finding a computationally efficient algorithm for the inverse continuous wavelet transform is a fundamental topic in applications. In this paper, we show the convergence of the inverse wavelet transform.
Wavelet transforms as solutions of partial differential equations
Energy Technology Data Exchange (ETDEWEB)
Zweig, G.
1997-10-01
This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Wavelet transforms are useful in representing transients whose time and frequency structure reflect the dynamics of an underlying physical system. Speech sound, pressure in turbulent fluid flow, or engine sound in automobiles are excellent candidates for wavelet analysis. This project focused on (1) methods for choosing the parent wavelet for a continuous wavelet transform in pattern recognition applications and (2) the more efficient computation of continuous wavelet transforms by understanding the relationship between discrete wavelet transforms and discretized continuous wavelet transforms. The most interesting result of this research is the finding that the generalized wave equation, on which the continuous wavelet transform is based, can be used to understand phenomena that relate to the process of hearing.
Wavelet Transforms: Application to Data Analysis - I -10 ...
Indian Academy of Sciences (India)
from 0 to 00, whereas translation index k takes values from -00 .... scaling function in any wavelet basis set. ..... sets derived from diverse sources like stock market, cos- ... [4] G B Folland, From Calculus to Wavelets: A New Mathematical Tech-.
Wavelet processing techniques for digital mammography
Laine, Andrew F.; Song, Shuwu
1992-09-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Similar to traditional coarse to fine matching strategies, the radiologist may first choose to look for coarse features (e.g., dominant mass) within low frequency levels of a wavelet transform and later examine finer features (e.g., microcalcifications) at higher frequency levels. In addition, features may be extracted by applying geometric constraints within each level of the transform. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet representations, enhanced by linear, exponential and constant weight functions through scale space. By improving the visualization of breast pathology we can improve the chances of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).
Real-time modeling of primitive environments through wavelet sensors and Hebbian learning
Vaccaro, James M.; Yaworsky, Paul S.
1999-06-01
Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.
Image-adaptive and robust digital wavelet-domain watermarking for images
Zhao, Yi; Zhang, Liping
2018-03-01
We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.
Directory of Open Access Journals (Sweden)
Heru Wahyu Herwanto
2012-09-01
Full Text Available Abstract: The Use of Wavelet Image Enhancement and Image Energy Texture for Detecting Suspected Mass in Mammogram. Breast cancer is one of the most dangerous cancer for female. The risk of the cancer can be lessened by early detection using mammography. This research sets out to detect and sign the edge of suspected mass in mammogram. The method used is an image enhancement and segmentation. The process of image enhancement uses the method of adaptive wavelet enhancement, meanwhile the segmentation uses the calculation of image energy texture with laws filter, smoothing, tressholding, morphology, and boudary extraction. The final result of this method will be compared with those of same method with corrected images abd adaptive histogram equalization. The result of the research shows that there is an improvement of enthropy, deviation standard, and contrast values. The overall execution program takes 1.82869 seconds longer than the adaptive histogram equalization.
Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries
Directory of Open Access Journals (Sweden)
Xiao Wang
2018-05-01
Full Text Available In practical electric vehicle applications, the noise of original discharging/charging voltage (DCV signals are inevitable, which comes from electromagnetic interference and the measurement noise of the sensors. To solve such problems, the Discrete Wavelet Transform (DWT based state of charge (SOC estimation method is proposed in this paper. Through a multi-resolution analysis, the original DCV signals with noise are decomposed into different frequency sub-bands. The desired de-noised DCV signals are then reconstructed by utilizing the inverse discrete wavelet transform, based on the sure rule. With the de-noised DCV signal, the SOC and the parameters are obtained using the adaptive extended Kalman Filter algorithm, and the adaptive forgetting factor recursive least square method. Simulation and experimental results show that the SOC estimation error is less than 1%, which indicates an effective improvement in SOC estimation accuracy.
JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
Directory of Open Access Journals (Sweden)
Thomas André
2007-03-01
Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.
Development of Wavelet Based Tools for Improving the γ-ray Spectrometry
International Nuclear Information System (INIS)
Hamzaoui, E-M.; El Badri, L.; Laraki, K.; Cherkaoui-Elmorsli, R.
2013-06-01
In this article, we propose a wavelet transform based tool to improve the use of gamma ray spectrometry as a nuclear technique. First, we attempt to study the problem of filtering the preamplifier's output signals of HPGe detector used in the measurements chain. Thus, we developed a nonlinear method based on discrete Coiflet transform combined to principal component analysis, which allows a significant improvement of the signal to noise ratio (SNR) at the output of the HPGe preamplifier. In a second step, the continuous wavelet transform, based on the Mexican Hat mother function, is used to achieve an automatic processing of the spectrometric data. This method permits us to get an alternative representation of the gamma energy spectrum. The results of different tests, performed in both the presence and the absence of a gamma radiation source, are illustrated. (authors)
JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding
Directory of Open Access Journals (Sweden)
André Thomas
2007-01-01
Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.
Nuclear data compression and reconstruction via discrete wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
1997-12-31
Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)
Nuclear data compression and reconstruction via discrete wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Park, Young Ryong; Cho, Nam Zin [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)
1998-12-31
Discrete Wavelet Transforms (DWTs) are recent mathematics, and begin to be used in various fields. The wavelet transform can be used to compress the signal and image due to its inherent properties. We applied the wavelet transform compression and reconstruction to the neutron cross section data. Numerical tests illustrate that the signal compression using wavelet is very effective to reduce the data saving spaces. 7 refs., 4 figs., 3 tabs. (Author)
Construction of a class of Daubechies type wavelet bases
International Nuclear Information System (INIS)
Li Dengfeng; Wu Guochang
2009-01-01
Extensive work has been done in the theory and the construction of compactly supported orthonormal wavelet bases of L 2 (R). Some of the most distinguished work was done by Daubechies, who constructed a whole family of such wavelet bases. In this paper, we construct a class of orthonormal wavelet bases by using the principle of Daubechies, and investigate the length of support and the regularity of these wavelet bases.
Directory of Open Access Journals (Sweden)
Deyu Cui
2018-04-01
Full Text Available State of charge (SOC estimation is becoming increasingly important, along with electric vehicle (EV rapid development, while SOC is one of the most significant parameters for the battery management system, indicating remaining energy and ensuring the safety and reliability of EV. In this paper, a hybrid wavelet neural network (WNN model combining the discrete wavelet transform (DWT method and adaptive WNN is proposed to estimate the SOC of lithium-ion batteries. The WNN model is trained by Levenberg-Marquardt (L-M algorithm, whose inputs are processed by discrete wavelet decomposition and reconstitution. Compared with back-propagation neural network (BPNN, L-M based BPNN (LMBPNN, L-M based WNN (LMWNN, DWT with L-M based BPNN (DWTLMBPNN and extend Kalman filter (EKF, the proposed intelligent SOC estimation method is validated and proved to be effective. Under the New European Driving Cycle (NEDC, the mean absolute error and maximum error can be reduced to 0.59% and 3.13%, respectively. The characteristics of high accuracy and strong robustness of the proposed method are verified by comparison study and robustness evaluation results (e.g., measurement noise test and untrained driving cycle test.
A Comparative Study on Optimal Structural Dynamics Using Wavelet Functions
Directory of Open Access Journals (Sweden)
Seyed Hossein Mahdavi
2015-01-01
Full Text Available Wavelet solution techniques have become the focus of interest among researchers in different disciplines of science and technology. In this paper, implementation of two different wavelet basis functions has been comparatively considered for dynamic analysis of structures. For this aim, computational technique is developed by using free scale of simple Haar wavelet, initially. Later, complex and continuous Chebyshev wavelet basis functions are presented to improve the time history analysis of structures. Free-scaled Chebyshev coefficient matrix and operation of integration are derived to directly approximate displacements of the corresponding system. In addition, stability of responses has been investigated for the proposed algorithm of discrete Haar wavelet compared against continuous Chebyshev wavelet. To demonstrate the validity of the wavelet-based algorithms, aforesaid schemes have been extended to the linear and nonlinear structural dynamics. The effectiveness of free-scaled Chebyshev wavelet has been compared with simple Haar wavelet and two common integration methods. It is deduced that either indirect method proposed for discrete Haar wavelet or direct approach for continuous Chebyshev wavelet is unconditionally stable. Finally, it is concluded that numerical solution is highly benefited by the least computation time involved and high accuracy of response, particularly using low scale of complex Chebyshev wavelet.
On extensions of wavelet systems to dual pairs of frames
DEFF Research Database (Denmark)
Christensen, Ole; Kim, Hong Oh; Kim, Rae Young
2015-01-01
It is an open problem whether any pair of Bessel sequences with wavelet structure can be extended to a pair of dual frames by adding a pair of singly generated wavelet systems. We consider the particular case where the given wavelet systems are generated by the multiscale setup with trigonometric...
Fast generation of computer-generated holograms using wavelet shrinkage.
Shimobaba, Tomoyoshi; Ito, Tomoyoshi
2017-01-09
Computer-generated holograms (CGHs) are generated by superimposing complex amplitudes emitted from a number of object points. However, this superposition process remains very time-consuming even when using the latest computers. We propose a fast calculation algorithm for CGHs that uses a wavelet shrinkage method, eliminating small wavelet coefficient values to express approximated complex amplitudes using only a few representative wavelet coefficients.
Image encryption using the fractional wavelet transform
International Nuclear Information System (INIS)
Vilardy, Juan M; Useche, J; Torres, C O; Mattos, L
2011-01-01
In this paper a technique for the coding of digital images is developed using Fractional Wavelet Transform (FWT) and random phase masks (RPMs). The digital image to encrypt is transformed with the FWT, after the coefficients resulting from the FWT (Approximation, Details: Horizontal, vertical and diagonal) are multiplied each one by different RPMs (statistically independent) and these latest results is applied an Inverse Wavelet Transform (IWT), obtaining the encrypted digital image. The decryption technique is the same encryption technique in reverse sense. This technique provides immediate advantages security compared to conventional techniques, in this technique the mother wavelet family and fractional orders associated with the FWT are additional keys that make access difficult to information to an unauthorized person (besides the RPMs used), thereby the level of encryption security is extraordinarily increased. In this work the mathematical support for the use of the FWT in the computational algorithm for the encryption is also developed.
Partially coherent imaging and spatial coherence wavelets
International Nuclear Information System (INIS)
Castaneda, Roman
2003-03-01
A description of spatially partially coherent imaging based on the propagation of second order spatial coherence wavelets and marginal power spectra (Wigner distribution functions) is presented. In this dynamics, the spatial coherence wavelets will be affected by the system through its elementary transfer function. The consistency of the model with the both extreme cases of full coherent and incoherent imaging was proved. In the last case we obtained the classical concept of optical transfer function as a simple integral of the elementary transfer function. Furthermore, the elementary incoherent response function was introduced as the Fourier transform of the elementary transfer function. It describes the propagation of spatial coherence wavelets form each object point to each image point through a specific point on the pupil planes. The point spread function of the system was obtained by a simple integral of the elementary incoherent response function. (author)
Motion compensation via redundant-wavelet multihypothesis.
Fowler, James E; Cui, Suxia; Wang, Yonghui
2006-10-01
Multihypothesis motion compensation has been widely used in video coding with previous attention focused on techniques employing predictions that are diverse spatially or temporally. In this paper, the multihypothesis concept is extended into the transform domain by using a redundant wavelet transform to produce multiple predictions that are diverse in transform phase. The corresponding multiple-phase inverse transform implicitly combines the phase-diverse predictions into a single spatial-domain prediction for motion compensation. The performance advantage of this redundant-wavelet-multihypothesis approach is investigated analytically, invoking the fact that the multiple-phase inverse involves a projection that significantly reduces the power of a dense-motion residual modeled as additive noise. The analysis shows that redundant-wavelet multihypothesis is capable of up to a 7-dB reduction in prediction-residual variance over an equivalent single-phase, single-hypothesis approach. Experimental results substantiate the performance advantage for a block-based implementation.
ECG denoising with adaptive bionic wavelet transform.
Sayadi, Omid; Shamsollahi, Mohammad Bagher
2006-01-01
In this paper a new ECG denoising scheme is proposed using a novel adaptive wavelet transform, named bionic wavelet transform (BWT), which had been first developed based on a model of the active auditory system. There has been some outstanding features with the BWT such as nonlinearity, high sensitivity and frequency selectivity, concentrated energy distribution and its ability to reconstruct signal via inverse transform but the most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying a new threshold value, one can handle ECG denoising with results comparing to those of wavelet transform (WT). Preliminary tests of BWT application to ECG denoising were constructed on the signals of MIT-BIH database which showed high performance of noise reduction.
Improvement of electrocardiogram by empirical wavelet transform
Chanchang, Vikanda; Kumchaiseemak, Nakorn; Sutthiopad, Malee; Luengviriya, Chaiya
2017-09-01
Electrocardiogram (ECG) is a crucial tool in the detection of cardiac arrhythmia. It is also often used in a routine physical exam, especially, for elderly people. This graphical representation of electrical activity of heart is obtained by a measurement of voltage at the skin; therefore, the signal is always contaminated by noise from various sources. For a proper interpretation, the quality of the ECG should be improved by a noise reduction. In this article, we present a study of a noise filtration in the ECG by using an empirical wavelet transform (EWT). Unlike the traditional wavelet method, EWT is adaptive since the frequency spectrum of the ECG is taken into account in the construction of the wavelet basis. We show that the signal-to-noise ratio increases after the noise filtration for different noise artefacts.
Image Enhancement In HSI Space Using Wavelet Transform
Bansal, Sonia; Malhotra, Deepti
2010-11-01
Image processing modifies images to improve them (enhancement, restoration), extract information (analysis, recognition), and change their structure (composition, image editing). Image Enhancement is simple and most appealing area among all the digital image processing techniques. The main purpose of image enhancement is to bring out detail that is hidden in an image or to increase contrast in a low contrast image [1]. The color restoration functions of some real color image enhancement algorithms are greatly at random and not proved , and the real color images enhanced which are based on illumination-reflectance model have the loss of details and the `halos', we proposed a new algorithm to overcome these disadvantages. Firstly, we transform the real color image from RGB space to HSI space which is approximately orthonormal system. Secondly, the illumination and the reflectance of value are separated by homomorphic filtering based on illumination-reflectance model. We have discovered that the high dynamic range of image including high bright lights is mainly caused by the reflectance. Thirdly, the details of reflectance are preserved by wavelet transform. Fourthly, the dynamic range of reflectance is compressed by Butterworth filtering. Lastly, the energy of the saturation of real color image in HSI space is attenuated according to the spectral sensitivity of most human vision.
International Nuclear Information System (INIS)
Vanin, V.R.
1990-01-01
The multidetector systems for high resolution gamma spectroscopy are presented. The observable parameters for identifying nuclides produced simultaneously in the reaction are analysed discussing the efficiency of filter systems. (M.C.K.)
Maximal Bell's inequality violation for non-maximal entanglement
International Nuclear Information System (INIS)
Kobayashi, M.; Khanna, F.; Mann, A.; Revzen, M.; Santana, A.
2004-01-01
Bell's inequality violation (BIQV) for correlations of polarization is studied for a product state of two two-mode squeezed vacuum (TMSV) states. The violation allowed is shown to attain its maximal limit for all values of the squeezing parameter, ζ. We show via an explicit example that a state whose entanglement is not maximal allow maximal BIQV. The Wigner function of the state is non-negative and the average value of either polarization is nil
Orthonormal Wavelet Bases for Quantum Molecular Dynamics
International Nuclear Information System (INIS)
Tymczak, C.; Wang, X.
1997-01-01
We report on the use of compactly supported, orthonormal wavelet bases for quantum molecular-dynamics (Car-Parrinello) algorithms. A wavelet selection scheme is developed and tested for prototypical problems, such as the three-dimensional harmonic oscillator, the hydrogen atom, and the local density approximation to atomic and molecular systems. Our method shows systematic convergence with increased grid size, along with improvement on compression rates, thereby yielding an optimal grid for self-consistent electronic structure calculations. copyright 1997 The American Physical Society
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.
Processors for wavelet analysis and synthesis: NIFS and TI-C80 MVP
Brooks, Geoffrey W.
1996-03-01
Two processors are considered for image quadrature mirror filtering (QMF). The neuromorphic infrared focal-plane sensor (NIFS) is an existing prototype analog processor offering high speed spatio-temporal Gaussian filtering, which could be used for the QMF low- pass function, and difference of Gaussian filtering, which could be used for the QMF high- pass function. Although not designed specifically for wavelet analysis, the biologically- inspired system accomplishes the most computationally intensive part of QMF processing. The Texas Instruments (TI) TMS320C80 Multimedia Video Processor (MVP) is a 32-bit RISC master processor with four advanced digital signal processors (DSPs) on a single chip. Algorithm partitioning, memory management and other issues are considered for optimal performance. This paper presents these considerations with simulated results leading to processor implementation of high-speed QMF analysis and synthesis.
A study of biorthogonal multiple vector-valued wavelets
International Nuclear Information System (INIS)
Han Jincang; Cheng Zhengxing; Chen Qingjiang
2009-01-01
The notion of vector-valued multiresolution analysis is introduced and the concept of biorthogonal multiple vector-valued wavelets which are wavelets for vector fields, is introduced. It is proved that, like in the scalar and multiwavelet case, the existence of a pair of biorthogonal multiple vector-valued scaling functions guarantees the existence of a pair of biorthogonal multiple vector-valued wavelet functions. An algorithm for constructing a class of compactly supported biorthogonal multiple vector-valued wavelets is presented. Their properties are investigated by means of operator theory and algebra theory and time-frequency analysis method. Several biorthogonality formulas regarding these wavelet packets are obtained.
Solution of wave-like equation based on Haar wavelet
Directory of Open Access Journals (Sweden)
Naresh Berwal
2012-11-01
Full Text Available Wavelet transform and wavelet analysis are powerful mathematical tools for many problems. Wavelet also can be applied in numerical analysis. In this paper, we apply Haar wavelet method to solve wave-like equation with initial and boundary conditions known. The fundamental idea of Haar wavelet method is to convert the differential equations into a group of algebraic equations, which involves a finite number or variables. The results and graph show that the proposed way is quite reasonable when compared to exact solution.
Arvind, Pratul
2012-11-01
The ability to identify and classify all ten types of faults in a distribution system is an important task for protection engineers. Unlike transmission system, distribution systems have a complex configuration and are subjected to frequent faults. In the present work, an algorithm has been developed for identifying all ten types of faults in a distribution system by collecting current samples at the substation end. The samples are subjected to wavelet packet transform and artificial neural network in order to yield better classification results. A comparison of results between wavelet transform and wavelet packet transform is also presented thereby justifying the feature extracted from wavelet packet transform yields promising results. It should also be noted that current samples are collected after simulating a 25kv distribution system in PSCAD software.
Flexible time-varying filter banks
Tuncer, Temel E.; Nguyen, Truong Q.
1993-09-01
Linear phase maximally flat FIR Butterworth filter approximations are discussed and a new filter design method is introduced. This variable cutoff filter design method uses the cosine modulated versions of a prototype filter. The design procedure is simple and different variants of this procedure can be used to obtain close to optimum linear phase filters. Using this method, flexible time-varying filter banks with good reconstruction error are introduced. These types of oversampled filter banks have small magnitude error which can be easily controlled by the appropriate choice of modulation frequency. This error can be further decreased by magnitude equalization without increasing the computational complexity considerably. Two dimensional design examples are also given.
Maximally Symmetric Composite Higgs Models.
Csáki, Csaba; Ma, Teng; Shu, Jing
2017-09-29
Maximal symmetry is a novel tool for composite pseudo Goldstone boson Higgs models: it is a remnant of an enhanced global symmetry of the composite fermion sector involving a twisting with the Higgs field. Maximal symmetry has far-reaching consequences: it ensures that the Higgs potential is finite and fully calculable, and also minimizes the tuning. We present a detailed analysis of the maximally symmetric SO(5)/SO(4) model and comment on its observational consequences.
Quantum dynamics and electronic spectroscopy within the framework of wavelets
International Nuclear Information System (INIS)
Toutounji, Mohamad
2013-01-01
This paper serves as a first-time report on formulating important aspects of electronic spectroscopy and quantum dynamics in condensed harmonic systems using the framework of wavelets, and a stepping stone to our future work on developing anharmonic wavelets. The Morlet wavelet is taken to be the mother wavelet for the initial state of the system of interest. This work reports daughter wavelets that may be used to study spectroscopy and dynamics of harmonic systems. These wavelets are shown to arise naturally upon optical electronic transition of the system of interest. Natural birth of basis (daughter) wavelets emerging on exciting an electronic two-level system coupled, both linearly and quadratically, to harmonic phonons is discussed. It is shown that this takes place through using the unitary dilation and translation operators, which happen to be part of the time evolution operator of the final electronic state. The corresponding optical autocorrelation function and linear absorption spectra are calculated to test the applicability and correctness of the herein results. The link between basis wavelets and the Liouville space generating function is established. An anharmonic mother wavelet is also proposed in the case of anharmonic electron–phonon coupling. A brief description of deriving anharmonic wavelets and the corresponding anharmonic Liouville space generating function is explored. In conclusion, a mother wavelet (be it harmonic or anharmonic) which accounts for Duschinsky mixing is suggested. (paper)
Principles of maximally classical and maximally realistic quantum ...
Indian Academy of Sciences (India)
Principles of maximally classical and maximally realistic quantum mechanics. S M ROY. Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400 005, India. Abstract. Recently Auberson, Mahoux, Roy and Singh have proved a long standing conjecture of Roy and Singh: In 2N-dimensional phase space, ...
Information retrieval system utilizing wavelet transform
Brewster, Mary E.; Miller, Nancy E.
2000-01-01
A method for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.
monthly energy consumption forecasting using wavelet analysis
African Journals Online (AJOL)
User
ABSTRACT. Monthly energy forecasts help heavy consumers of electric power to prepare adequate budget to pay their electricity bills and also draw the attention of management and stakeholders to electric- ity consumption levels so that energy efficiency measures are put in place to reduce cost. In this paper, a wavelet ...
Characterization and Simulation of Gunfire with Wavelets
Directory of Open Access Journals (Sweden)
David O. Smallwood
1999-01-01
Full Text Available Gunfire is used as an example to show how the wavelet transform can be used to characterize and simulate nonstationary random events when an ensemble of events is available. The structural response to nearby firing of a high-firing rate gun has been characterized in several ways as a nonstationary random process. The current paper will explore a method to describe the nonstationary random process using a wavelet transform. The gunfire record is broken up into a sequence of transient waveforms each representing the response to the firing of a single round. A wavelet transform is performed on each of these records. The gunfire is simulated by generating realizations of records of a single-round firing by computing an inverse wavelet transform from Gaussian random coefficients with the same mean and standard deviation as those estimated from the previously analyzed gunfire record. The individual records are assembled into a realization of many rounds firing. A second-order correction of the probability density function is accomplished with a zero memory nonlinear function. The method is straightforward, easy to implement, and produces a simulated record much like the measured gunfire record.
Multiscale wavelet representations for mammographic feature analysis
Laine, Andrew F.; Song, Shuwu
1992-12-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet coefficients, enhanced by linear, exponential and constant weight functions localized in scale space. By improving the visualization of breast pathology we can improve the changes of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).
Wavelet based multicarrier code division multiple access ...
African Journals Online (AJOL)
This paper presents the study on Wavelet transform based Multicarrier Code Division Multiple Access (MC-CDMA) system for a downlink wireless channel. The performance of the system is studied for Additive White Gaussian Noise Channel (AWGN) and slowly varying multipath channels. The bit error rate (BER) versus ...
Late washing filter cleaning cycle demonstration
International Nuclear Information System (INIS)
Meyer, M.L.; McCabe, D.J.
1992-01-01
The DWPF Late Washing Facility will filter cesium and potassium tetraphenyl borate (TPB) solids using a Mott sintered metal filter, identical to the filter now used in the In-tank Precipitation Facility. The purpose of the late wash step is primarily to remove the nitrite salts from the slurry prior to delivery to DWPF. Periodic chemical cleaning of the filter will be required, presumably after each batch although the actual required frequency could not be determined on the lab-scale. Minimization of chemical cleaning solution volumes is key to maximizing the attainment of the Late Wash facility. This report summarizes work completed in experiments designed to identify minimum cleaning solution requirements
In Situ Cleanable Alternative HEPA Filter Media
International Nuclear Information System (INIS)
Adamson, D. J.; Terry, M. T.
2002-01-01
The Westinghouse Savannah River Company, located at the Savannah River Site in Aiken, South Carolina, is currently testing two types of filter media for possible deployment as in situ regenerable/cleanable High Efficiency Particulate Air (HEPA) filters. The filters are being investigated to replace conventional, disposable, glass-fiber, HEPA filters that require frequent removal, replacement, and disposal. This is not only costly and subjects site personnel to radiation exposure, but adds to the ever-growing waste disposal problem. The types of filter media being tested, as part of a National Energy Technology Laboratory procurement, are sintered nickel metal and ceramic monolith membrane. These media were subjected to a hostile environment to simulate conditions that challenge the high-level waste tank ventilation systems. The environment promoted rapid filter plugging to maximize the number of filter loading/cleaning cycles that would occur in a specified period of time. The filters were challenged using nonradioactive simulated high-level waste materials and atmospheric dust; materials that cause filter pluggage in the field. The filters are cleaned in situ using an aqueous solution. The study found that both filter media were insensitive to high humidity or moisture conditions and were easily cleaned in situ. The filters regenerated to approximately clean filter status even after numerous plugging and in situ cleaning cycles. Air Techniques International is conducting particle retention testing on the filter media at the Oak Ridge Filter Test Facility. The filters are challenged using 0.3-mm di-octyl phthalate particles. Both the ceramic and sintered media have a particle retention efficiency > 99.97%. The sintered metal and ceramic filters not only can be cleaned in situ, but also hold great potential as a long life alternative to conventional HEPA filters. The Defense Nuclear Facility Safety Board Technical Report, ''HEPA Filters Used in the Department of
Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin
2015-01-01
Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
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.
Resonance detection of EEG signals using two-layer wavelet analysis
International Nuclear Information System (INIS)
Abdallah, H. M; Odeh, F.S.
2000-01-01
This paper presents the hybrid quadrature mirror filter (HQMF) algorithm applied to the electroencephalogram (EEG) signal during mental activity. The information contents of this signal, i.e., its medical diagnosis, lie in its power spectral density (PSD). The HQMF algorithm is a modified technique that is based on the shape and the details of the signal. If applied efficiently, the HQMF algorithm will produce much better results than conventional wavelet methods in detecting (diagnosing) the information of the EEG signal from its PSD. This technique is applicable not only to EEG signals, but is highly recommended to compression analysis and de noising techniques. (authors). 16 refs., 9 figs
Directory of Open Access Journals (Sweden)
Budi Setiyono
2012-02-01
Full Text Available Biometric represents the human identification method development using natural characteristic of humanbeing as its bases. Every iris has the detail and unique texture, even differ between right and left eye.Theeye iris identification process in this research are data acquisition, early processing, feature exctractionand classification. Algorithm used for classification of texture slice the eye is Gabor wavelet filtering, andclassification process of slice the eye texture will be used by a Artificial Neural Network LVQ. Recognitionthe value of feature vektor in each iris obtained from to the number of right recognition value or thepercentage of right one. The best recognition percentage is 87,5 %.
A new approach to pre-processing digital image for wavelet-based watermark
Agreste, Santa; Andaloro, Guido
2008-11-01
The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.
International Nuclear Information System (INIS)
Delakis, Ioannis; Hammad, Omer; Kitney, Richard I
2007-01-01
Wavelet-based de-noising has been shown to improve image signal-to-noise ratio in magnetic resonance imaging (MRI) while maintaining spatial resolution. Wavelet-based de-noising techniques typically implemented in MRI require that noise displays uniform spatial distribution. However, images acquired with parallel MRI have spatially varying noise levels. In this work, a new algorithm for filtering images with parallel MRI is presented. The proposed algorithm extracts the edges from the original image and then generates a noise map from the wavelet coefficients at finer scales. The noise map is zeroed at locations where edges have been detected and directional analysis is also used to calculate noise in regions of low-contrast edges that may not have been detected. The new methodology was applied on phantom and brain images and compared with other applicable de-noising techniques. The performance of the proposed algorithm was shown to be comparable with other techniques in central areas of the images, where noise levels are high. In addition, finer details and edges were maintained in peripheral areas, where noise levels are low. The proposed methodology is fully automated and can be applied on final reconstructed images without requiring sensitivity profiles or noise matrices of the receiver coils, therefore making it suitable for implementation in a clinical MRI setting
A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.
Gregoire, John M; Dale, Darren; van Dover, R Bruce
2011-01-01
Peak detection is ubiquitous in the analysis of spectral data. While many noise-filtering algorithms and peak identification algorithms have been developed, recent work [P. Du, W. Kibbe, and S. Lin, Bioinformatics 22, 2059 (2006); A. Wee, D. Grayden, Y. Zhu, K. Petkovic-Duran, and D. Smith, Electrophoresis 29, 4215 (2008)] has demonstrated that both of these tasks are efficiently performed through analysis of the wavelet transform of the data. In this paper, we present a wavelet-based peak detection algorithm with user-defined parameters that can be readily applied to the application of any spectral data. Particular attention is given to the algorithm's resolution of overlapping peaks. The algorithm is implemented for the analysis of powder diffraction data, and successful detection of Bragg peaks is demonstrated for both low signal-to-noise data from theta-theta diffraction of nanoparticles and combinatorial x-ray diffraction data from a composition spread thin film. These datasets have different types of background signals which are effectively removed in the wavelet-based method, and the results demonstrate that the algorithm provides a robust method for automated peak detection.
Directory of Open Access Journals (Sweden)
Karl Friston
2010-01-01
Full Text Available We describe a Bayesian filtering scheme for nonlinear state-space models in continuous time. This scheme is called Generalised Filtering and furnishes posterior (conditional densities on hidden states and unknown parameters generating observed data. Crucially, the scheme operates online, assimilating data to optimize the conditional density on time-varying states and time-invariant parameters. In contrast to Kalman and Particle smoothing, Generalised Filtering does not require a backwards pass. In contrast to variational schemes, it does not assume conditional independence between the states and parameters. Generalised Filtering optimises the conditional density with respect to a free-energy bound on the model's log-evidence. This optimisation uses the generalised motion of hidden states and parameters, under the prior assumption that the motion of the parameters is small. We describe the scheme, present comparative evaluations with a fixed-form variational version, and conclude with an illustrative application to a nonlinear state-space model of brain imaging time-series.
Directory of Open Access Journals (Sweden)
Audrey Barbakoff
2011-03-01
Full Text Available In the Library with the Lead Pipe welcomes Audrey Barbakoff, a librarian at the Milwaukee Public Library, and Ahniwa Ferrari, Virtual Experience Manager at the Pierce County Library System in Washington, for a point-counterpoint piece on filtering in libraries. The opinions expressed here are those of the authors, and are not endorsed by their employers. [...
Directory of Open Access Journals (Sweden)
A. H. Ahrari
2017-09-01
Full Text Available Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar and different decomposition filters (mean.linear,ma,min and rand for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.
Weighted Sum-Rate Maximization Using Weighted MMSE for MIMO-BC Beamforming Design
DEFF Research Database (Denmark)
Christensen, Søren; De Carvalho, Elisabeth; Agarwal, Rajiv
2009-01-01
This paper studies linear transmit filter design for weighted sum-rate (WSR) maximization in the multiple input multiple output broadcast channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent ...
Distributed-Memory Fast Maximal Independent Set
Energy Technology Data Exchange (ETDEWEB)
Kanewala Appuhamilage, Thejaka Amila J.; Zalewski, Marcin J.; Lumsdaine, Andrew
2017-09-13
The Maximal Independent Set (MIS) graph problem arises in many applications such as computer vision, information theory, molecular biology, and process scheduling. The growing scale of MIS problems suggests the use of distributed-memory hardware as a cost-effective approach to providing necessary compute and memory resources. Luby proposed four randomized algorithms to solve the MIS problem. All those algorithms are designed focusing on shared-memory machines and are analyzed using the PRAM model. These algorithms do not have direct efficient distributed-memory implementations. In this paper, we extend two of Luby’s seminal MIS algorithms, “Luby(A)” and “Luby(B),” to distributed-memory execution, and we evaluate their performance. We compare our results with the “Filtered MIS” implementation in the Combinatorial BLAS library for two types of synthetic graph inputs.
Maximizing and customer loyalty: Are maximizers less loyal?
Directory of Open Access Journals (Sweden)
Linda Lai
2011-06-01
Full Text Available Despite their efforts to choose the best of all available solutions, maximizers seem to be more inclined than satisficers to regret their choices and to experience post-decisional dissonance. Maximizers may therefore be expected to change their decisions more frequently and hence exhibit lower customer loyalty to providers of products and services compared to satisficers. Findings from the study reported here (N = 1978 support this prediction. Maximizers reported significantly higher intentions to switch to another service provider (television provider than satisficers. Maximizers' intentions to switch appear to be intensified and mediated by higher proneness to regret, increased desire to discuss relevant choices with others, higher levels of perceived knowledge of alternatives, and higher ego involvement in the end product, compared to satisficers. Opportunities for future research are suggested.
Implications of maximal Jarlskog invariant and maximal CP violation
International Nuclear Information System (INIS)
Rodriguez-Jauregui, E.; Universidad Nacional Autonoma de Mexico
2001-04-01
We argue here why CP violating phase Φ in the quark mixing matrix is maximal, that is, Φ=90 . In the Standard Model CP violation is related to the Jarlskog invariant J, which can be obtained from non commuting Hermitian mass matrices. In this article we derive the conditions to have Hermitian mass matrices which give maximal Jarlskog invariant J and maximal CP violating phase Φ. We find that all squared moduli of the quark mixing elements have a singular point when the CP violation phase Φ takes the value Φ=90 . This special feature of the Jarlskog invariant J and the quark mixing matrix is a clear and precise indication that CP violating Phase Φ is maximal in order to let nature treat democratically all of the quark mixing matrix moduli. (orig.)
Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W
2016-09-01
Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69.
Phenomenology of maximal and near-maximal lepton mixing
International Nuclear Information System (INIS)
Gonzalez-Garcia, M. C.; Pena-Garay, Carlos; Nir, Yosef; Smirnov, Alexei Yu.
2001-01-01
The possible existence of maximal or near-maximal lepton mixing constitutes an intriguing challenge for fundamental theories of flavor. We study the phenomenological consequences of maximal and near-maximal mixing of the electron neutrino with other (x=tau and/or muon) neutrinos. We describe the deviations from maximal mixing in terms of a parameter ε(equivalent to)1-2sin 2 θ ex and quantify the present experimental status for |ε| e mixing comes from solar neutrino experiments. We find that the global analysis of solar neutrino data allows maximal mixing with confidence level better than 99% for 10 -8 eV 2 ∼ 2 ∼ -7 eV 2 . In the mass ranges Δm 2 ∼>1.5x10 -5 eV 2 and 4x10 -10 eV 2 ∼ 2 ∼ -7 eV 2 the full interval |ε| e mixing in atmospheric neutrinos, supernova neutrinos, and neutrinoless double beta decay
Maximal quantum Fisher information matrix
International Nuclear Information System (INIS)
Chen, Yu; Yuan, Haidong
2017-01-01
We study the existence of the maximal quantum Fisher information matrix in the multi-parameter quantum estimation, which bounds the ultimate precision limit. We show that when the maximal quantum Fisher information matrix exists, it can be directly obtained from the underlying dynamics. Examples are then provided to demonstrate the usefulness of the maximal quantum Fisher information matrix by deriving various trade-off relations in multi-parameter quantum estimation and obtaining the bounds for the scalings of the precision limit. (paper)
Praveen, Angam; Vijayarekha, K; Abraham, Saju T; Venkatraman, B
2013-09-01
Time of flight diffraction (TOFD) technique is a well-developed ultrasonic non-destructive testing (NDT) method and has been applied successfully for accurate sizing of defects in metallic materials. This technique was developed in early 1970s as a means for accurate sizing and positioning of cracks in nuclear components became very popular in the late 1990s and is today being widely used in various industries for weld inspection. One of the main advantages of TOFD is that, apart from fast technique, it provides higher probability of detection for linear defects. Since TOFD is based on diffraction of sound waves from the extremities of the defect compared to reflection from planar faces as in pulse echo and phased array, the resultant signal would be quite weak and signal to noise ratio (SNR) low. In many cases the defect signal is submerged in this noise making it difficult for detection, positioning and sizing. Several signal processing methods such as digital filtering, Split Spectrum Processing (SSP), Hilbert Transform and Correlation techniques have been developed in order to suppress unwanted noise and enhance the quality of the defect signal which can thus be used for characterization of defects and the material. Wavelet Transform based thresholding techniques have been applied largely for de-noising of ultrasonic signals. However in this paper, higher order wavelets are used for analyzing the de-noising performance for TOFD signals obtained from Austenitic Stainless Steel welds. It is observed that higher order wavelets give greater SNR improvement compared to the lower order wavelets. Copyright © 2013 Elsevier B.V. All rights reserved.
Time-Frequency-Wavenumber Analysis of Surface Waves Using the Continuous Wavelet Transform
Poggi, V.; Fäh, D.; Giardini, D.
2013-03-01
A modified approach to surface wave dispersion analysis using active sources is proposed. The method is based on continuous recordings, and uses the continuous wavelet transform to analyze the phase velocity dispersion of surface waves. This gives the possibility to accurately localize the phase information in time, and to isolate the most significant contribution of the surface waves. To extract the dispersion information, then, a hybrid technique is applied to the narrowband filtered seismic recordings. The technique combines the flexibility of the slant stack method in identifying waves that propagate in space and time, with the resolution of f- k approaches. This is particularly beneficial for higher mode identification in cases of high noise levels. To process the continuous wavelet transform, a new mother wavelet is presented and compared to the classical and widely used Morlet type. The proposed wavelet is obtained from a raised-cosine envelope function (Hanning type). The proposed approach is particularly suitable when using continuous recordings (e.g., from seismological-like equipment) since it does not require any hardware-based source triggering. This can be subsequently done with the proposed method. Estimation of the surface wave phase delay is performed in the frequency domain by means of a covariance matrix averaging procedure over successive wave field excitations. Thus, no record stacking is necessary in the time domain and a large number of consecutive shots can be used. This leads to a certain simplification of the field procedures. To demonstrate the effectiveness of the method, we tested it on synthetics as well on real field data. For the real case we also combine dispersion curves from ambient vibrations and active measurements.
Pigmented skin lesion detection using random forest and wavelet-based texture
Hu, Ping; Yang, Tie-jun
2016-10-01
The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.
Loris, Ignace; Simons, Frederik J.; Daubechies, Ingrid; Nolet, Guust; Fornasier, Massimo; Vetter, Philip; Judd, Stephen; Voronin, Sergey; Vonesch, Cédric; Charléty, Jean
2010-05-01
Global seismic wavespeed models are routinely parameterized in terms of spherical harmonics, networks of tetrahedral nodes, rectangular voxels, or spherical splines. Up to now, Earth model parametrizations by wavelets on the three-dimensional ball remain uncommon. Here we propose such a procedure with the following three goals in mind: (1) The multiresolution character of a wavelet basis allows for the models to be represented with an effective spatial resolution that varies as a function of position within the Earth. (2) This property can be used to great advantage in the regularization of seismic inversion schemes by seeking the most sparse solution vector, in wavelet space, through iterative minimization of a combination of the ℓ2 (to fit the data) and ℓ1 norms (to promote sparsity in wavelet space). (3) With the continuing increase in high-quality seismic data, our focus is also on numerical efficiency and the ability to use parallel computing in reconstructing the model. In this presentation we propose a new wavelet basis to take advantage of these three properties. To form the numerical grid we begin with a surface tesselation known as the 'cubed sphere', a construction popular in fluid dynamics and computational seismology, coupled with an semi-regular radial subdivison that honors the major seismic discontinuities between the core-mantle boundary and the surface. This mapping first divides the volume of the mantle into six portions. In each 'chunk' two angular and one radial variable are used for parametrization. In the new variables standard 'cartesian' algorithms can more easily be used to perform the wavelet transform (or other common transforms). Edges between chunks are handled by special boundary filters. We highlight the benefits of this construction and use it to analyze the information present in several published seismic compressional-wavespeed models of the mantle, paying special attention to the statistics of wavelet and scaling coefficients
From cardinal spline wavelet bases to highly coherent dictionaries
International Nuclear Information System (INIS)
Andrle, Miroslav; Rebollo-Neira, Laura
2008-01-01
Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation. (fast track communication)
Complex noise suppression using a sparse representation and 3D filtering of images
Kravchenko, V. F.; Ponomaryov, V. I.; Pustovoit, V. I.; Palacios-Enriquez, A.
2017-08-01
A novel method for the filtering of images corrupted by complex noise composed of randomly distributed impulses and additive Gaussian noise has been substantiated for the first time. The method consists of three main stages: the detection and filtering of pixels corrupted by impulsive noise, the subsequent image processing to suppress the additive noise based on 3D filtering and a sparse representation of signals in a basis of wavelets, and the concluding image processing procedure to clean the final image of the errors emerged at the previous stages. A physical interpretation of the filtering method under complex noise conditions is given. A filtering block diagram has been developed in accordance with the novel approach. Simulations of the novel image filtering method have shown an advantage of the proposed filtering scheme in terms of generally recognized criteria, such as the structural similarity index measure and the peak signal-to-noise ratio, and when visually comparing the filtered images.
Joint multifractal analysis based on wavelet leaders
Jiang, Zhi-Qiang; Yang, Yan-Hong; Wang, Gang-Jin; Zhou, Wei-Xing
2017-12-01
Mutually interacting components form complex systems and these components usually have long-range cross-correlated outputs. Using wavelet leaders, we propose a method for characterizing the joint multifractal nature of these long-range cross correlations; we call this method joint multifractal analysis based on wavelet leaders (MF-X-WL). We test the validity of the MF-X-WL method by performing extensive numerical experiments on dual binomial measures with multifractal cross correlations and bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. Both experiments indicate that MF-X-WL is capable of detecting cross correlations in synthetic data with acceptable estimating errors. We also apply the MF-X-WL method to pairs of series from financial markets (returns and volatilities) and online worlds (online numbers of different genders and different societies) and determine intriguing joint multifractal behavior.
Wavelet neural network load frequency controller
International Nuclear Information System (INIS)
Hemeida, Ashraf Mohamed
2005-01-01
This paper presents the feasibility of applying a wavelet neural network (WNN) approach for the load frequency controller (LFC) to damp the frequency oscillations of two area power systems due to load disturbances. The present intelligent control system trained the wavelet neural network (WNN) controller on line with adaptive learning rates, which are derived in the sense of a discrete type Lyapunov stability theorem. The present WNN controller is designed individually for each area. The proposed technique is applied successfully for a wide range of operating conditions. The time simulation results indicate its superiority and effectiveness over the conventional approach. The effects of consideration of the governor dead zone on the system performance are studied using the proposed controller and the conventional one
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.
Wavelet analysis of the impedance cardiogram waveforms
International Nuclear Information System (INIS)
Podtaev, S; Stepanov, R; Dumler, A; Chugainov, S; Tziberkin, K
2012-01-01
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.
Gestures recognition based on wavelet and LLE
International Nuclear Information System (INIS)
Ai, Qingsong; Liu, Quan; Lu, Ying; Yuan, Tingting
2013-01-01
Wavelet analysis is a time–frequency, non-stationary method while the largest Lyapunov exponent (LLE) is used to judge the non-linear characteristic of systems. Because surface electromyography signal (SEMGS) is a complex signal that is characterized by non-stationary and non-linear properties. This paper combines wavelet coefficient and LLE together as the new feature of SEMGS. The proposed method not only reflects the non-stationary and non-linear characteristics of SEMGS, but also is suitable for its classification. Then, the BP (back propagation) neural network is employed to implement the identification of six gestures (fist clench, fist extension, wrist extension, wrist flexion, radial deviation, ulnar deviation). The experimental results indicate that based on the proposed method, the identification of these six gestures can reach an average rate of 97.71 %.
Wavelets and their applications past and future
Coifman, Ronald R.
2009-04-01
As this is a conference on mathematical tools for defense, I would like to dedicate this talk to the memory of Louis Auslander, who through his insights and visionary leadership, brought powerful new mathematics into DARPA, he has provided the main impetus to the development and insertion of wavelet based processing in defense. My goal here is to describe the evolution of a stream of ideas in Harmonic Analysis, ideas which in the past have been mostly applied for the analysis and extraction of information from physical data, and which now are increasingly applied to organize and extract information and knowledge from any set of digital documents, from text to music to questionnaires. This form of signal processing on digital data, is part of the future of wavelet analysis.
Lange, L. H.
1974-01-01
Five different methods for determining the maximizing condition for x(a - x) are presented. Included is the ancient Greek version and a method attributed to Fermat. None of the proofs use calculus. (LS)
Finding Maximal Quasiperiodicities in Strings
DEFF Research Database (Denmark)
Brodal, Gerth Stølting; Pedersen, Christian N. S.
2000-01-01
of length n in time O(n log n) and space O(n). Our algorithm uses the suffix tree as the fundamental data structure combined with efficient methods for merging and performing multiple searches in search trees. Besides finding all maximal quasiperiodic substrings, our algorithm also marks the nodes......Apostolico and Ehrenfeucht defined the notion of a maximal quasiperiodic substring and gave an algorithm that finds all maximal quasiperiodic substrings in a string of length n in time O(n log2 n). In this paper we give an algorithm that finds all maximal quasiperiodic substrings in a string...... in the suffix tree that have a superprimitive path-label....
Salvio, Alberto; Strumia, Alessandro; Urbano, Alfredo
2016-01-01
Motivated by the 750 GeV diphoton excess found at LHC, we compute the maximal width into $\\gamma\\gamma$ that a neutral scalar can acquire through a loop of charged fermions or scalars as function of the maximal scale at which the theory holds, taking into account vacuum (meta)stability bounds. We show how an extra gauge symmetry can qualitatively weaken such bounds, and explore collider probes and connections with Dark Matter.
Transformer Protection Using the Wavelet Transform
ÖZGÖNENEL, Okan; ÖNBİLGİN, Güven; KOCAMAN, Çağrı
2014-01-01
This paper introduces a novel approach for power transformer protection algorithm. Power system signals such as current and voltage have traditionally been analysed by the Fast Fourier Transform. This paper aims to prove that the Wavelet Transform is a reliable and computationally efficient tool for distinguishing between the inrush currents and fault currents. The simulated results presented clearly show that the proposed technique for power transformer protection facilitates the a...
Wavelet representation of the nuclear dynamics
Energy Technology Data Exchange (ETDEWEB)
Jouault, B.; Sebille, F.; Mota, V. de la
1997-12-31
The study of transport phenomena in nuclear matter is addressed in a new approach named DYWAN, based on the projection methods of statistical physics and on the mathematical theory of wavelets. Strongly compressed representations of the nuclear systems are obtained with an accurate description of the wave functions and of their antisymmetrization. The results of the approach are illustrated for the ground state description as well as for the dissipative dynamics of nuclei at intermediate energies. (K.A.). 52 refs.
Wavelet Decomposition of the Financial Market
Czech Academy of Sciences Publication Activity Database
Vošvrda, Miloslav; Vácha, Lukáš
2007-01-01
Roč. 16, č. 1 (2007), s. 38-54 ISSN 1210-0455 R&D Projects: GA ČR GA402/04/1026; GA ČR(CZ) GA402/06/1417 Grant - others:GA UK(CZ) 454/2004/A-EK FSV Institutional research plan: CEZ:AV0Z10750506 Keywords : agents' trading strategies * heterogeneous agents model with stochastic memory * worst out algorithm * wavelet Subject RIV: AH - Economics
Wavelet representation of the nuclear dynamics
International Nuclear Information System (INIS)
Jouault, B.; Sebille, F.; Mota, V. de la.
1997-01-01
The study of transport phenomena in nuclear matter is addressed in a new approach named DYWAN, based on the projection methods of statistical physics and on the mathematical theory of wavelets. Strongly compressed representations of the nuclear systems are obtained with an accurate description of the wave functions and of their antisymmetrization. The results of the approach are illustrated for the ground state description as well as for the dissipative dynamics of nuclei at intermediate energies. (K.A.)
Multiscale peak detection in wavelet space.
Zhang, Zhi-Min; Tong, Xia; Peng, Ying; Ma, Pan; Zhang, Ming-Jin; Lu, Hong-Mei; Chen, Xiao-Qing; Liang, Yi-Zeng
2015-12-07
Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .
On transforms between Gabor frames and wavelet frames
DEFF Research Database (Denmark)
Christensen, Ole; Goh, Say Song
2013-01-01
We describe a procedure that enables us to construct dual pairs of wavelet frames from certain dual pairs of Gabor frames. Applying the construction to Gabor frames generated by appropriate exponential Bsplines gives wavelet frames generated by functions whose Fourier transforms are compactly...... supported splines with geometrically distributed knot sequences. There is also a reverse transform, which yields pairs of dual Gabor frames when applied to certain wavelet frames....
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
Pedestrian detection based on redundant wavelet transform
Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun
2016-10-01
Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.
Fringe pattern information retrieval using wavelets
Sciammarella, Cesar A.; Patimo, Caterina; Manicone, Pasquale D.; Lamberti, Luciano
2005-08-01
Two-dimensional phase modulation is currently the basic model used in the interpretation of fringe patterns that contain displacement information, moire, holographic interferometry, speckle techniques. Another way to look to these two-dimensional signals is to consider them as frequency modulated signals. This alternative interpretation has practical implications similar to those that exist in radio engineering for handling frequency modulated signals. Utilizing this model it is possible to obtain frequency information by using the energy approach introduced by Ville in 1944. A natural complementary tool of this process is the wavelet methodology. The use of wavelet makes it possible to obtain the local values of the frequency in a one or two dimensional domain without the need of previous phase retrieval and differentiation. Furthermore from the properties of wavelets it is also possible to obtain at the same time the phase of the signal with the advantage of a better noise removal capabilities and the possibility of developing simpler algorithms for phase unwrapping due to the availability of the derivative of the phase.
JPEG and wavelet compression of ophthalmic images
Eikelboom, Robert H.; Yogesan, Kanagasingam; Constable, Ian J.; Barry, Christopher J.
1999-05-01
This study was designed to determine the degree and methods of digital image compression to produce ophthalmic imags of sufficient quality for transmission and diagnosis. The photographs of 15 subjects, which inclined eyes with normal, subtle and distinct pathologies, were digitized to produce 1.54MB images and compressed to five different methods: (i) objectively by calculating the RMS error between the uncompressed and compressed images, (ii) semi-subjectively by assessing the visibility of blood vessels, and (iii) subjectively by asking a number of experienced observers to assess the images for quality and clinical interpretation. Results showed that as a function of compressed image size, wavelet compressed images produced less RMS error than JPEG compressed images. Blood vessel branching could be observed to a greater extent after Wavelet compression compared to JPEG compression produced better images then a JPEG compression for a given image size. Overall, it was shown that images had to be compressed to below 2.5 percent for JPEG and 1.7 percent for Wavelet compression before fine detail was lost, or when image quality was too poor to make a reliable diagnosis.
Rate-distortion analysis of directional wavelets.
Maleki, Arian; Rajaei, Boshra; Pourreza, Hamid Reza
2012-02-01
The inefficiency of separable wavelets in representing smooth edges has led to a great interest in the study of new 2-D transformations. The most popular criterion for analyzing these transformations is the approximation power. Transformations with near-optimal approximation power are useful in many applications such as denoising and enhancement. However, they are not necessarily good for compression. Therefore, most of the nearly optimal transformations such as curvelets and contourlets have not found any application in image compression yet. One of the most promising schemes for image compression is the elegant idea of directional wavelets (DIWs). While these algorithms outperform the state-of-the-art image coders in practice, our theoretical understanding of them is very limited. In this paper, we adopt the notion of rate-distortion and calculate the performance of the DIW on a class of edge-like images. Our theoretical analysis shows that if the edges are not "sharp," the DIW will compress them more efficiently than the separable wavelets. It also demonstrates the inefficiency of the quadtree partitioning that is often used with the DIW. To solve this issue, we propose a new partitioning scheme called megaquad partitioning. Our simulation results on real-world images confirm the benefits of the proposed partitioning algorithm, promised by our theoretical analysis. © 2011 IEEE
Forced Ignition Study Based On Wavelet Method
Martelli, E.; Valorani, M.; Paolucci, S.; Zikoski, Z.
2011-05-01
The control of ignition in a rocket engine is a critical problem for combustion chamber design. Therefore it is essential to fully understand the mechanism of ignition during its earliest stages. In this paper the characteristics of flame kernel formation and initial propagation in a hydrogen-argon-oxygen mixing layer are studied using 2D direct numerical simulations with detailed chemistry and transport properties. The flame kernel is initiated by adding an energy deposition source term in the energy equation. The effect of unsteady strain rate is studied by imposing a 2D turbulence velocity field, which is initialized by means of a synthetic field. An adaptive wavelet method, based on interpolating wavelets is used in this study to solve the compressible reactive Navier- Stokes equations. This method provides an alternative means to refine the computational grid points according to local demands of the physical solution. The present simulations show that in the very early instants the kernel perturbed by the turbulent field is characterized by an increased burning area and a slightly increased rad- ical formation. In addition, the calculations show that the wavelet technique yields a significant reduction in the number of degrees of freedom necessary to achieve a pre- scribed solution accuracy.
Comparison on Integer Wavelet Transforms in Spherical Wavelet Based Image Based Relighting
Institute of Scientific and Technical Information of China (English)
WANGZe; LEEYin; LEUNGChising; WONGTientsin; ZHUYisheng
2003-01-01
To provide a good quality rendering in the Image based relighting (IBL) system, tremendous reference images under various illumination conditions are needed. Therefore data compression is essential to enable interactive action. And the rendering speed is another crucial consideration for real applications. Based on Spherical wavelet transform (SWT), this paper presents a quick representation method with Integer wavelet transform (IWT) for the IBL system. It focuses on comparison on different IWTs with the Embedded zerotree wavelet (EZW) used in the IBL system. The whole compression procedure contains two major compression steps. Firstly, SWT is applied to consider the correlation among different reference images. Secondly, the SW transformed images are compressed with IWT based image compression approach. Two IWTs are used and good results are showed in the simulations.
Coresident sensor fusion and compression using the wavelet transform
Energy Technology Data Exchange (ETDEWEB)
Yocky, D.A.
1996-03-11
Imagery from coresident sensor platforms, such as unmanned aerial vehicles, can be combined using, multiresolution decomposition of the sensor images by means of the two-dimensional wavelet transform. The wavelet approach uses the combination of spatial/spectral information at multiple scales to create a fused image. This can be done in both an ad hoc or model-based approach. We compare results from commercial ``fusion`` software and the ad hoc, wavelet approach. Results show the wavelet approach outperforms the commercial algorithms and also supports efficient compression of the fused image.
EEG Signal Decomposition and Improved Spectral Analysis Using Wavelet Transform
National Research Council Canada - National Science Library
Bhatti, Muhammad
2001-01-01
EEG (Electroencephalograph), as a noninvasive testing method, plays a key role in the diagnosing diseases, and is useful for both physiological research and medical applications. Wavelet transform (WT...
A study of non-binary discontinuity wavelet
International Nuclear Information System (INIS)
Lin Hai; Liu Lianshou
2006-01-01
This paper gives a study of non-binary discontinuity wavelet, put forward the theory and method of constituting basic wavelet functions, and has constituted concretely a wavelet function using λ=3.4 as an example. It also conducts a theoretical inference on the decomposition algorithm and reconstruction algorithm of non-binary wavelet, and gives a concrete study of the change of matrix in connection with λ=3.4. In the end, it shows the future of application of the result to the study of high energy collision. (authors)
Wavelets for the stimulation of turbulent incompressible flows
International Nuclear Information System (INIS)
Deriaz, E.
2006-02-01
This PhD thesis presents original wavelet methods aimed at simulating incompressible fluids. In order to construct 2D and 3D wavelets designed for incompressible flows, we resume P-G Lemarie-Rieussets and K. Urbans works on divergence free wavelets. We show the existence of associated fast algorithms. In the following, we use divergence-free wavelet construction to define the Helmholtz decomposition of 2D and 3D vector fields. All these algorithms provide a new method for the numerical resolution of the incompressible Navier-Stokes equations. (author)
Wavelet-based moment invariants for pattern recognition
Chen, Guangyi; Xie, Wenfang
2011-07-01
Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.
Wavelet Approach to Data Analysis, Manipulation, Compression, and Communication
National Research Council Canada - National Science Library
Chui, Charles K
2007-01-01
...; secondly, based on minimum-energy criteria, new data processing tools, particularly variational algorithms and optimal wavelet thresholding methods, with applications to image restoration, were introduced...
Wavelet-Based Signal Processing of Electromagnetic Pulse Generated Waveforms
National Research Council Canada - National Science Library
Ardolino, Richard S
2007-01-01
This thesis investigated and compared alternative signal processing techniques that used wavelet-based methods instead of traditional frequency domain methods for processing measured electromagnetic pulse (EMP) waveforms...
Watermarking on 3D mesh based on spherical wavelet transform.
Jin, Jian-Qiu; Dai, Min-Ya; Bao, Hu-Jun; Peng, Qun-Sheng
2004-03-01
In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.
Wavelet-based verification of the quantitative precipitation forecast
Yano, Jun-Ichi; Jakubiak, Bogumil
2016-06-01
This paper explores the use of wavelets for spatial verification of quantitative precipitation forecasts (QPF), and especially the capacity of wavelets to provide both localization and scale information. Two 24-h forecast experiments using the two versions of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) on 22 August 2010 over Poland are used to illustrate the method. Strong spatial localizations and associated intermittency of the precipitation field make verification of QPF difficult using standard statistical methods. The wavelet becomes an attractive alternative, because it is specifically designed to extract spatially localized features. The wavelet modes are characterized by the two indices for the scale and the localization. Thus, these indices can simply be employed for characterizing the performance of QPF in scale and localization without any further elaboration or tunable parameters. Furthermore, spatially-localized features can be extracted in wavelet space in a relatively straightforward manner with only a weak dependence on a threshold. Such a feature may be considered an advantage of the wavelet-based method over more conventional "object" oriented verification methods, as the latter tend to represent strong threshold sensitivities. The present paper also points out limits of the so-called "scale separation" methods based on wavelets. Our study demonstrates how these wavelet-based QPF verifications can be performed straightforwardly. Possibilities for further developments of the wavelet-based methods, especially towards a goal of identifying a weak physical process contributing to forecast error, are also pointed out.
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.
Regularization by fractional filter methods and data smoothing
International Nuclear Information System (INIS)
Klann, E; Ramlau, R
2008-01-01
This paper is concerned with the regularization of linear ill-posed problems by a combination of data smoothing and fractional filter methods. For the data smoothing, a wavelet shrinkage denoising is applied to the noisy data with known error level δ. For the reconstruction, an approximation to the solution of the operator equation is computed from the data estimate by fractional filter methods. These fractional methods are based on the classical Tikhonov and Landweber method, but avoid, at least partially, the well-known drawback of oversmoothing. Convergence rates as well as numerical examples are presented
Bunnoon, Pituk; Chalermyanont, Kusumal; Limsakul, Chusak
2010-02-01
This paper proposed the discrete transform and neural network algorithms to obtain the monthly peak load demand in mid term load forecasting. The mother wavelet daubechies2 (db2) is employed to decomposed, high pass filter and low pass filter signals from the original signal before using feed forward back propagation neural network to determine the forecasting results. The historical data records in 1997-2007 of Electricity Generating Authority of Thailand (EGAT) is used as reference. In this study, historical information of peak load demand(MW), mean temperature(Tmean), consumer price index (CPI), and industrial index (economic:IDI) are used as feature inputs of the network. The experimental results show that the Mean Absolute Percentage Error (MAPE) is approximately 4.32%. This forecasting results can be used for fuel planning and unit commitment of the power system in the future.
Maximization of energy in the output of a linear system
International Nuclear Information System (INIS)
Dudley, D.G.
1976-01-01
A time-limited signal which, when passed through a linear system, maximizes the total output energy is considered. Previous work has shown that the solution is given by the eigenfunction associated with the maximum eigenvalue in a Hilbert-Schmidt integral equation. Analytical results are available for the case where the transfer function is a low-pass filter. This work is extended by obtaining a numerical solution to the integral equation which allows results for reasonably general transfer functions
Unsupervised Retinal Vessel Segmentation Using Combined Filters.
Directory of Open Access Journals (Sweden)
Wendeson S Oliveira
Full Text Available Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
Noise reduction by support vector regression with a Ricker wavelet kernel
International Nuclear Information System (INIS)
Deng, Xiaoying; Yang, Dinghui; Xie, Jing
2009-01-01
We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR
Noise reduction by support vector regression with a Ricker wavelet kernel
Deng, Xiaoying; Yang, Dinghui; Xie, Jing
2009-06-01
We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR.
Adib, Mani; Cretu, Edmond
2013-01-01
We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786
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.
Directory of Open Access Journals (Sweden)
Mani Adib
2013-01-01
Full Text Available We present a new method for removing artifacts in electroencephalography (EEG records during Galvanic Vestibular Stimulation (GVS. The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters.
International Nuclear Information System (INIS)
Khawaja, Z; Mazeran, P-E; Bigerelle, M; Guillemot, G; Mansori, M El
2011-01-01
This article presents a multi-scale theory based on wavelet decomposition to characterize the evolution of roughness in relation with a finishing process or an observed surface property. To verify this approach in production conditions, analyses were developed for the finishing process of the hardened steel by abrasive belts. These conditions are described by seven parameters considered in the Tagushi experimental design. The main objective of this work is to identify the most relevant roughness parameter and characteristic length allowing to assess the influence of finishing process, and to test the relevance of the measurement scale. Results show that wavelet approach allows finding this scale.
Directory of Open Access Journals (Sweden)
A. Garmroodi Asil
2017-09-01
To further reduce the sulfur dioxide emission of the entire refining process, two scenarios of acid gas or air preheats are investigated when either of them is used simultaneously with the third enrichment scheme. The maximum overall sulfur recovery efficiency and highest combustion chamber temperature is slightly higher for acid gas preheats but air preheat is more favorable because it is more benign. To the best of our knowledge, optimization of the entire GTU + enrichment section and SRU processes has not been addressed previously.
Energy Technology Data Exchange (ETDEWEB)
Yoshida, M; Komeda, I; Takizaki, K
1982-01-01
Bag filters are widely used throughout the cement industry for recovering raw materials and products and for improving the environment. Their general mechanism, performance and advantages are shown in a classification table, and there are comparisons and explanations. The outer and inner sectional construction of the Shinto ultra-jet collector for pulverized coal is illustrated and there are detailed descriptions of dust cloud prevention, of measures used against possible sources of ignition, of oxygen supply and of other topics. Finally, explanations are given of matters that require careful and comprehensive study when selecting equipment.
Hamming, Richard W
1997-01-01
Digital signals occur in an increasing number of applications: in telephone communications; in radio, television, and stereo sound systems; and in spacecraft transmissions, to name just a few. This introductory text examines digital filtering, the processes of smoothing, predicting, differentiating, integrating, and separating signals, as well as the removal of noise from a signal. The processes bear particular relevance to computer applications, one of the focuses of this book.Readers will find Hamming's analysis accessible and engaging, in recognition of the fact that many people with the s
Wavelet based free-form deformations for nonrigid registration
Sun, Wei; Niessen, Wiro J.; Klein, Stefan
2014-03-01
In nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have been proposed as a signal representation suitable for multi-scale problems. Wavelet analysis leads to a unique decomposition of a signal into its coarse- and fine-scale components. Potentially, this could therefore be useful for image registration. In this work, we investigate whether a wavelet-based FFD model has advantages for nonrigid image registration. We use a B-splines based wavelet, as defined by Cai and Wang.1 This wavelet is expressed as a linear combination of B-spline basis functions. Derived from the original B-spline function, this wavelet is smooth, differentiable, and compactly supported. The basis functions of this wavelet are orthogonal across scales in Sobolev space. This wavelet was previously used for registration in computer vision, in 2D optical flow problems,2 but it was not compared with the conventional B-spline FFD in medical image registration problems. An advantage of choosing this B-splines based wavelet model is that the space of allowable deformation is exactly equivalent to that of the traditional B-spline. The wavelet transformation is essentially a (linear) reparameterization of the B-spline transformation model. Experiments on 10 CT lung and 18 T1-weighted MRI brain datasets show that wavelet based registration leads to smoother deformation fields than traditional B-splines based registration, while achieving better accuracy.
Maximizing Entropy over Markov Processes
DEFF Research Database (Denmark)
Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis
2013-01-01
The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of an system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code....
Maximizing entropy over Markov processes
DEFF Research Database (Denmark)
Biondi, Fabrizio; Legay, Axel; Nielsen, Bo Friis
2014-01-01
The channel capacity of a deterministic system with confidential data is an upper bound on the amount of bits of data an attacker can learn from the system. We encode all possible attacks to a system using a probabilistic specification, an Interval Markov Chain. Then the channel capacity...... as a reward function, a polynomial algorithm to verify the existence of a system maximizing entropy among those respecting a specification, a procedure for the maximization of reward functions over Interval Markov Chains and its application to synthesize an implementation maximizing entropy. We show how...... to use Interval Markov Chains to model abstractions of deterministic systems with confidential data, and use the above results to compute their channel capacity. These results are a foundation for ongoing work on computing channel capacity for abstractions of programs derived from code. © 2014 Elsevier...
Raghu, S; Sriraam, N; Kumar, G Pradeep
2017-02-01
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictal-epileptic.
Zhonggang, Liang; Hong, Yan
2006-10-01
A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.
Wavelet Based Diagnosis and Protection of Electric Motors
Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur
2010-01-01
In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN
Early detection of rogue waves by the wavelet transforms
International Nuclear Information System (INIS)
Bayındır, Cihan
2016-01-01
Highlights: • The advantages of wavelet analysis over the Fourier analysis for the early detection of rogue waves are discussed. • The triangular wavelet spectra can be detected at early stages of the development of rogue waves. • The wavelet analysis is capable of detecting not only the emergence but also the location of a rogue wave. • Wavelet analysis is also capable of predicting the characteristic distances between successive rogue waves. - Abstract: We discuss the possible advantages of using the wavelet transform over the Fourier transform for the early detection of rogue waves. We show that the triangular wavelet spectra of the rogue waves can be detected at early stages of the development of rogue waves in a chaotic wave field. Compared to the Fourier spectra, the wavelet spectra are capable of detecting not only the emergence of a rogue wave but also its possible spatial (or temporal) location. Due to this fact, wavelet transform is also capable of predicting the characteristic distances between successive rogue waves. Therefore multiple simultaneous breaking of the successive rogue waves on ships or on the offshore structures can be predicted and avoided by smart designs and operations.
Multiresolution signal decomposition schemes. Part 2: Morphological wavelets
H.J.A.M. Heijmans (Henk); J. Goutsias (John)
1999-01-01
htmlabstractIn its original form, the wavelet transform is a linear tool. However, it has been increasingly recognized that nonlinear extensions are possible. A major impulse to the development of nonlinear wavelet transforms has been given by the introduction of the lifting scheme by Sweldens. The
Evaluation of the wavelet image two-line coder
DEFF Research Database (Denmark)
Rein, Stephan Alexander; Fitzek, Frank Hanns Paul; Gühmann, Clemens
2015-01-01
This paper introduces the wavelet image two-line (Wi2l) coding algorithm for low complexity compression of images. The algorithm recursively encodes an image backwards reading only two lines of a wavelet subband, which are read in blocks of 512 bytes from flash memory. It thus only requires very ...
Polarized spectral features of human breast tissues through wavelet ...
Indian Academy of Sciences (India)
Abstract. Fluorescence characteristics of human breast tissues are investigated through wavelet transform and principal component analysis (PCA). Wavelet transform of polar- ized fluorescence spectra of human breast tissues is found to localize spectral features that can reliably differentiate different tissue types.
Early detection of rogue waves by the wavelet transforms
Energy Technology Data Exchange (ETDEWEB)
Bayındır, Cihan, E-mail: cihan.bayindir@isikun.edu.tr
2016-01-08
Highlights: • The advantages of wavelet analysis over the Fourier analysis for the early detection of rogue waves are discussed. • The triangular wavelet spectra can be detected at early stages of the development of rogue waves. • The wavelet analysis is capable of detecting not only the emergence but also the location of a rogue wave. • Wavelet analysis is also capable of predicting the characteristic distances between successive rogue waves. - Abstract: We discuss the possible advantages of using the wavelet transform over the Fourier transform for the early detection of rogue waves. We show that the triangular wavelet spectra of the rogue waves can be detected at early stages of the development of rogue waves in a chaotic wave field. Compared to the Fourier spectra, the wavelet spectra are capable of detecting not only the emergence of a rogue wave but also its possible spatial (or temporal) location. Due to this fact, wavelet transform is also capable of predicting the characteristic distances between successive rogue waves. Therefore multiple simultaneous breaking of the successive rogue waves on ships or on the offshore structures can be predicted and avoided by smart designs and operations.
Wavelet-Coded OFDM for Next Generation Mobile Communications
DEFF Research Database (Denmark)
Cavalcante, Lucas Costa Pereira; Vegas Olmos, Juan José; Tafur Monroy, Idelfonso
2016-01-01
In this work, we evaluate the performance of Wavelet-Coding into offering robustness for OFDM signals against the combined effects of varying fading and noise bursts. Wavelet-Code enables high diversity gains with a low complex receiver, and, most notably, without compromising the system’s spectr......-wave frequencies in future generation mobile communication due to its robustness against multipath fading....
Energy Technology Data Exchange (ETDEWEB)
Duval, L.
2000-11-01
Wavelet and wavelet packet transforms are the most commonly used algorithms for seismic data compression. Wavelet coefficients are generally quantized and encoded by classical entropy coding techniques. We first propose in this work a compression algorithm based on the wavelet transform. The wavelet transform is used together with a zero-tree type coding, with first use in seismic applications. Classical wavelet transforms nevertheless yield a quite rigid approach, since it is often desirable to adapt the transform stage to the properties of each type of signal. We thus propose a second algorithm using, instead of wavelets, a set of so called 'extended transforms'. These transforms, originating from the filter bank theory, are parameterized. Classical examples are Malvar's Lapped Orthogonal Transforms (LOT) or de Queiroz et al. Generalized Lapped Orthogonal Transforms (GenLOT). We propose several optimization criteria to build 'extended transforms' which are adapted the properties of seismic signals. We further show that these transforms can be used with the same zero-tree type coding technique as used with wavelets. Both proposed algorithms provide exact compression rate choice, block-wise compression (in the case of extended transforms) and partial decompression for quality control or visualization. Performances are tested on a set of actual seismic data. They are evaluated for several quality measures. We also compare them to other seismic compression algorithms. (author)
Maximizing noise energy for noise-masking studies.
Jules Étienne, Cédric; Arleo, Angelo; Allard, Rémy
2017-08-01
Noise-masking experiments are widely used to investigate visual functions. To be useful, noise generally needs to be strong enough to noticeably impair performance, but under some conditions, noise does not impair performance even when its contrast approaches the maximal displayable limit of 100 %. To extend the usefulness of noise-masking paradigms over a wider range of conditions, the present study developed a noise with great masking strength. There are two typical ways of increasing masking strength without exceeding the limited contrast range: use binary noise instead of Gaussian noise or filter out frequencies that are not relevant to the task (i.e., which can be removed without affecting performance). The present study combined these two approaches to further increase masking strength. We show that binarizing the noise after the filtering process substantially increases the energy at frequencies within the pass-band of the filter given equated total contrast ranges. A validation experiment showed that similar performances were obtained using binarized-filtered noise and filtered noise (given equated noise energy at the frequencies within the pass-band) suggesting that the binarization operation, which substantially reduced the contrast range, had no significant impact on performance. We conclude that binarized-filtered noise (and more generally, truncated-filtered noise) can substantially increase the energy of the noise at frequencies within the pass-band. Thus, given a limited contrast range, binarized-filtered noise can display higher energy levels than Gaussian noise and thereby widen the range of conditions over which noise-masking paradigms can be useful.
Evaluating low pass filters on SPECT reconstructed cardiac orientation estimation
Dwivedi, Shekhar
2009-02-01
Low pass filters can affect the quality of clinical SPECT images by smoothing. Appropriate filter and parameter selection leads to optimum smoothing that leads to a better quantification followed by correct diagnosis and accurate interpretation by the physician. This study aims at evaluating the low pass filters on SPECT reconstruction algorithms. Criteria for evaluating the filters are estimating the SPECT reconstructed cardiac azimuth and elevation angle. Low pass filters studied are butterworth, gaussian, hamming, hanning and parzen. Experiments are conducted using three reconstruction algorithms, FBP (filtered back projection), MLEM (maximum likelihood expectation maximization) and OSEM (ordered subsets expectation maximization), on four gated cardiac patient projections (two patients with stress and rest projections). Each filter is applied with varying cutoff and order for each reconstruction algorithm (only butterworth used for MLEM and OSEM). The azimuth and elevation angles are calculated from the reconstructed volume and the variation observed in the angles with varying filter parameters is reported. Our results demonstrate that behavior of hamming, hanning and parzen filter (used with FBP) with varying cutoff is similar for all the datasets. Butterworth filter (cutoff > 0.4) behaves in a similar fashion for all the datasets using all the algorithms whereas with OSEM for a cutoff < 0.4, it fails to generate cardiac orientation due to oversmoothing, and gives an unstable response with FBP and MLEM. This study on evaluating effect of low pass filter cutoff and order on cardiac orientation using three different reconstruction algorithms provides an interesting insight into optimal selection of filter parameters.
International Conference and Workshop on Fractals and Wavelets
Barnsley, Michael; Devaney, Robert; Falconer, Kenneth; Kannan, V; PB, Vinod
2014-01-01
Fractals and wavelets are emerging areas of mathematics with many common factors which can be used to develop new technologies. This volume contains the selected contributions from the lectures and plenary and invited talks given at the International Workshop and Conference on Fractals and Wavelets held at Rajagiri School of Engineering and Technology, India from November 9-12, 2013. Written by experts, the contributions hope to inspire and motivate researchers working in this area. They provide more insight into the areas of fractals, self similarity, iterated function systems, wavelets and the applications of both fractals and wavelets. This volume will be useful for the beginners as well as experts in the fields of fractals and wavelets.
Denoising in Wavelet Packet Domain via Approximation Coefficients
Directory of Open Access Journals (Sweden)
Zahra Vahabi
2012-01-01
Full Text Available In this paper we propose a new approach in the wavelet domain for image denoising. In recent researches wavelet transform has introduced a time-Frequency transform for computing wavelet coefficient and eliminating noise. Some coefficients have effected smaller than the other's from noise, so they can be use reconstruct images with other subbands. We have developed Approximation image to estimate better denoised image. Naturally noiseless subimage introduced image with lower noise. Beside denoising we obtain a bigger compression rate. Increasing image contrast is another advantage of this method. Experimental results demonstrate that our approach compares favorably to more typical methods of denoising and compression in wavelet domain.100 images of LIVE Dataset were tested, comparing signal to noise ratios (SNR,soft thresholding was %1.12 better than hard thresholding, POAC was %1.94 better than soft thresholding and POAC with wavelet packet was %1.48 better than POAC.
Chamaebatiaria millefolium (Torr.) Maxim.: fernbush
Nancy L. Shaw; Emerenciana G. Hurd
2008-01-01
Fernbush - Chamaebatiaria millefolium (Torr.) Maxim. - the only species in its genus, is endemic to the Great Basin, Colorado Plateau, and adjacent areas of the western United States. It is an upright, generally multistemmed, sweetly aromatic shrub 0.3 to 2 m tall. Bark of young branches is brown and becomes smooth and gray with age. Leaves are leathery, alternate,...
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.
Seismic image watermarking using optimized wavelets
International Nuclear Information System (INIS)
Mufti, M.
2010-01-01
Geotechnical processes and technologies are becoming more and more sophisticated by the use of computer and information technology. This has made the availability, authenticity and security of geo technical data even more important. One of the most common methods of storing and sharing seismic data images is through standardized SEG- Y file format.. Geo technical industry is now primarily data centric. The analytic and detection capability of seismic processing tool is heavily dependent on the correctness of the contents of the SEG-Y data file. This paper describes a method through an optimized wavelet transform technique which prevents unauthorized alteration and/or use of seismic data. (author)
Coherent states versus De Broglie-Wavelets
International Nuclear Information System (INIS)
Barut, A.O.
1993-08-01
There are two types of nonspreading localized wave forms representing a stable, individual, indivisible, single quantum particle with interference properties endowed with classical (hidden) parameters, i.e. initial positions and velocity: coherent states and wavelets. The first is exactly known for oscillator, the second for free particles. Their relation and their construction is discussed from a new unified point of view. We then extend this contraction to the Coulomb problem, where with the introduction of a new time variable T, nonspreading states are obtained. (author). 10 refs
Image Mosaic Techniques OptimizationUsing Wavelet
Institute of Scientific and Technical Information of China (English)
ZHOUAn-qi; CUILi
2014-01-01
This essay concentrates on two key procedures of image mosaic——image registration and imagefusion.Becauseof the character of geometric transformation invariance of edge points, wecalculate the angle difference of the direction vector ofedge points in different images anddraw an angle difference histogramto adjust the rotationproblem. Through this way, algorithm based on gray information is expandedandcan be used in images withdisplacementand rotation. Inthe term of image fusion, wavelet multi-scale analysis is used to fuse spliced images. In order to choose the best method of imagefusion,weevaluate the results of different methods of image fusion by cross entropy.
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.
Wavelet analysis of the nuclear phase space
International Nuclear Information System (INIS)
Jouault, B.; Sebille, F.; Mota, V. de la.
1997-01-01
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)
Wavelet-Based Quantum Field Theory
Directory of Open Access Journals (Sweden)
Mikhail V. Altaisky
2007-11-01
Full Text Available The Euclidean quantum field theory for the fields $phi_{Delta x}(x$, which depend on both the position $x$ and the resolution $Delta x$, constructed in SIGMA 2 (2006, 046, on the base of the continuous wavelet transform, is considered. The Feynman diagrams in such a theory become finite under the assumption there should be no scales in internal lines smaller than the minimal of scales of external lines. This regularisation agrees with the existing calculations of radiative corrections to the electron magnetic moment. The transition from the newly constructed theory to a standard Euclidean field theory is achieved by integration over the scale arguments.
Conductance calculations with a wavelet basis set
DEFF Research Database (Denmark)
Thygesen, Kristian Sommer; Bollinger, Mikkel; Jacobsen, Karsten Wedel
2003-01-01
We present a method based on density functional theory (DFT) for calculating the conductance of a phase-coherent system. The metallic contacts and the central region where the electron scattering occurs, are treated on the same footing taking their full atomic and electronic structure into account....... The linear-response conductance is calculated from the Green's function which is represented in terms of a system-independent basis set containing wavelets with compact support. This allows us to rigorously separate the central region from the contacts and to test for convergence in a systematic way...
Multi-scale freeform surface texture filtering using a mesh relaxation scheme
International Nuclear Information System (INIS)
Jiang, Xiangqian; Abdul-Rahman, Hussein S; Scott, Paul J
2013-01-01
Surface filtering algorithms using Fourier, Gaussian, wavelets, etc, are well-established for simple Euclidean geometries. However, these filtration techniques cannot be applied to today's complex freeform surfaces, which have non-Euclidean geometries, without distortion of the results. This paper proposes a new multi-scale filtering algorithm for freeform surfaces that are represented by triangular meshes based on a mesh relaxation scheme. The proposed algorithm is capable of decomposing a freeform surface into different scales and separating surface roughness, waviness and form from each other, as will be demonstrated throughout the paper. Results of applying the proposed algorithm to computer-generated as well as real surfaces are represented and compared with a lifting wavelet filtering algorithm. (paper)
Mei, Shu-Li; Lv, Hong-Liang; Ma, Qin
2008-01-01
Based on restricted variational principle, a novel method for interval wavelet construction is proposed. For the excellent local property of quasi-Shannon wavelet, its interval wavelet is constructed, and then applied to solve ordinary differential equations. Parameter choices for the interval wavelet method are discussed and its numerical performance is demonstrated.
DEFF Research Database (Denmark)
Zibar, Darko; Carvalho, L.; Piels, Molly
2014-01-01
We show that phase noise estimation based on Bayesian filtering outperforms conventional time-domain approaches in the presence of moderate measurement noise. Additionally, carrier synchronization based on Bayesian filtering, in combination with expectation maximization, is demonstrated for the f...
Cryptocurrency price drivers: Wavelet coherence analysis revisited.
Phillips, Ross C; Gorse, Denise
2018-01-01
Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.
Wavelet representation of the nuclear dynamics
International Nuclear Information System (INIS)
Jouault, B.; Sebille, F.; De La Mota, V.
1997-01-01
The study of the transport phenomena in nuclear matter is addressed in a new approach based on wavelet theory and the projection methods of statistical physics. The advantage of this framework is to optimize the representation spaces and the numerical treatment which gives the opportunity to enlarge the spectra of physical processes taken into account to preserve some important quantum information. At the same time this approach is more efficient than the usual solving schemes and mathematical formulations of the equations based on usual concepts. The application of this methodology to the the study of the physical phenomena related to the heavy ion collisions at intermediate energies has resulted in a model named DYWAN (DYnamical WAvelets in Nuclei). The results obtained with DYWAN for the central collisions in the system Ca + Ca at three different beam energies are reported. These are in agreement with the experimental results since a fusion process at 30 MeV is observed as well as a binary reaction at 50 MeV and kind of an explosion of the system at 90 MeV
Wavelet tree structure based speckle noise removal for optical coherence tomography
Yuan, Xin; Liu, Xuan; Liu, Yang
2018-02-01
We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.
Directory of Open Access Journals (Sweden)
Coghetto Roland
2015-09-01
Full Text Available We are inspired by the work of Henri Cartan [16], Bourbaki [10] (TG. I Filtres and Claude Wagschal [34]. We define the base of filter, image filter, convergent filter bases, limit filter and the filter base of tails (fr: filtre des sections.
IMNN: Information Maximizing Neural Networks
Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.
2018-04-01
This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.
International Nuclear Information System (INIS)
Ferrandis, Javier
2005-01-01
The current experimental determination of the absolute values of the CKM elements indicates that 2 vertical bar V ub /V cb V us vertical bar =(1-z), with z given by z=0.19+/-0.14. This fact implies that irrespective of the form of the quark Yukawa matrices, the measured value of the SM CP phase β is approximately the maximum allowed by the measured absolute values of the CKM elements. This is β=(π/6-z/3) for γ=(π/3+z/3), which implies α=π/2. Alternatively, assuming that β is exactly maximal and using the experimental measurement sin(2β)=0.726+/-0.037, the phase γ is predicted to be γ=(π/2-β)=66.3 o +/-1.7 o . The maximality of β, if confirmed by near-future experiments, may give us some clues as to the origin of CP violation
Implementation of Texture Based Image Retrieval Using M-band Wavelet Transform
Institute of Scientific and Technical Information of China (English)
LiaoYa-li; Yangyan; CaoYang
2003-01-01
Wavelet transform has attracted attention because it is a very useful tool for signal analyzing. As a fundamental characteristic of an image, texture traits play an important role in the human vision system for recognition and interpretation of images. The paper presents an approach to implement texture-based image retrieval using M-band wavelet transform. Firstly the traditional 2-band wavelet is extended to M-band wavelet transform. Then the wavelet moments are computed by M-band wavelet coefficients in the wavelet domain. The set of wavelet moments forms the feature vector related to the texture distribution of each wavelet images. The distances between the feature vectors describe the similarities of different images. The experimental result shows that the M-band wavelet moment features of the images are effective for image indexing.The retrieval method has lower computational complexity, yet it is capable of giving better retrieval performance for a given medical image database.
Strategy to maximize maintenance operation
Espinoza, Michael
2005-01-01
This project presents a strategic analysis to maximize maintenance operations in Alcan Kitimat Works in British Columbia. The project studies the role of maintenance in improving its overall maintenance performance. It provides strategic alternatives and specific recommendations addressing Kitimat Works key strategic issues and problems. A comprehensive industry and competitive analysis identifies the industry structure and its competitive forces. In the mature aluminium industry, the bargain...
Scalable Nonlinear AUC Maximization Methods
Khalid, Majdi; Ray, Indrakshi; Chitsaz, Hamidreza
2017-01-01
The area under the ROC curve (AUC) is a measure of interest in various machine learning and data mining applications. It has been widely used to evaluate classification performance on heavily imbalanced data. The kernelized AUC maximization machines have established a superior generalization ability compared to linear AUC machines because of their capability in modeling the complex nonlinear structure underlying most real world-data. However, the high training complexity renders the kernelize...
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.
Signal-dependent independent component analysis by tunable mother wavelets
International Nuclear Information System (INIS)
Seo, Kyung Ho
2006-02-01
The objective of this study is to improve the standard independent component analysis when applied to real-world signals. Independent component analysis starts from the assumption that signals from different physical sources are statistically independent. But real-world signals such as EEG, ECG, MEG, and fMRI signals are not statistically independent perfectly. By definition, standard independent component analysis algorithms are not able to estimate statistically dependent sources, that is, when the assumption of independence does not hold. Therefore before independent component analysis, some preprocessing stage is needed. This paper started from simple intuition that wavelet transformed source signals by 'well-tuned' mother wavelet will be simplified sufficiently, and then the source separation will show better results. By the correlation coefficient method, the tuning process between source signal and tunable mother wavelet was executed. Gamma component of raw EEG signal was set to target signal, and wavelet transform was executed by tuned mother wavelet and standard mother wavelets. Simulation results by these wavelets was shown
A note on the standard dual frame of a wavelet frame with three-scale
International Nuclear Information System (INIS)
Chen Qingjiang; Wei Zongtian; Feng Jinshun
2009-01-01
In this paper, it is shown that there exist wavelet frames generated by two functions which have good dual wavelet frames, but for which the standard dual wavelet frame does not consist of wavelets. That is to say, the standard dual wavelet frame cannot be generated by the translations and dilations of a single function. Relation to some physical theories such as entropy and E-infinity theory is also discussed.
Goossens, Bart; Aelterman, Jan; Luong, Hiep; Pizurica, Aleksandra; Philips, Wilfried
2013-02-01
In digital cameras and mobile phones, there is an ongoing trend to increase the image resolution, decrease the sensor size and to use lower exposure times. Because smaller sensors inherently lead to more noise and a worse spatial resolution, digital post-processing techniques are required to resolve many of the artifacts. Color filter arrays (CFAs), which use alternating patterns of color filters, are very popular because of price and power consumption reasons. However, color filter arrays require the use of a post-processing technique such as demosaicing to recover full resolution RGB images. Recently, there has been some interest in techniques that jointly perform the demosaicing and denoising. This has the advantage that the demosaicing and denoising can be performed optimally (e.g. in the MSE sense) for the considered noise model, while avoiding artifacts introduced when using demosaicing and denoising sequentially. In this paper, we will continue the research line of the wavelet-based demosaicing techniques. These approaches are computationally simple and very suited for combination with denoising. Therefore, we will derive Bayesian Minimum Squared Error (MMSE) joint demosaicing and denoising rules in the complex wavelet packet domain, taking local adaptivity into account. As an image model, we will use Gaussian Scale Mixtures, thereby taking advantage of the directionality of the complex wavelets. Our results show that this technique is well capable of reconstructing fine details in the image, while removing all of the noise, at a relatively low computational cost. In particular, the complete reconstruction (including color correction, white balancing etc) of a 12 megapixel RAW image takes 3.5 sec on a recent mid-range GPU.
FLOUTING MAXIMS IN INDONESIA LAWAK KLUB CONVERSATION
Directory of Open Access Journals (Sweden)
Rahmawati Sukmaningrum
2017-04-01
Full Text Available This study aims to identify the types of maxims flouted in the conversation in famous comedy show, Indonesia Lawak Club. Likewise, it also tries to reveal the speakers‘ intention of flouting the maxim in the conversation during the show. The writers use descriptive qualitative method in conducting this research. The data is taken from the dialogue of Indonesia Lawak club and then analyzed based on Grice‘s cooperative principles. The researchers read the dialogue‘s transcripts, identify the maxims, and interpret the data to find the speakers‘ intention for flouting the maxims in the communication. The results show that there are four types of maxims flouted in the dialogue. Those are maxim of quality (23%, maxim of quantity (11%, maxim of manner (31%, and maxim of relevance (35. Flouting the maxims in the conversations is intended to make the speakers feel uncomfortable with the conversation, show arrogances, show disagreement or agreement, and ridicule other speakers.
Identification Method of Mud Shale Fractures Base on Wavelet Transform
Xia, Weixu; Lai, Fuqiang; Luo, Han
2018-01-01
In recent years, inspired by seismic analysis technology, a new method for analysing mud shale fractures oil and gas reservoirs by logging properties has emerged. By extracting the high frequency attribute of the wavelet transform in the logging attribute, the formation information hidden in the logging signal is extracted, identified the fractures that are not recognized by conventional logging and in the identified fracture segment to show the “cycle jump”, “high value”, “spike” and other response effect is more obvious. Finally formed a complete wavelet denoising method and wavelet high frequency identification fracture method.
EEG Artifact Removal Using a Wavelet Neural Network
Nguyen, Hoang-Anh T.; Musson, John; Li, Jiang; McKenzie, Frederick; Zhang, Guangfan; Xu, Roger; Richey, Carl; Schnell, Tom
2011-01-01
!n this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We. compared the WNN algorithm with .the ICA technique ,and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.
A hybrid video compression based on zerotree wavelet structure
International Nuclear Information System (INIS)
Kilic, Ilker; Yilmaz, Reyat
2009-01-01
A video compression algorithm comparable to the standard techniques at low bit rates is presented in this paper. The overlapping block motion compensation (OBMC) is combined with discrete wavelet transform which followed by Lloyd-Max quantization and zerotree wavelet (ZTW) structure. The novel feature of this coding scheme is the combination of hierarchical finite state vector quantization (HFSVQ) with the ZTW to encode the quantized wavelet coefficients. It is seen that the proposed video encoder (ZTW-HFSVQ) performs better than the MPEG-4 and Zerotree Entropy Coding (ZTE). (author)
Pseudo-stochastic signal characterization in wavelet-domain
International Nuclear Information System (INIS)
Zaytsev, Kirill I; Zhirnov, Andrei A; Alekhnovich, Valentin I; Yurchenko, Stanislav O
2015-01-01
In this paper we present the method for fast and accurate characterization of pseudo-stochastic signals, which contain a large number of similar but randomly-located fragments. This method allows estimating the statistical characteristics of pseudo-stochastic signal, and it is based on digital signal processing in wavelet-domain. Continuous wavelet transform and the criterion for wavelet scale power density are utilized. We are experimentally implementing this method for the purpose of sand granulometry, and we are estimating the statistical parameters of test sand fractions
Analysis of Ultrasonic Transmitted Signal for Apple using Wavelet Transform
International Nuclear Information System (INIS)
Kim, Ki Bok; Lee, Sang Dae; Choi, Man Yong; Kim, Man Soo
2005-01-01
This study was conducted to analyze the ultrasonic transmitted signal for apple using wavelet transform. Fruit consists of nonlinear visco-elastic properties such as flesh, an ovary and rind and lienee most ultrasonic wave is attenuated and its frequency is shifted during passing the fruit. Thus it is not easy to evaluate the internal quality of the fruit using typical ultrasonic parameters such as wave velocity, attenuation, and frequency spectrum. The discrete wavelet transform was applied to the ultrasonic transmitted signal for apple. The magnitude of the first peak frequency of the wavelet basis from the ultrasonic transmitted signal showed a close correlation to the storage time of apple
Evolutive Optimization of Wavelets and Shapelets for Bioelectrical Signal Analysis
Pinzón Morales, Rubén Dario
2011-01-01
análisis Wavelet es una poderosa herramienta para el procesamiento de señal digital. Ha sido ampliamente utilizado en señales bioeléctricas incluyendo evocar potenciales relacionados (ERP), señales de electromiografía (EMG), grabaciones de microelectrodos (MER), electrocardiograma (ECG), electroencefalogramas (EEG), entre otros. Algunas de las principales ventajas de la wavelet transform son el soporte compacto, y la concentración de la energía. Básicamente, la transformada wavelet es una con...
Wavelets an elementary treatment of theory and applications
Koornwinder, T H
1993-01-01
Nowadays, some knowledge of wavelets is almost mandatory for mathematicians, physicists and electrical engineers. The emphasis in this volume, based on an intensive course on Wavelets given at CWI, Amsterdam, is on the affine case. The first part presents a concise introduction of the underlying theory to the uninitiated reader. The second part gives applications in various areas. Some of the contributions here are a fresh exposition of earlier work by others, while other papers contain new results by the authors. The areas are so diverse as seismic processing, quadrature formulae, and wavelet
Comparative study of wavelets of the first and second generation
International Nuclear Information System (INIS)
Ososkov, G.A.; Shitov, A.B.; Stadnik, A.V.
2001-01-01
In order to compare efficiency a comprehensive set of benchmarking tests is developed, which is used to compare abilities of continuous wavelet transform of the vanishing momenta type as well as the second generation wavelets constructed on the basis of the lifting scheme. It is based on processing of various types of pure and contaminated harmonic signals, delta-function, study of the signal phase dependence and the gain-frequency characteristics. The results of a comparative multiscale analysis allow one to reveal advantages and flaws of the considered types of wavelets
Miniaturized dielectric waveguide filters
Sandhu, MY; Hunter, IC
2016-01-01
Design techniques for a new class of integrated monolithic high-permittivity ceramic waveguide filters are presented. These filters enable a size reduction of 50% compared to air-filled transverse electromagnetic filters with the same unloaded Q-factor. Designs for Chebyshev and asymmetric generalised Chebyshev filter and a diplexer are presented with experimental results for an 1800 MHz Chebyshev filter and a 1700 MHz generalised Chebyshev filter showing excellent agreement with theory.
Real-time wavelet-based inline banknote-in-bundle counting for cut-and-bundle machines
Petker, Denis; Lohweg, Volker; Gillich, Eugen; Türke, Thomas; Willeke, Harald; Lochmüller, Jens; Schaede, Johannes
2011-03-01
Automatic banknote sheet cut-and-bundle machines are widely used within the scope of banknote production. Beside the cutting-and-bundling, which is a mature technology, image-processing-based quality inspection for this type of machine is attractive. We present in this work a new real-time Touchless Counting and perspective cutting blade quality insurance system, based on a Color-CCD-Camera and a dual-core Computer, for cut-and-bundle applications in banknote production. The system, which applies Wavelet-based multi-scale filtering is able to count banknotes inside a 100-bundle within 200-300 ms depending on the window size.
Directory of Open Access Journals (Sweden)
Łukasz Doliński
2018-01-01
Full Text Available This paper demonstrates the effectiveness of a nondestructive diagnostic technique used to determine the location and size of delamination in laminated coatings of wind turbine blades. This is realized based on results of numerical and experimental investigations obtained by the use of the finite element method (FEM and laser scanning vibrometry (LSV. The proposed method is based on the one-dimensional continuous wavelet transform of vibration parameters of a wind turbine blade. The investigations were conducted for a 1 : 10 scaled-down blade of a 36 m rotor wind turbine. Glass fibres and epoxy resin were used as laminate components. For numerical studies, a simple delamination model was proposed. The results obtained by the authors were used to determine the optimal set of parameters of the continuous wavelet transform. The application of high-quality LSV for experimental measurements allowed determining the optimal conditions of measuring procedures. At the same time the capabilities and limitations, resulting from the nature of the measurement method, were identified. In order to maximize the effectiveness of the detection method, preliminary signal processing was performed. Beside base wavelets also different waveform families were tested. The results obtained by the authors showed that it is possible to identify and localize even relatively small damage.
Power maximization of a point absorber wave energy converter using improved model predictive control
Milani, Farideh; Moghaddam, Reihaneh Kardehi
2017-08-01
This paper considers controlling and maximizing the absorbed power of wave energy converters for irregular waves. With respect to physical constraints of the system, a model predictive control is applied. Irregular waves' behavior is predicted by Kalman filter method. Owing to the great influence of controller parameters on the absorbed power, these parameters are optimized by imperialist competitive algorithm. The results illustrate the method's efficiency in maximizing the extracted power in the presence of unknown excitation force which should be predicted by Kalman filter.
Fast Image Edge Detection based on Faber Schauder Wavelet and Otsu Threshold
Directory of Open Access Journals (Sweden)
Assma Azeroual
2017-12-01
Full Text Available Edge detection is a critical stage in many computer vision systems, such as image segmentation and object detection. As it is difficult to detect image edges with precision and with low complexity, it is appropriate to find new methods for edge detection. In this paper, we take advantage of Faber Schauder Wavelet (FSW and Otsu threshold to detect edges in a multi-scale way with low complexity, since the extrema coefficients of this wavelet are located on edge points and contain only arithmetic operations. First, the image is smoothed using bilateral filter depending on noise estimation. Second, the FSW extrema coefficients are selected based on Otsu threshold. Finally, the edge points are linked using a predictive edge linking algorithm to get the image edges. The effectiveness of the proposed method is supported by the experimental results which prove that our method is faster than many competing state-of-the-art approaches and can be used in real-time applications.
Energy Technology Data Exchange (ETDEWEB)
Jammazi, Rania; Aloui, Chaker [International finance group-Tunisia, Faculty of Management and Economic Sciences of Tunis, Boulevard du 7 novembre, El Manar University, B.P. 248, C.P. 2092, Tunis Cedex (Tunisia)
2010-03-15
While there is a large body of empirical studies on the relationship between crude oil price changes and stock market returns, they have failed to achieve a consensus on this subject. In this paper, we combine wavelet analysis and Markov Switching Vector Autoregressive (MS-VAR) approach to explore the impact of the crude oil (CO) shocks on the stock market returns for UK, France and Japan over the period from January 1989 to December 2007. Our procedure involves the estimation of the extended MS-VAR model in order to investigate the importance of the resultant wavelet filtering series (after removing random components) in determining the behavior of the stock market volatilities. We show that CO shocks do not affect the recession stock market phases (except for Japan). However, they significantly reduce moderate and/or expansion stock market phases temporarily. Moreover, this negative relationship appears to be more pronounced during the pre-1999 period. The empirical findings will prove extremely useful to investors who need to understand the exact effect of international oil changes on certain stocks prices as well as for policy managers who need a more thorough evaluation about the efficiency of hedging policies affected by oil price changes. (author)
Energy Technology Data Exchange (ETDEWEB)
Jammazi, Rania, E-mail: jamrania2@yahoo.f [International finance group-Tunisia, Faculty of Management and Economic Sciences of Tunis, Boulevard du 7 novembre, El Manar University, B.P. 248, C.P. 2092, Tunis Cedex (Tunisia); Aloui, Chaker, E-mail: chaker.aloui@fsegt.rnu.t [International finance group-Tunisia, Faculty of Management and Economic Sciences of Tunis, Boulevard du 7 novembre, El Manar University, B.P. 248, C.P. 2092, Tunis Cedex (Tunisia)
2010-03-15
While there is a large body of empirical studies on the relationship between crude oil price changes and stock market returns, they have failed to achieve a consensus on this subject. In this paper, we combine wavelet analysis and Markov Switching Vector Autoregressive (MS-VAR) approach to explore the impact of the crude oil (CO) shocks on the stock market returns for UK, France and Japan over the period from January 1989 to December 2007. Our procedure involves the estimation of the extended MS-VAR model in order to investigate the importance of the resultant wavelet filtering series (after removing random components) in determining the behavior of the stock market volatilities. We show that CO shocks do not affect the recession stock market phases (except for Japan). However, they significantly reduce moderate and/or expansion stock market phases temporarily. Moreover, this negative relationship appears to be more pronounced during the pre-1999 period. The empirical findings will prove extremely useful to investors who need to understand the exact effect of international oil changes on certain stocks prices as well as for policy managers who need a more thorough evaluation about the efficiency of hedging policies affected by oil price changes.
Zhang, Xiaolei; Zhang, Xiangchao; Yuan, He; Zhang, Hao; Xu, Min
2018-02-01
Digital holography is a promising measurement method in the fields of bio-medicine and micro-electronics. But the captured images of digital holography are severely polluted by the speckle noise because of optical scattering and diffraction. Via analyzing the properties of Fresnel diffraction and the topographies of micro-structures, a novel reconstruction method based on the dual-tree complex wavelet transform (DT-CWT) is proposed. This algorithm is shiftinvariant and capable of obtaining sparse representations for the diffracted signals of salient features, thus it is well suited for multiresolution processing of the interferometric holograms of directional morphologies. An explicit representation of orthogonal Fresnel DT-CWT bases and a specific filtering method are developed. This method can effectively remove the speckle noise without destroying the salient features. Finally, the proposed reconstruction method is compared with the conventional Fresnel diffraction integration and Fresnel wavelet transform with compressive sensing methods to validate its remarkable superiority on the aspects of topography reconstruction and speckle removal.
DE-BLURRING SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY IMAGES USING WAVELET DECOMPOSITION
Directory of Open Access Journals (Sweden)
Neethu M. Sasi
2016-02-01
Full Text Available Single photon emission computed tomography imaging is a popular nuclear medicine imaging technique which generates images by detecting radiations emitted by radioactive isotopes injected in the human body. Scattering of these emitted radiations introduces blur in this type of images. This paper proposes an image processing technique to enhance cardiac single photon emission computed tomography images by reducing the blur in the image. The algorithm works in two main stages. In the first stage a maximum likelihood estimate of the point spread function and the true image is obtained. In the second stage Lucy Richardson algorithm is applied on the selected wavelet coefficients of the true image estimate. The significant contribution of this paper is that processing of images is done in the wavelet domain. Pre-filtering is also done as a sub stage to avoid unwanted ringing effects. Real cardiac images are used for the quantitative and qualitative evaluations of the algorithm. Blur metric, peak signal to noise ratio and Tenengrad criterion are used as quantitative measures. Comparison against other existing de-blurring algorithms is also done. The simulation results indicate that the proposed method effectively reduces blur present in the image.
Directory of Open Access Journals (Sweden)
Shuting Wan
2018-05-01
Full Text Available Kurtogram can adaptively select the resonant frequency band, and then the characteristic fault frequency can be obtained by analyzing the selected band. However, the kurtogram is easily affected by random impulses and noise. In recent years, improvements to kurtogram have been concentrated on two aspects: (a the decomposition method of the frequency band; and (b the selection index of the optimal frequency band. In this article, a new method called Teager Energy Entropy Ratio Gram (TEERgram is proposed. The TEER algorithm takes the wavelet packet transform (WPT as the signal frequency band decomposition method, which can adaptively segment the frequency band and control the noise. At the same time, Teager Energy Entropy Ratio (TEER is proposed as a computing index for wavelet packet subbands. WPT has better decomposition properties than traditional finite impulse response (FIR filtering and Fourier decomposition in the kurtogram algorithm. At the same time, TEER has better performance than the envelope spectrum or even the square envelope spectrum. Therefore, the TEERgram method can accurately identify the resonant frequency band under strong background noise. The effectiveness of the proposed method is verified by simulation and experimental analysis.
Qian, Jinfang; Zhang, Changjiang
2014-11-01
An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.
Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images
Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.
2017-10-01
Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.
International Nuclear Information System (INIS)
Jammazi, Rania; Aloui, Chaker
2010-01-01
While there is a large body of empirical studies on the relationship between crude oil price changes and stock market returns, they have failed to achieve a consensus on this subject. In this paper, we combine wavelet analysis and Markov Switching Vector Autoregressive (MS-VAR) approach to explore the impact of the crude oil (CO) shocks on the stock market returns for UK, France and Japan over the period from January 1989 to December 2007. Our procedure involves the estimation of the extended MS-VAR model in order to investigate the importance of the resultant wavelet filtering series (after removing random components) in determining the behavior of the stock market volatilities. We show that CO shocks do not affect the recession stock market phases (except for Japan). However, they significantly reduce moderate and/or expansion stock market phases temporarily. Moreover, this negative relationship appears to be more pronounced during the pre-1999 period. The empirical findings will prove extremely useful to investors who need to understand the exact effect of international oil changes on certain stocks prices as well as for policy managers who need a more thorough evaluation about the efficiency of hedging policies affected by oil price changes.
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.
Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali
2017-07-01
The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.
Wavelet network controller for nuclear steam generators
International Nuclear Information System (INIS)
Habibiyan, H; Sayadian, A; Ghafoori-Fard, H
2005-01-01
Poor control of steam generator water level is the main cause of unexpected shutdowns in nuclear power plants. Particularly at low powers, it is a difficult task due to shrink and swell phenomena and flow measurement errors. In addition, the steam generator is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. Therefore, it seems that design of a suitable controller is a necessary step to enhance plant availability factor. The purpose of this paper is to design, analyze and evaluate a water level controller for U-tube steam generators using wavelet neural networks. Computer simulations show that the proposed controller improves transient response of steam generator water level and demonstrate its superiority to existing controllers
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.
Harmonic analysis from Fourier to wavelets
Pereyra, Maria Cristina
2012-01-01
In the last 200 years, harmonic analysis has been one of the most influential bodies of mathematical ideas, having been exceptionally significant both in its theoretical implications and in its enormous range of applicability throughout mathematics, science, and engineering. In this book, the authors convey the remarkable beauty and applicability of the ideas that have grown from Fourier theory. They present for an advanced undergraduate and beginning graduate student audience the basics of harmonic analysis, from Fourier's study of the heat equation, and the decomposition of functions into sums of cosines and sines (frequency analysis), to dyadic harmonic analysis, and the decomposition of functions into a Haar basis (time localization). While concentrating on the Fourier and Haar cases, the book touches on aspects of the world that lies between these two different ways of decomposing functions: time-frequency analysis (wavelets). Both finite and continuous perspectives are presented, allowing for the introd...
Hexagonal wavelet processing of digital mammography
Laine, Andrew F.; Schuler, Sergio; Huda, Walter; Honeyman-Buck, Janice C.; Steinbach, Barbara G.
1993-09-01
This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms and used to enhance features of importance to mammography within a continuum of scale-space. We present a method of contrast enhancement based on an overcomplete, non-separable multiscale representation: the hexagonal wavelet transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by local and global non-linear operators. Multiscale edges identified within distinct levels of transform space provide local support for enhancement. We demonstrate that features extracted from multiresolution representations can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.
Network Anomaly Detection Based on Wavelet Analysis
Lu, Wei; Ghorbani, Ali A.
2008-12-01
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.
Fast wavelet based sparse approximate inverse preconditioner
Energy Technology Data Exchange (ETDEWEB)
Wan, W.L. [Univ. of California, Los Angeles, CA (United States)
1996-12-31
Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
Directory of Open Access Journals (Sweden)
Mohd Tahir Ismail
2016-06-01
Full Text Available The daily returns of four African countries' stock market indices for the period January 2, 2000, to December 31, 2014, were employed to compare the GARCH(1,1 model and a newly proposed Maximal Overlap Discreet Wavelet Transform (MODWT-GARCH(1,1 model. The results showed that although both models fit the returns data well, the forecast produced by the GARCH(1,1 model underestimates the observed returns whereas the newly proposed MODWT-GARCH(1,1 model generates an accurate forecast value of the observed returns. The results generally showed that the newly proposed MODWT-GARCH(1,1 model best fits returns series for these African countries. Hence the proposed MODWT-GARCH should be applied on other context to further verify its validity.
Study and analysis of wavelet based image compression techniques
African Journals Online (AJOL)
user
Discrete Wavelet Transform (DWT) is a recently developed compression ... serve emerging areas of mobile multimedia and internet communication, ..... In global thresholding the best trade-off between PSNR and compression is provided by.
Selection of the wavelet function for the frequencies estimation
International Nuclear Information System (INIS)
Garcia R, A.
2007-01-01
At the moment the signals are used to diagnose the state of the systems, by means of the extraction of their more important characteristics such as the frequencies, tendencies, changes and temporary evolutions. This characteristics are detected by means of diverse analysis techniques, as Autoregressive methods, Fourier Transformation, Fourier transformation in short time, Wavelet transformation, among others. The present work uses the one Wavelet transformation because it allows to analyze stationary, quasi-stationary and transitory signals in the time-frequency plane. It also describes a methodology to select the scales and the Wavelet function to be applied the one Wavelet transformation with the objective of detecting to the dominant system frequencies. (Author)
SYMMETRY, HAMILTONIAN PROBLEMS AND WAVELETS IN ACCELERATOR PHYSICS
International Nuclear Information System (INIS)
FEDOROVA, A.; ZEITLIN, M.; PARSA, Z.
2000-01-01
In this paper the authors consider applications of methods from wavelet analysis to nonlinear dynamical problems related to accelerator physics. In this approach they take into account underlying algebraical, geometrical and topological structures of corresponding problems
Wavelet packet transform-based robust video watermarking technique
Indian Academy of Sciences (India)
If any conflict happens to the copyright identification and authentication, ... the present work is concentrated on the robust digital video watermarking. .... the wavelet decomposition, resulting in a new family of orthonormal bases for function ...
Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation
Directory of Open Access Journals (Sweden)
Schell Thomas
2003-01-01
Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.
Application of wavelets to singular integral scattering equations
International Nuclear Information System (INIS)
Kessler, B.M.; Payne, G.L.; Polyzou, W.N.
2004-01-01
The use of orthonormal wavelet basis functions for solving singular integral scattering equations is investigated. It is shown that these basis functions lead to sparse matrix equations which can be solved by iterative techniques. The scaling properties of wavelets are used to derive an efficient method for evaluating the singular integrals. The accuracy and efficiency of the wavelet transforms are demonstrated by solving the two-body T-matrix equation without partial wave projection. The resulting matrix equation which is characteristic of multiparticle integral scattering equations is found to provide an efficient method for obtaining accurate approximate solutions to the integral equation. These results indicate that wavelet transforms may provide a useful tool for studying few-body systems
Journal Afrika Statistika ISSN 0852-0305 Nonlinear wavelet ...
African Journals Online (AJOL)
mator; Nonparametric regression; Strong mixing condition. ... In this paper we consider the right censorship model and we introduce a new nonlinear ... provide excellent selective review article on nonlinear wavelet methods in nonparametric.
Processing of pulse oximeter data using discrete wavelet analysis.
Lee, Seungjoon; Ibey, Bennett L; Xu, Weijian; Wilson, Mark A; Ericson, M Nance; Coté, Gerard L
2005-07-01
A wavelet-based signal processing technique was employed to improve an implantable blood perfusion monitoring system. Data was acquired from both in vitro and in vivo sources: a perfusion model and the proximal jejunum of an adult pig. Results showed that wavelet analysis could isolate perfusion signals from raw, periodic, in vitro data as well as fast Fourier transform (FFT) methods. However, for the quasi-periodic in vivo data segments, wavelet analysis provided more consistent results than the FFT analysis for data segments of 50, 10, and 5 s in length. Wavelet analysis has thus been shown to require less data points for quasi-periodic data than FFT analysis making it a good choice for an indwelling perfusion monitor where power consumption and reaction time are paramount.
Lawther, R
2018-01-01
In this work the author lets \\Phi be an irreducible root system, with Coxeter group W. He considers subsets of \\Phi which are abelian, meaning that no two roots in the set have sum in \\Phi \\cup \\{ 0 \\}. He classifies all maximal abelian sets (i.e., abelian sets properly contained in no other) up to the action of W: for each W-orbit of maximal abelian sets we provide an explicit representative X, identify the (setwise) stabilizer W_X of X in W, and decompose X into W_X-orbits. Abelian sets of roots are closely related to abelian unipotent subgroups of simple algebraic groups, and thus to abelian p-subgroups of finite groups of Lie type over fields of characteristic p. Parts of the work presented here have been used to confirm the p-rank of E_8(p^n), and (somewhat unexpectedly) to obtain for the first time the 2-ranks of the Monster and Baby Monster sporadic groups, together with the double cover of the latter. Root systems of classical type are dealt with quickly here; the vast majority of the present work con...
Schrödinger like equation for wavelets
Directory of Open Access Journals (Sweden)
A. Zúñiga-Segundo
2016-01-01
Full Text Available An explicit phase space representation of the wave function is build based on a wavelet transformation. The wavelet transformation allows us to understand the relationship between s − ordered Wigner function, (or Wigner function when s = 0, and the Torres-Vega-Frederick’s wave functions. This relationship is necessary to find a general solution of the Schrödinger equation in phase-space.
Secured Data Transmission Using Wavelet Based Steganography and cryptography
K.Ravindra Reddy; Ms Shaik Taj Mahaboob
2014-01-01
Steganography and cryptographic methods are used together with wavelets to increase the security of the data while transmitting through networks. Another technology, the digital watermarking is the process of embedding information into a digital (image) signal. Before embedding the plain text into the image, the plain text is encrypted by using Data Encryption Standard (DES) algorithm. The encrypted text is embedded into the LL sub band of the wavelet decomposed image using Le...
Frame scaling function sets and frame wavelet sets in Rd
International Nuclear Information System (INIS)
Liu Zhanwei; Hu Guoen; Wu Guochang
2009-01-01
In this paper, we classify frame wavelet sets and frame scaling function sets in higher dimensions. Firstly, we obtain a necessary condition for a set to be the frame wavelet sets. Then, we present a necessary and sufficient condition for a set to be a frame scaling function set. We give a property of frame scaling function sets, too. Some corresponding examples are given to prove our theory in each section.
Big data extraction with adaptive wavelet analysis (Presentation Video)
Qu, Hongya; Chen, Genda; Ni, Yiqing
2015-04-01
Nondestructive evaluation and sensing technology have been increasingly applied to characterize material properties and detect local damage in structures. More often than not, they generate images or data strings that are difficult to see any physical features without novel data extraction techniques. In the literature, popular data analysis techniques include Short-time Fourier Transform, Wavelet Transform, and Hilbert Transform for time efficiency and adaptive recognition. In this study, a new data analysis technique is proposed and developed by introducing an adaptive central frequency of the continuous Morlet wavelet transform so that both high frequency and time resolution can be maintained in a time-frequency window of interest. The new analysis technique is referred to as Adaptive Wavelet Analysis (AWA). This paper will be organized in several sections. In the first section, finite time-frequency resolution limitations in the traditional wavelet transform are introduced. Such limitations would greatly distort the transformed signals with a significant frequency variation with time. In the second section, Short Time Wavelet Transform (STWT), similar to Short Time Fourier Transform (STFT), is defined and developed to overcome such shortcoming of the traditional wavelet transform. In the third section, by utilizing the STWT and a time-variant central frequency of the Morlet wavelet, AWA can adapt the time-frequency resolution requirement to the signal variation over time. Finally, the advantage of the proposed AWA is demonstrated in Section 4 with a ground penetrating radar (GPR) image from a bridge deck, an analytical chirp signal with a large range sinusoidal frequency change over time, the train-induced acceleration responses of the Tsing-Ma Suspension Bridge in Hong Kong, China. The performance of the proposed AWA will be compared with the STFT and traditional wavelet transform.
A short introduction to frames, Gabor systems, and wavelet systems
DEFF Research Database (Denmark)
Christensen, Ole
2014-01-01
In this article we present a short survey of frame theory in Hilbert spaces. We discuss Gabor frames and wavelet frames, and a recent transform that allows to move results from one setting into the other and vice versa.......In this article we present a short survey of frame theory in Hilbert spaces. We discuss Gabor frames and wavelet frames, and a recent transform that allows to move results from one setting into the other and vice versa....
Fast, large-scale hologram calculation in wavelet domain
Shimobaba, Tomoyoshi; Matsushima, Kyoji; Takahashi, Takayuki; Nagahama, Yuki; Hasegawa, Satoki; Sano, Marie; Hirayama, Ryuji; Kakue, Takashi; Ito, Tomoyoshi
2018-04-01
We propose a large-scale hologram calculation using WAvelet ShrinkAge-Based superpositIon (WASABI), a wavelet transform-based algorithm. An image-type hologram calculated using the WASABI method is printed on a glass substrate with the resolution of 65 , 536 × 65 , 536 pixels and a pixel pitch of 1 μm. The hologram calculation time amounts to approximately 354 s on a commercial CPU, which is approximately 30 times faster than conventional methods.
Controlled wavelet domain sparsity for x-ray tomography
Purisha, Zenith; Rimpeläinen, Juho; Bubba, Tatiana; Siltanen, Samuli
2018-01-01
Tomographic reconstruction is an ill-posed inverse problem that calls for regularization. One possibility is to require sparsity of the unknown in an orthonormal wavelet basis. This, in turn, can be achieved by variational regularization, where the penalty term is the sum of the absolute values of the wavelet coefficients. The primal-dual fixed point algorithm showed that the minimizer of the variational regularization functional can be computed iteratively using a soft-thresholding operation. Choosing the soft-thresholding parameter \
International Nuclear Information System (INIS)
Regentova, E.; Zhang, L.; Veni, G.; Zheng, J.
2007-01-01
A system is designed for detecting microcalcification clusters (MCC) in digital mammograms. The system is intended for computer-aided diagnostic prompting. Further discrimination of MCC as benign or malignant is assumed to be performed by radiologists. Processing of mammograms is based on the statistical modeling by means of wavelet domain hidden markov trees (WHMT). Segmentation is performed by the weighted likelihood evaluation followed by the classification based on spatial filters for a single microcalcification (MC) and a cluster of MC detection. The analysis is carried out on FROC curves for 40 mammograms from the mini-MIAS database and for 100 mammograms with 50 cancerous and 50 benign cases from DDSM database. The designed system is capable to detect 100% of true positive cases in these sets. The rate of false positives is 2.9 per case for mini-MIAS dataset; and 0.01 for the DDSM images. (orig.)
Hegde, Ganapathi; Vaya, Pukhraj
2013-10-01
This article presents a parallel architecture for 3-D discrete wavelet transform (3-DDWT). The proposed design is based on the 1-D pipelined lifting scheme. The architecture is fully scalable beyond the present coherent Daubechies filter bank (9, 7). This 3-DDWT architecture has advantages such as no group of pictures restriction and reduced memory referencing. It offers low power consumption, low latency and high throughput. The computing technique is based on the concept that lifting scheme minimises the storage requirement. The application specific integrated circuit implementation of the proposed architecture is done by synthesising it using 65 nm Taiwan Semiconductor Manufacturing Company standard cell library. It offers a speed of 486 MHz with a power consumption of 2.56 mW. This architecture is suitable for real-time video compression even with large frame dimensions.
Hortos, William S.
2008-04-01
Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at
An adaptive mode-driven spatiotemporal motion vector prediction for wavelet video coding
Zhao, Fan; Liu, Guizhong; Qi, Yong
2010-07-01
The three-dimensional subband/wavelet codecs use 5/3 filters rather than Haar filters for the motion compensation temporal filtering (MCTF) to improve the coding gain. In order to curb the increased motion vector rate, an adaptive motion mode driven spatiotemporal motion vector prediction (AMDST-MVP) scheme is proposed. First, by making use of the direction histograms of four motion vector fields resulting from the initial spatial motion vector prediction (SMVP), the motion mode of the current GOP is determined according to whether the fast or complex motion exists in the current GOP. Then the GOP-level MVP scheme is thereby determined by either the S-MVP or the AMDST-MVP, namely, AMDST-MVP is the combination of S-MVP and temporal-MVP (T-MVP). If the latter is adopted, the motion vector difference (MVD) between the neighboring MV fields and the S-MVP resulting MV of the current block is employed to decide whether or not the MV of co-located block in the previous frame is used for prediction the current block. Experimental results show that AMDST-MVP not only can improve the coding efficiency but also reduce the number of computation complexity.
Applications of wavelet transforms for nuclear power plant signal analysis
International Nuclear Information System (INIS)
Seker, S.; Turkcan, E.; Upadhyaya, B.R.; Erbay, A.S.
1998-01-01
The safety of Nuclear Power Plants (NPPs) may be enhanced by the timely processing of information derived from multiple process signals from NPPs. The most widely used technique in signal analysis applications is the Fourier transform in the frequency domain to generate power spectral densities (PSD). However, the Fourier transform is global in nature and will obscure any non-stationary signal feature. Lately, a powerful technique called the Wavelet Transform, has been developed. This transform uses certain basis functions for representing the data in an effective manner, with capability for sub-band analysis and providing time-frequency localization as needed. This paper presents a brief overview of wavelets applied to the nuclear industry for signal processing and plant monitoring. The basic theory of Wavelets is also summarized. In order to illustrate the application of wavelet transforms data were acquired from the operating nuclear power plant Borssele in the Netherlands. The experimental data consist of various signals in the power plant and are selected from a stationary power operation. Their frequency characteristics and the mutual relations were investigated using MATLAB signal processing and wavelet toolbox for computing their PSDs and coherence functions by multi-resolution analysis. The results indicate that the sub-band PSD matches with the original signal PSD and enhances the estimation of coherence functions. The Wavelet analysis demonstrates the feasibility of application to stationary signals to provide better estimates in the frequency band of interest as compared to the classical FFT approach. (author)
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.
Application of wavelet transform in seismic signal processing
International Nuclear Information System (INIS)
Ghasemi, M. R.; Mohammadzadeh, A.; Salajeghe, E.
2005-01-01
Wavelet transform is a new tool for signal analysis which can perform a simultaneous signal time and frequency representations. Under Multi Resolution Analysis, one can quickly determine details for signals and their properties using Fast Wavelet Transform algorithms. In this paper, for a better physical understanding of a signal and its basic algorithms, Multi Resolution Analysis together with wavelet transforms in a form of Digital Signal Processing will be discussed. For a Seismic Signal Processing, sets of Orthonormal Daubechies Wavelets are suggested. when dealing with the application of wavelets in SSP, one may discuss about denoising from the signal and data compression existed in the signal, which is important in seismic signal data processing. Using this techniques, EL-Centro and Nagan signals were remodeled with a 25% of total points, resulted in a satisfactory results with an acceptable error drift. Thus a total of 1559 and 2500 points for EL-centro and Nagan seismic curves each, were reduced to 389 and 625 points respectively, with a very reasonable error drift, details of which are recorded in the paper. Finally, the future progress in signal processing, based on wavelet theory will be appointed
Recirculating electric air filter
Bergman, W.
1985-01-09
An electric air filter cartridge has a cylindrical inner high voltage electrode, a layer of filter material, and an outer ground electrode formed of a plurality of segments moveably connected together. The outer electrode can be easily opened to remove or insert filter material. Air flows through the two electrodes and the filter material and is exhausted from the center of the inner electrode.
Energy Technology Data Exchange (ETDEWEB)
Garcia R, A. [ININ, Carretera Mexico-Toluca S/N, 52750 La Marquesa, Ocoyoacac, Estado de Mexico (Mexico)]. e-mail: ramador@nuclear.inin.mx
2007-07-01
At the moment the signals are used to diagnose the state of the systems, by means of the extraction of their more important characteristics such as the frequencies, tendencies, changes and temporary evolutions. This characteristics are detected by means of diverse analysis techniques, as Autoregressive methods, Fourier Transformation, Fourier transformation in short time, Wavelet transformation, among others. The present work uses the one Wavelet transformation because it allows to analyze stationary, quasi-stationary and transitory signals in the time-frequency plane. It also describes a methodology to select the scales and the Wavelet function to be applied the one Wavelet transformation with the objective of detecting to the dominant system frequencies. (Author)
Künzi, R.
2015-06-15
Power converters require passive low-pass filters which are capable of reducing voltage ripples effectively. In contrast to signal filters, the components of power filters must carry large currents or withstand large voltages, respectively. In this paper, three different suitable filter struc tures for d.c./d.c. power converters with inductive load are introduced. The formulas needed to calculate the filter components are derived step by step and practical examples are given. The behaviour of the three discussed filters is compared by means of the examples. P ractical aspects for the realization of power filters are also discussed.