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Sample records for wavelet transform tiwt

  1. Adaptive Wavelet Transforms

    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.

  2. 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.

  3. Lecture notes on wavelet transforms

    CERN Document Server

    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 ...

  4. 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

  5. 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.

  6. A new fractional wavelet transform

    Science.gov (United States)

    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.

  7. 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)

  8. 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.

  9. 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.

  10. 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.

  11. Transformer Protection Using the Wavelet Transform

    OpenAIRE

    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...

  12. 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)

  13. 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)

  14. A New Formula for the Inverse Wavelet Transform

    OpenAIRE

    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.

  15. Wavelet transform of generalized functions in K ′{Mp} spaces

    Indian Academy of Sciences (India)

    Using convolution theory in K{Mp} space we obtain bounded results for the wavelet transform. Calderón-type reproducing formula is derived in distribution sense as an application of the same. An inversion formula for the wavelet transform of generalized functions is established. Keywords. Continuous wavelet transform ...

  16. Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

    Science.gov (United States)

    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

  17. 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....

  18. 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.

  19. Comparison between wavelet and wavelet packet transform features for classification of faults in distribution system

    Science.gov (United States)

    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.

  20. Analysis on Behaviour of Wavelet Coefficient during Fault Occurrence in Transformer

    Science.gov (United States)

    Sreewirote, Bancha; Ngaopitakkul, Atthapol

    2018-03-01

    The protection system for transformer has play significant role in avoiding severe damage to equipment when disturbance occur and ensure overall system reliability. One of the methodology that widely used in protection scheme and algorithm is discrete wavelet transform. However, characteristic of coefficient under fault condition must be analyzed to ensure its effectiveness. So, this paper proposed study and analysis on wavelet coefficient characteristic when fault occur in transformer in both high- and low-frequency component from discrete wavelet transform. The effect of internal and external fault on wavelet coefficient of both fault and normal phase has been taken into consideration. The fault signal has been simulate using transmission connected to transformer experimental setup on laboratory level that modelled after actual system. The result in term of wavelet coefficient shown a clearly differentiate between wavelet characteristic in both high and low frequency component that can be used to further design and improve detection and classification algorithm that based on discrete wavelet transform methodology in the future.

  1. Regularization of EIT reconstruction based on multi-scales wavelet transforms

    Directory of Open Access Journals (Sweden)

    Gong Bo

    2016-09-01

    Full Text Available Electrical Impedance Tomography (EIT intends to obtain the conductivity distribution of a domain from the electrical boundary conditions. This is an ill-posed inverse problem usually solved on finite element meshes. Wavelet transforms are widely used for medical image reconstruction. However, because of the irregular form of the finite element meshes, the canonical wavelet transforms is impossible to perform on meshes. In this article, we present a framework that combines multi-scales wavelet transforms and finite element meshes by viewing meshes as undirected graphs and applying spectral graph wavelet transform on the meshes.

  2. Detection of seismic phases by wavelet transform. Dependence of its performance on wavelet functions; Wavelet henkan ni yoru jishinha no iso kenshutsu. Wavelet ni yoru sai

    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.

  3. 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.

  4. 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.

  5. 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

  6. Watermarking on 3D mesh based on spherical wavelet transform.

    Science.gov (United States)

    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.

  7. ECG denoising with adaptive bionic wavelet transform.

    Science.gov (United States)

    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.

  8. 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.

  9. 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

  10. Clifford Continuous Wavelet Transforms in Ll0,2 and Ll0,3

    International Nuclear Information System (INIS)

    Bernstein, S.

    2008-01-01

    We consider Clifford-valued functions defined on R n . From the viewpoint of square integrable group representations a continuous wavelet transform is an irreducible continuous unitary representation of the affin group on the real line but also on R n . We will demonstrate that different Clifford continuous wavelet transforms can be obtained inside the calculus with similar properties than the real valued transform. Nevertheless, the Clifford wavelet transform is neither just a special vector transform nor just a wavelet transform applied to each component of the Clifford-valued function.

  11. Analysis and removing noise from speech using wavelet transform

    Science.gov (United States)

    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.

  12. Comparative study on γ energy spectrum denoise by fourier and wavelet transforms

    International Nuclear Information System (INIS)

    Shi Dongsheng; Di Yuming; Zhou Chunlin

    2007-01-01

    This paper introduces the basic principle of wavelet and Fourier transforms, applies wavelet transform method to denoise γ energy spectrum of 60 Co and compares it with Fourier transform method. The result of simulation with MATLAB software tool showed that as compared with traditional Fourier transform, wavelet transform has comparatively higher accuracy for γ energy spectrum denoising and is more feasible to γ energy spectrum denoising. (authors)

  13. 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.

  14. Directional dual-tree rational-dilation complex wavelet transform.

    Science.gov (United States)

    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.

  15. Multiresolution signal decomposition transforms, subbands, and wavelets

    CERN Document Server

    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

  16. Comparison of Fourier transform and continuous wavelet transform to study echo-planar imaging flow maps

    Energy Technology Data Exchange (ETDEWEB)

    Rodriguez G, A.; Bowtell, R.; Mansfield, P. [Area de Procesamiento Digital de Senales e Imagenes Biomedicas. Universidad Autonoma Metropolitana Iztapalapa. Mexico D.F. 09340 Mexico (Mexico)

    1998-12-31

    Velocity maps were studied combining Doyle and Mansfield method (1986) with each of the following transforms: Fourier, window Fourier and wavelet (Mexican hat). Continuous wavelet transform was compared against the two Fourier transform to determine which technique is best suited to study blood maps generated by Half Fourier Echo-Planar Imaging. Coefficient images were calculated and plots of the pixel intensity variation are presented. Finally, contour maps are shown to visualize the behavior of the blood flow in the cardiac chambers for the wavelet technique. (Author)

  17. Comparison of Fourier transform and continuous wavelet transform to study echo-planar imaging flow maps

    International Nuclear Information System (INIS)

    Rodriguez G, A.; Bowtell, R.; Mansfield, P.

    1998-01-01

    Velocity maps were studied combining Doyle and Mansfield method (1986) with each of the following transforms: Fourier, window Fourier and wavelet (Mexican hat). Continuous wavelet transform was compared against the two Fourier transform to determine which technique is best suited to study blood maps generated by Half Fourier Echo-Planar Imaging. Coefficient images were calculated and plots of the pixel intensity variation are presented. Finally, contour maps are shown to visualize the behavior of the blood flow in the cardiac chambers for the wavelet technique. (Author)

  18. 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

  19. Using the Dual-Tree Complex Wavelet Transform for Improved Fabric Defect Detection

    Directory of Open Access Journals (Sweden)

    Hermanus Vermaak

    2016-01-01

    Full Text Available The dual-tree complex wavelet transform (DTCWT solves the problems of shift variance and low directional selectivity in two and higher dimensions found with the commonly used discrete wavelet transform (DWT. It has been proposed for applications such as texture classification and content-based image retrieval. In this paper, the performance of the dual-tree complex wavelet transform for fabric defect detection is evaluated. As experimental samples, the fabric images from TILDA, a textile texture database from the Workgroup on Texture Analysis of the German Research Council (DFG, are used. The mean energies of real and imaginary parts of complex wavelet coefficients taken separately are identified as effective features for the purpose of fabric defect detection. Then it is shown that the use of the dual-tree complex wavelet transform yields greater performance as compared to the undecimated wavelet transform (UDWT with a detection rate of 4.5% to 15.8% higher depending on the fabric type.

  20. 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)

  1. 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.

  2. 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...

  3. 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.

  4. Discrete wavelet transform: a tool in smoothing kinematic data.

    Science.gov (United States)

    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.

  5. 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.

  6. Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings

    Science.gov (United States)

    Wang, Dong; Zhao, Yang; Yi, Cai; Tsui, Kwok-Leung; Lin, Jianhui

    2018-02-01

    Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing fault diagnosis have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong vibration components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for fault diagnosis of rolling element bearings. Industrial bearing fault signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.

  7. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

    Energy Technology Data Exchange (ETDEWEB)

    Kingsbury, J Ng and N G [Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ (United Kingdom)

    2004-02-06

    This book provides an overview of the theory and practice of continuous and discrete wavelet transforms. Divided into seven chapters, the first three chapters of the book are introductory, describing the various forms of the wavelet transform and their computation, while the remaining chapters are devoted to applications in fluids, engineering, medicine and miscellaneous areas. Each chapter is well introduced, with suitable examples to demonstrate key concepts. Illustrations are included where appropriate, thus adding a visual dimension to the text. A noteworthy feature is the inclusion, at the end of each chapter, of a list of further resources from the academic literature which the interested reader can consult. The first chapter is purely an introduction to the text. The treatment of wavelet transforms begins in the second chapter, with the definition of what a wavelet is. The chapter continues by defining the continuous wavelet transform and its inverse and a description of how it may be used to interrogate signals. The continuous wavelet transform is then compared to the short-time Fourier transform. Energy and power spectra with respect to scale are also discussed and linked to their frequency counterparts. Towards the end of the chapter, the two-dimensional continuous wavelet transform is introduced. Examples of how the continuous wavelet transform is computed using the Mexican hat and Morlet wavelets are provided throughout. The third chapter introduces the discrete wavelet transform, with its distinction from the discretized continuous wavelet transform having been made clear at the end of the second chapter. In the first half of the chapter, the logarithmic discretization of the wavelet function is described, leading to a discussion of dyadic grid scaling, frames, orthogonal and orthonormal bases, scaling functions and multiresolution representation. The fast wavelet transform is introduced and its computation is illustrated with an example using the Haar

  8. 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.

  9. Reversible Integer Wavelet Transform for the Joint of Image Encryption and Watermarking

    Directory of Open Access Journals (Sweden)

    Bin Wang

    2015-01-01

    Full Text Available In recent years, signal processing in the encrypted domain has attracted considerable research interest, especially embedding watermarking in encrypted image. In this work, a novel joint of image encryption and watermarking based on reversible integer wavelet transform is proposed. Firstly, the plain-image is encrypted by chaotic maps and reversible integer wavelet transform. Then the lossless watermarking is embedded in the encrypted image by reversible integer wavelet transform and histogram modification. Finally an encrypted image containing watermarking is obtained by the inverse integer wavelet transform. What is more, the original image and watermarking can be completely recovered by inverse process. Numerical experimental results and comparing with previous works show that the proposed scheme possesses higher security and embedding capacity than previous works. It is suitable for protecting the image information.

  10. A wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry

    Science.gov (United States)

    Wang, Jianhua; Yang, Yanxi

    2018-05-01

    We present a new wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry (2-D WTP). First of all, the maximum value point is extracted from two-dimensional wavelet transform coefficient modulus, and the local extreme value points over 90% of maximum value are also obtained, they both constitute wavelet ridge candidates. Then, the gradient of rotate factor is introduced into the Abid's cost function, and the logarithmic Logistic model is used to adjust and improve the cost function weights so as to obtain more reasonable value estimation. At last, the dynamic programming method is used to accurately find the optimal wavelet ridge, and the wrapped phase can be obtained by extracting the phase at the ridge. Its advantage is that, the fringe pattern with low signal-to-noise ratio can be demodulated accurately, and its noise immunity will be better. Meanwhile, only one fringe pattern is needed to projected to measured object, so dynamic three-dimensional (3-D) measurement in harsh environment can be realized. Computer simulation and experimental results show that, for the fringe pattern with noise pollution, the 3-D surface recovery accuracy by the proposed algorithm is increased. In addition, the demodulation phase accuracy of Morlet, Fan and Cauchy mother wavelets are compared.

  11. The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

    International Nuclear Information System (INIS)

    Kingsbury, J Ng and N G

    2004-01-01

    This book provides an overview of the theory and practice of continuous and discrete wavelet transforms. Divided into seven chapters, the first three chapters of the book are introductory, describing the various forms of the wavelet transform and their computation, while the remaining chapters are devoted to applications in fluids, engineering, medicine and miscellaneous areas. Each chapter is well introduced, with suitable examples to demonstrate key concepts. Illustrations are included where appropriate, thus adding a visual dimension to the text. A noteworthy feature is the inclusion, at the end of each chapter, of a list of further resources from the academic literature which the interested reader can consult. The first chapter is purely an introduction to the text. The treatment of wavelet transforms begins in the second chapter, with the definition of what a wavelet is. The chapter continues by defining the continuous wavelet transform and its inverse and a description of how it may be used to interrogate signals. The continuous wavelet transform is then compared to the short-time Fourier transform. Energy and power spectra with respect to scale are also discussed and linked to their frequency counterparts. Towards the end of the chapter, the two-dimensional continuous wavelet transform is introduced. Examples of how the continuous wavelet transform is computed using the Mexican hat and Morlet wavelets are provided throughout. The third chapter introduces the discrete wavelet transform, with its distinction from the discretized continuous wavelet transform having been made clear at the end of the second chapter. In the first half of the chapter, the logarithmic discretization of the wavelet function is described, leading to a discussion of dyadic grid scaling, frames, orthogonal and orthonormal bases, scaling functions and multiresolution representation. The fast wavelet transform is introduced and its computation is illustrated with an example using the Haar

  12. Discrimination Between Inrush and Short Circuit Currents in Differential Protection of Power Transformer Based on Correlation Method Using the Wavelet Transform

    OpenAIRE

    M. Rasoulpoor; M. Banejad; A. Ahmadyfard

    2011-01-01

    This paper presents a novel technique for transformer differential protection to prevent incorrect operation due to inrush current. The proposed method in this paper is based on time-frequency transform known as the Wavelet transform. The discrete Wavelet transform is used for analysis the differential current signals in time and frequency domains. The investigation on the energy distribution of the signal on the discrete Wavelet transform components shows the difference distribution between ...

  13. 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

  14. Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography

    Science.gov (United States)

    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.

  15. Protein structure analysis using the resonant recognition model and wavelet transforms

    International Nuclear Information System (INIS)

    Fang, Q.; Cosic, I.

    1998-01-01

    An approach based on the resonant recognition model and the discrete wavelet transform is introduced here for characterising proteins' biological function. The protein sequence is converted into a numerical series by assigning the electron-ion interaction potential to each amino acid from N-terminal to C-terminal. A set of peaks is found after performing a wavelet transform onto a numerical series representing a group of homologous proteins. These peaks are related to protein structural and functional properties and named characteristic vector of that protein group. Further more, the amino acids contributing mostly to a protein's biological functions, the so-called 'hot spots' amino acids, are predicted by the continuous wavelet transform. It is found that the hot spots are clustered around the protein's cleft structure. The wavelets approach provides a novel methods for amino acid sequence analysis as well as an expansion for the newly established macromolecular interaction model: the resonant recognition model. Copyright (1998) Australasian Physical and Engineering Sciences in Medicine

  16. Application of Bipartite Entangled States to Quantum Mechanical Version of Complex Wavelet Transforms

    International Nuclear Information System (INIS)

    Fan Hongyi; Lu Hailiang; Xu Xuefen

    2006-01-01

    We introduce the bipartite entangled states to present a quantum mechanical version of complex wavelet transform. Using the technique of integral within an ordered product of operators we show that the complex wavelet transform can be studied in terms of various quantum state vectors in two-mode Fock space. In this way the creterion for mother wavelet can be examined quantum-mechanically and therefore more deeply.

  17. Towards discrete wavelet transform-based human activity recognition

    Science.gov (United States)

    Khare, Manish; Jeon, Moongu

    2017-06-01

    Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

  18. Improvement of electrocardiogram by empirical wavelet transform

    Science.gov (United States)

    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.

  19. Implementation of 2D Discrete Wavelet Transform by Number Theoretic Transform and 2D Overlap-Save Method

    Directory of Open Access Journals (Sweden)

    Lina Yang

    2014-01-01

    Full Text Available To reduce the computation complexity of wavelet transform, this paper presents a novel approach to be implemented. It consists of two key techniques: (1 fast number theoretic transform(FNTT In the FNTT, linear convolution is replaced by the circular one. It can speed up the computation of 2D discrete wavelet transform. (2 In two-dimensional overlap-save method directly calculating the FNTT to the whole input sequence may meet two difficulties; namely, a big modulo obstructs the effective implementation of the FNTT and a long input sequence slows the computation of the FNTT down. To fight with such deficiencies, a new technique which is referred to as 2D overlap-save method is developed. Experiments have been conducted. The fast number theoretic transform and 2D overlap-method have been used to implement the dyadic wavelet transform and applied to contour extraction in pattern recognition.

  20. A feasibility study on wavelet transform for reactivity coefficient estimation

    International Nuclear Information System (INIS)

    Shimazu, Yoichiro

    2000-01-01

    Recently, a new method using Fourier transform has been introduced in place of the conventional method in order to reduce the time required for the measurement of moderator temperature coefficient in domestic PWRs. The basic concept of these methods is to eliminate noise in the reactivity signal. From this point of view, wavelet analysis is also known as an effective method. In this paper, we tried to apply this method to estimate reactivity coefficients of a nuclear reactor. The basic idea of the reactivity coefficient estimation is to analyze the ratios themselves of the corresponding expansion coefficients of the wavelet transform of the signals of reactivity and the relevant parameter. The concept requires no inverse wavelet transform. Based on numerical simulations, it is found that the method can reasonably estimate reactivity coefficient, for example moderator temperature coefficient, with less length of time sequence data than those required for Fourier transform method. We will continue this study to examine the validity of the estimation procedure for the actual reactor data and further to estimate the other reactivity coefficients. (author)

  1. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    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.

  2. Study of low insertion loss and miniaturization wavelet transform and inverse transform processor using SAW devices.

    Science.gov (United States)

    Jiang, Hua; Lu, Wenke; Zhang, Guoan

    2013-07-01

    In this paper, we propose a low insertion loss and miniaturization wavelet transform and inverse transform processor using surface acoustic wave (SAW) devices. The new SAW wavelet transform devices (WTDs) use the structure with two electrode-widths-controlled (EWC) single phase unidirectional transducers (SPUDT-SPUDT). This structure consists of the input withdrawal weighting interdigital transducer (IDT) and the output overlap weighting IDT. Three experimental devices for different scales 2(-1), 2(-2), and 2(-3) are designed and measured. The minimum insertion loss of the three devices reaches 5.49dB, 4.81dB, and 5.38dB respectively which are lower than the early results. Both the electrode width and the number of electrode pairs are reduced, thus making the three devices much smaller than the early devices. Therefore, the method described in this paper is suitable for implementing an arbitrary multi-scale low insertion loss and miniaturization wavelet transform and inverse transform processor using SAW devices. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. Multi-Resolution Wavelet-Transformed Image Analysis of Histological Sections of Breast Carcinomas

    Directory of Open Access Journals (Sweden)

    Hae-Gil Hwang

    2005-01-01

    Full Text Available Multi-resolution images of histological sections of breast cancer tissue were analyzed using texture features of Haar- and Daubechies transform wavelets. Tissue samples analyzed were from ductal regions of the breast and included benign ductal hyperplasia, ductal carcinoma in situ (DCIS, and invasive ductal carcinoma (CA. To assess the correlation between computerized image analysis and visual analysis by a pathologist, we created a two-step classification system based on feature extraction and classification. In the feature extraction step, we extracted texture features from wavelet-transformed images at 10× magnification. In the classification step, we applied two types of classifiers to the extracted features, namely a statistics-based multivariate (discriminant analysis and a neural network. Using features from second-level Haar transform wavelet images in combination with discriminant analysis, we obtained classification accuracies of 96.67 and 87.78% for the training and testing set (90 images each, respectively. We conclude that the best classifier of carcinomas in histological sections of breast tissue are the texture features from the second-level Haar transform wavelet images used in a discriminant function.

  4. Application of 3D wavelet transforms for crack detection in rotor ...

    Indian Academy of Sciences (India)

    Vijayawada 520 007. bAll India Council for Technical Education (AICTE), New Delhi 110 001 ... rotor system the transient analysis has been applied. ... In the present work a new wavelet plot called cross wavelet transform (XWT) has been.

  5. BOOK REVIEW: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

    Science.gov (United States)

    Ng, J.; Kingsbury, N. G.

    2004-02-01

    This book provides an overview of the theory and practice of continuous and discrete wavelet transforms. Divided into seven chapters, the first three chapters of the book are introductory, describing the various forms of the wavelet transform and their computation, while the remaining chapters are devoted to applications in fluids, engineering, medicine and miscellaneous areas. Each chapter is well introduced, with suitable examples to demonstrate key concepts. Illustrations are included where appropriate, thus adding a visual dimension to the text. A noteworthy feature is the inclusion, at the end of each chapter, of a list of further resources from the academic literature which the interested reader can consult. The first chapter is purely an introduction to the text. The treatment of wavelet transforms begins in the second chapter, with the definition of what a wavelet is. The chapter continues by defining the continuous wavelet transform and its inverse and a description of how it may be used to interrogate signals. The continuous wavelet transform is then compared to the short-time Fourier transform. Energy and power spectra with respect to scale are also discussed and linked to their frequency counterparts. Towards the end of the chapter, the two-dimensional continuous wavelet transform is introduced. Examples of how the continuous wavelet transform is computed using the Mexican hat and Morlet wavelets are provided throughout. The third chapter introduces the discrete wavelet transform, with its distinction from the discretized continuous wavelet transform having been made clear at the end of the second chapter. In the first half of the chapter, the logarithmic discretization of the wavelet function is described, leading to a discussion of dyadic grid scaling, frames, orthogonal and orthonormal bases, scaling functions and multiresolution representation. The fast wavelet transform is introduced and its computation is illustrated with an example using the Haar

  6. Information retrieval system utilizing wavelet transform

    Science.gov (United States)

    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.

  7. Wavelet-transform-based time–frequency domain reflectometry for reduction of blind spot

    International Nuclear Information System (INIS)

    Lee, Sin Ho; Park, Jin Bae; Choi, Yoon Ho

    2012-01-01

    In this paper, wavelet-transform-based time–frequency domain reflectometry (WTFDR) is proposed to reduce the blind spot in reflectometry. TFDR has a blind spot problem when the time delay between the reference signal and the reflected signal is short enough compared with the time duration of the reference signal. To solve the blind spot problem, the wavelet transform (WT) is used because the WT has linearity. Using the characteristics of the WT, the overlapped reference signal at the measured signal can be separated and the blind spot is reduced by obtaining the difference of the wavelet coefficients for the reference and reflected signals. In the proposed method, the complex wavelet is utilized as a mother wavelet because the reference signal in WTFDR has a complex form. Finally, the computer simulations and the real experiments are carried out to confirm the effectiveness and accuracy of the proposed method. (paper)

  8. Discrete Fourier and wavelet transforms an introduction through linear algebra with applications to signal processing

    CERN Document Server

    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.

  9. Short-term data forecasting based on wavelet transformation and chaos theory

    Science.gov (United States)

    Wang, Yi; Li, Cunbin; Zhang, Liang

    2017-09-01

    A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

  10. LOW COMPLEXITY HYBRID LOSSY TO LOSSLESS IMAGE CODER WITH COMBINED ORTHOGONAL POLYNOMIALS TRANSFORM AND INTEGER WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    R. Krishnamoorthy

    2012-05-01

    Full Text Available In this paper, a new lossy to lossless image coding scheme combined with Orthogonal Polynomials Transform and Integer Wavelet Transform is proposed. The Lifting Scheme based Integer Wavelet Transform (LS-IWT is first applied on the image in order to reduce the blocking artifact and memory demand. The Embedded Zero tree Wavelet (EZW subband coding algorithm is used in this proposed work for progressive image coding which achieves efficient bit rate reduction. The computational complexity of lower subband coding of EZW algorithm is reduced in this proposed work with a new integer based Orthogonal Polynomials transform coding. The normalization and mapping are done on the subband of the image for exploiting the subjective redundancy and the zero tree structure is obtained for EZW coding and so the computation complexity is greatly reduced in this proposed work. The experimental results of the proposed technique also show that the efficient bit rate reduction is achieved for both lossy and lossless compression when compared with existing techniques.

  11. The use of wavelet transforms in the solution of two-phase flow problems

    International Nuclear Information System (INIS)

    Moridis, G.J.; Nikolaou, M.; You, Yong

    1994-10-01

    In this paper we present the use of wavelets to solve the nonlinear Partial Differential.Equation (PDE) of two-phase flow in one dimension. The wavelet transforms allow a drastically different approach in the discretization of space. In contrast to the traditional trigonometric basis functions, wavelets approximate a function not by cancellation but by placement of wavelets at appropriate locations. When an abrupt chance, such as a shock wave or a spike, occurs in a function, only local coefficients in a wavelet approximation will be affected. The unique feature of wavelets is their Multi-Resolution Analysis (MRA) property, which allows seamless investigational any spatial resolution. The use of wavelets is tested in the solution of the one-dimensional Buckley-Leverett problem against analytical solutions and solutions obtained from standard numerical models. Two classes of wavelet bases (Daubechies and Chui-Wang) and two methods (Galerkin and collocation) are investigated. We determine that the Chui-Wang, wavelets and a collocation method provide the optimum wavelet solution for this type of problem. Increasing the resolution level improves the accuracy of the solution, but the order of the basis function seems to be far less important. Our results indicate that wavelet transforms are an effective and accurate method which does not suffer from oscillations or numerical smearing in the presence of steep fronts

  12. Improved Real-time Denoising Method Based on Lifting Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Liu Zhaohua

    2014-06-01

    Full Text Available Signal denoising can not only enhance the signal to noise ratio (SNR but also reduce the effect of noise. In order to satisfy the requirements of real-time signal denoising, an improved semisoft shrinkage real-time denoising method based on lifting wavelet transform was proposed. The moving data window technology realizes the real-time wavelet denoising, which employs wavelet transform based on lifting scheme to reduce computational complexity. Also hyperbolic threshold function and recursive threshold computing can ensure the dynamic characteristics of the system, in addition, it can improve the real-time calculating efficiency as well. The simulation results show that the semisoft shrinkage real-time denoising method has quite a good performance in comparison to the traditional methods, namely soft-thresholding and hard-thresholding. Therefore, this method can solve more practical engineering problems.

  13. Study on critical heat flux based on wavelet transform in rectangular narrow channels

    International Nuclear Information System (INIS)

    Zhou Tao; Ju Zhongyun; Zhang Lei; Li Jingjing; Sheng Cheng; Xiao Zejun

    2014-01-01

    Critical heat flux is very important for nuclear reactor safety, and observing temperature rise rate is a feasible method. Through using the wavelet transform to analyze the CHF temperature rise curves in rectangular narrow channels, it can remove relative weaker interference and effectively judge CHF. Rectangular narrow channel can strengthen heat transfer and reduce CHF, whose characteristics are proved by, temperature rise curves analyzed by wavelet transform. Respectively applying Daubechies function and Haar function is for guarantee the accuracy of the wavelet analysis, and Daubechies function is more accurate than Haar function in the detail signal processing from results. While the wavelet analysis and experimental results are compared and found in good agreement with the experimental results. (authors)

  14. 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

  15. Wavelet transform-vector quantization compression of supercomputer ocean model simulation output

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J N; Brislawn, C M

    1992-11-12

    We describe a new procedure for efficient compression of digital information for storage and transmission purposes. The algorithm involves a discrete wavelet transform subband decomposition of the data set, followed by vector quantization of the wavelet transform coefficients using application-specific vector quantizers. The new vector quantizer design procedure optimizes the assignment of both memory resources and vector dimensions to the transform subbands by minimizing an exponential rate-distortion functional subject to constraints on both overall bit-rate and encoder complexity. The wavelet-vector quantization method, which originates in digital image compression. is applicable to the compression of other multidimensional data sets possessing some degree of smoothness. In this paper we discuss the use of this technique for compressing the output of supercomputer simulations of global climate models. The data presented here comes from Semtner-Chervin global ocean models run at the National Center for Atmospheric Research and at the Los Alamos Advanced Computing Laboratory.

  16. Methods of compression of digital holograms, based on 1-level wavelet transform

    International Nuclear Information System (INIS)

    Kurbatova, E A; Cheremkhin, P A; Evtikhiev, N N

    2016-01-01

    To reduce the size of memory required for storing information about 3D-scenes and to decrease the rate of hologram transmission, digital hologram compression can be used. Compression of digital holograms by wavelet transforms is among most powerful methods. In the paper the most popular wavelet transforms are considered and applied to the digital hologram compression. Obtained values of reconstruction quality and hologram's diffraction efficiencies are compared. (paper)

  17. Feasibility Study for Applicability of the Wavelet Transform to Code Accuracy Quantification

    International Nuclear Information System (INIS)

    Kim, Jong Rok; Choi, Ki Yong

    2012-01-01

    A purpose of the assessment process of large thermal-hydraulic system codes is verifying their quality by comparing code predictions against experimental data. This process is essential for reliable safety analysis of nuclear power plants. Extensive experimental programs have been conducted in order to support the development and validation activities of best estimate thermal-hydraulic codes. So far, the Fast Fourier Transform Based Method (FFTBM) has been used widely for quantification of the prediction accuracy regardless of its limitation that it does not provide any time resolution for a local event. As alternative options, several time windowing methods (running average, short time Fourier transform, and etc.) can be utilized, but such time windowing methods also have a limitation of a fixed resolution. This limitation can be overcome by a wavelet transform because the resolution of the wavelet transform effectively varies in the time-frequency plane depending on choice of basic functions which are not necessarily sinusoidal. In this study, a feasibility of a new code accuracy quantification methodology using the wavelet transform is pursued

  18. Standard filter approximations for low power Continuous Wavelet Transforms.

    Science.gov (United States)

    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.

  19. Study on critical heat flux based on wavelet transform in rectangular narrow channels

    International Nuclear Information System (INIS)

    Zhou Tao; Ju Zhongyun; Zhang Lei; Li Jingjing; Sheng Cheng; Xiao Zejun

    2014-01-01

    Critical heat flux is very important for the safety of nuclear reactor, and observing temperature rise rate is a feasible method. The wavelet transform is used to analyze the CHF temperature rise curves in rectangular narrow channels, which can remove relative weaker interference and effectively judge CHF. Rectangular narrow channel can strengthen heat transfer and reduce CHF, whose characteristics are proved by temperature rise curves analyzed by wavelet transform. Respectively applying Daubechies function and Haar function is to guarantee the accuracy of the wavelet analysis, and Daubechies function is more accurate than Haar function in the detail signal processing from results. While the wavelet analysis and experimental results are compared and found in good agreement with the experimental results. (authors)

  20. Research of generalized wavelet transformations of Haar correctness in remote sensing of the Earth

    Science.gov (United States)

    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.

  1. Identification Method of Mud Shale Fractures Base on Wavelet Transform

    Science.gov (United States)

    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.

  2. Estimation of moderator temperature coefficient of actual PWRs using wavelet transform

    International Nuclear Information System (INIS)

    Katsumata, Ryosuke; Shimazu, Yoichiro

    2001-01-01

    Recently, an applicability of wavelet transform for estimation of moderator temperature coefficient was shown in numerical simulations. The basic concept of the wavelet transform is to eliminate noise in the measured signals. The concept is similar to that of Fourier transform method in which the analyzed reactivity component is divided by the analyzed component of relevant parameter. In order to apply the method to analyze measured data in actual PWRs, we carried out numerical simulations on the data that were more similar to actual data and proposed a method for estimation of moderator temperature coefficient using the wavelet transform. In the numerical simulations we obtained moderator temperature coefficients with the relative error of less than 4%. Based on this result we applied this method to analyze measured data in actual PWRs and the results have proved that the method is applicable for estimation of moderator temperature coefficients in the actual PWRs. It is expected that this method can reduce the required data length during the measurement. We expect to expand the applicability of this method to estimate the other reactivity coefficients with the data of short transient. (author)

  3. 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.

  4. Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

    Science.gov (United States)

    Reena Benjamin, J; Jayasree, T

    2018-02-01

    In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

  5. Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

    Directory of Open Access Journals (Sweden)

    R. Hajiabadi

    2016-10-01

    Full Text Available Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated. Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part and second, high-frequency part (detail will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of

  6. Implementation of the 2-D Wavelet Transform into FPGA for Image

    Science.gov (United States)

    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.

  7. 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.

  8. Application of the wavelet transform for speech processing

    Science.gov (United States)

    Maes, Stephane

    1994-01-01

    Speaker identification and word spotting will shortly play a key role in space applications. An approach based on the wavelet transform is presented that, in the context of the 'modulation model,' enables extraction of speech features which are used as input for the classification process.

  9. Wavelet Transformation for Damage Identication in Wind Turbine Blades

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Skov, Jonas falk; Kirkegaard, Poul Henning

    2014-01-01

    The present paper documents a proposed modal and wavelet analysis-based structural health monitoring (SHM) method for damage identification in wind turbine blades. A finite element (FE) model of a full-scale wind turbine blade is developed and introduced to a transverse surface crack. Hereby, post......-damage mode shapes are derived through modal analysis and subsequently analyzed with continuous two-dimensional wavelet transformation for damage identification, namely detection, localization and assessment. It is found that valid damage identification is obtained even when utilizing the mode shape...

  10. A novel algorithm for discrimination between inrush current and internal faults in power transformer differential protection based on discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Eldin, A.A. Hossam; Refaey, M.A. [Electrical Engineering Department, Alexandria University, Alexandria (Egypt)

    2011-01-15

    This paper proposes a novel methodology for transformer differential protection, based on wave shape recognition of the discriminating criterion extracted of the instantaneous differential currents. Discrete wavelet transform has been applied to the differential currents due to internal fault and inrush currents. The diagnosis criterion is based on median absolute deviation (MAD) of wavelet coefficients over a specified frequency band. The proposed algorithm is examined using various simulated inrush and internal fault current cases on a power transformer that has been modeled using electromagnetic transients program EMTDC software. Results of evaluation study show that, proposed wavelet based differential protection scheme can discriminate internal faults from inrush currents. (author)

  11. Simultaneous spectrophotometric determination of binary mixtures of surfactants using continuous wavelet transformation

    International Nuclear Information System (INIS)

    Afkhami, Abbas; Nematollahi, Davood; Madrakian, Tayyebeh; Abbasi-Tarighat, Maryam; Hajihadi, Mitra

    2009-01-01

    This work presents a simple, rapid, and novel method for simultaneous determination of binary mixtures of some surfactants using continuous wavelet transformation. The method is based on the difference in the effect of surfactants Cetyltrimethylammoniumbromide (CTAB), dodecyl trimethylammonium bromide (DTAB), cetylpyridinium bromide (CPB) and TritonX-100 (TX-100) on the absorption spectra of complex of Beryllium with Chrome Azurol S (CAS) at pH 5.4. Binary mixtures of CTAB-DTAB, DTAB-CPB and CTAB-TX-100 were analyzed without prior separation steps. Different mother wavelets from the family of continuous wavelet transforms were selected and applied under the optimal conditions for simultaneous determinations. The proposed methods, under the working conditions, were successfully applied to simultaneous determination of surfactants in hair conditioner and mouthwash samples.

  12. Application of the fourier and wavelet transforms in noise reduction of the out of the ordinary data

    International Nuclear Information System (INIS)

    Tafreshi, M. A.; Sadeghi, Y.

    2006-01-01

    In this article the noise reduction of the experimental data by the Fourier and the wavelet transforms has been investigated. Using both simulated and experimental data (from the plasma focus facility, Dena), the sensitive features of the application of the Fourier transform are visualized and discussed. Then, the main idea of the wavelet transform and the results of the noise reduction with this transform are presented. Due to this investigation, for the cases such as the current derivative of the Dena facility, where the reliability of the Fourier transform can be doubtful, the wavelet transform can be considered as a more accurate alternative approach

  13. Electrocardiogram de-noising based on forward wavelet transform ...

    Indian Academy of Sciences (India)

    Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed ... This technique permits to cancel noises and retain the informa- tion of the ... Wavelet analysis is used for transforming the signal under investigation into joined temporal and ... introduced the BWT in our proposed ECG de-noising system.

  14. Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

    Science.gov (United States)

    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.

  15. 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...

  16. Three-dimensional object recognitions from two-dimensional images using wavelet transforms and neural networks

    Science.gov (United States)

    Deschenes, Sylvain; Sheng, Yunlong; Chevrette, Paul C.

    1998-03-01

    3D object classification from 2D IR images is shown. The wavelet transform is used for edge detection. Edge tracking is used for removing noise effectively int he wavelet transform. The invariant Fourier descriptor is used to describe the contour curves. Invariance under out-of-plane rotation is achieved by the feature space trajectory neural network working as a classifier.

  17. Wavelet analysis

    CERN Document Server

    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...

  18. Identification of weak nonlinearities on damping and stiffness by the continuous wavelet transform

    Science.gov (United States)

    Ta, Minh-Nghi; Lardiès, Joseph

    2006-05-01

    We consider the free response of a nonlinear vibrating system. Using the ridges and skeletons of the continuous wavelet transform, we identify weak nonlinearities on damping and stiffness and estimate their physical parameters. The crucial choice of the son wavelet function is obtained using an optimization technique based on the entropy of the continuous wavelet transform. The method is applied to simulated single-degree-of-freedom systems and multi-degree-of-freedom systems with nonlinearities on damping and stiffness. Experimental validation of the nonlinear identification and parameter estimation method is presented. The experimental system is a clamped beam with nonlinearities on damping and stiffness and these nonlinearities are identified and quantified from a displacement sensor.

  19. Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    K. Parvathi

    2009-01-01

    Full Text Available The watershed transformation is a useful morphological segmentation tool for a variety of grey-scale images. However, over segmentation and under segmentation have become the key problems for the conventional algorithm. In this paper, an efficient segmentation method for high-resolution remote sensing image analysis is presented. Wavelet analysis is one of the most popular techniques that can be used to detect local intensity variation and hence the wavelet transformation is used to analyze the image. Wavelet transform is applied to the image, producing detail (horizontal, vertical, and diagonal and Approximation coefficients. The image gradient with selective regional minima is estimated with the grey-scale morphology for the Approximation image at a suitable resolution, and then the watershed is applied to the gradient image to avoid over segmentation. The segmented image is projected up to high resolutions using the inverse wavelet transform. The watershed segmentation is applied to small subset size image, demanding less computational time. We have applied our new approach to analyze remote sensing images. The algorithm was implemented in MATLAB. Experimental results demonstrated the method to be effective.

  20. Grating geophone signal processing based on wavelet transform

    Science.gov (United States)

    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.

  1. Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

    Science.gov (United States)

    Ergen, Burhan; Tatar, Yetkin; Gulcur, Halil Ozcan

    2012-01-01

    Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

  2. Human Body Image Edge Detection Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    李勇; 付小莉

    2003-01-01

    Human dresses are different in thousands way.Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to tte peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.

  3. The Use of Continuous Wavelet Transform Based on the Fast Fourier Transform in the Analysis of Multi-channel Electrogastrography Recordings.

    Science.gov (United States)

    Komorowski, Dariusz; Pietraszek, Stanislaw

    2016-01-01

    This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal.

  4. Wavelet Packet Transform Based Driver Distraction Level Classification Using EEG

    Directory of Open Access Journals (Sweden)

    Mousa Kadhim Wali

    2013-01-01

    Full Text Available We classify the driver distraction level (neutral, low, medium, and high based on different wavelets and classifiers using wireless electroencephalogram (EEG signals. 50 subjects were used for data collection using 14 electrodes. We considered for this research 4 distraction stimuli such as Global Position Systems (GPS, music player, short message service (SMS, and mental tasks. Deriving the amplitude spectrum of three different frequency bands theta, alpha, and beta of EEG signals was based on fusion of discrete wavelet packet transform (DWPT and FFT. Comparing the results of three different classifiers (subtractive fuzzy clustering probabilistic neural network, -nearest neighbor was based on spectral centroid, and power spectral features extracted by different wavelets (db4, db8, sym8, and coif5. The results of this study indicate that the best average accuracy achieved by subtractive fuzzy inference system classifier is 79.21% based on power spectral density feature extracted by sym8 wavelet which gave a good class discrimination under ANOVA test.

  5. 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.

  6. A REVIEW WAVELET TRANSFORM AND FUZZY K-MEANS BASED IMAGE DE-NOISING METHOD

    OpenAIRE

    Nidhi Patel*, Asst. Prof. Pratik Kumar Soni

    2017-01-01

    The research area of image processing technique using fuzzy k-means and wavelet transform. The enormous amount of data necessary for images is a main reason for the growth of many areas within the research field of computer imaging such as image processing and compression. In order to get this in requisites of the concerned research work, wavelet transforms and k-means clustering is applied. This can be done in order to discover more possible combinations that may lead to the finest de-noisin...

  7. Wavelet basics

    CERN Document Server

    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...

  8. Wavefield analysis in inhomogeneous media by wavelet transform; Wavelet henkan ni yoru fukinshitsu baitai no hadoba kaiseki

    Energy Technology Data Exchange (ETDEWEB)

    Matsushima, J; Rokugawa, S; Kato, Y [The University of Tokyo, Tokyo (Japan). Faculty of Engineering; Yokota, T; Miyazaki, T [Geological Survey of Japan, Tsukuba (Japan); Ichie, Y [The University of Tokyo, Tokyo (Japan)

    1996-10-01

    Data processing techniques have been investigated for clarifying structures and physical properties of geothermal reservoirs in the deep underground by seismic exploration using multiple wells. They include the initial motion time-distance tomography, amplitude tomography, diffracted wave tomography, and structure imaging using reflected wave or scattered wave. When applying these data processing methods to observed records, weak and minor signals essentially required are canceled due to averaging the analytical fields. In this study, influence of inhomogeneous media on the wavefield was evaluated. Data were analyzed considering frequency by using wavelet transform by which time-frequency can be easily analyzed. From the time-frequency analysis using wavelet transform, it was illustrated that high frequency scattered waves, generated by scatterer like cracks or by irregularity on the reflection surface, arrive behind direct P-wave and direct S-wave. 5 refs., 8 figs.

  9. Long memory analysis by using maximal overlapping discrete wavelet transform

    Science.gov (United States)

    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.

  10. Feature Extraction on Brain Computer Interfaces using Discrete Dyadic Wavelet Transform: Preliminary Results

    International Nuclear Information System (INIS)

    Gareis, I; Gentiletti, G; Acevedo, R; Rufiner, L

    2011-01-01

    The purpose of this work is to evaluate different feature extraction alternatives to detect the event related evoked potential signal on brain computer interfaces, trying to minimize the time employed and the classification error, in terms of sensibility and specificity of the method, looking for alternatives to coherent averaging. In this context the results obtained performing the feature extraction using discrete dyadic wavelet transform using different mother wavelets are presented. For the classification a single layer perceptron was used. The results obtained with and without the wavelet decomposition were compared; showing an improvement on the classification rate, the specificity and the sensibility for the feature vectors obtained using some mother wavelets.

  11. [Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

    Science.gov (United States)

    Li, Su-Yi; Ji, Yan-Ju; Liu, Wei-Yu; Wang, Zhi-Hong

    2013-04-01

    In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

  12. Application of the adaptive wavelet transform for analysis of blood flow oscillations in the human skin

    International Nuclear Information System (INIS)

    Tankanag, Arina; Chemeris, Nikolay

    2008-01-01

    An original method for the analysis of oscillations of cutaneous blood flow has been developed, which makes use of laser Doppler flowmetry (LDF) data and is based on the continuous wavelet transform and adaptive wavelet theory. The potential of the method has been demonstrated in experiments with the response of microcirculatory bed to the local linearly-increasing heating of a skin spot. The use of adaptive wavelet transform for analysis of peripheral blood flow oscillations enables one to process short (5 min) LDF signals in a wide frequency range (0.009-2 Hz). The major advantage of the method proposed, as compared to traditional wavelet analysis, has been shown to be a significant reduction of 'border effects'. This makes possible a correct low-frequency component analysis of much shorter LDF signals compared to those used in traditional wavelet processing.

  13. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    International Nuclear Information System (INIS)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun; Wang, Jianhui

    2010-01-01

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  14. SEGMENTATION OF POLARIMETRIC SAR IMAGES USIG WAVELET TRANSFORMATION AND TEXTURE FEATURES

    Directory of Open Access Journals (Sweden)

    A. Rezaeian

    2015-12-01

    Full Text Available Polarimetric Synthetic Aperture Radar (PolSAR sensors can collect useful observations from earth’s surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT. Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  15. Segmentation of Polarimetric SAR Images Usig Wavelet Transformation and Texture Features

    Science.gov (United States)

    Rezaeian, A.; Homayouni, S.; Safari, A.

    2015-12-01

    Polarimetric Synthetic Aperture Radar (PolSAR) sensors can collect useful observations from earth's surfaces and phenomena for various remote sensing applications, such as land cover mapping, change and target detection. These data can be acquired without the limitations of weather conditions, sun illumination and dust particles. As result, SAR images, and in particular Polarimetric SAR (PolSAR) are powerful tools for various environmental applications. Unlike the optical images, SAR images suffer from the unavoidable speckle, which causes the segmentation of this data difficult. In this paper, we use the wavelet transformation for segmentation of PolSAR images. Our proposed method is based on the multi-resolution analysis of texture features is based on wavelet transformation. Here, we use the information of gray level value and the information of texture. First, we produce coherency or covariance matrices and then generate span image from them. In the next step of proposed method is texture feature extraction from sub-bands is generated from discrete wavelet transform (DWT). Finally, PolSAR image are segmented using clustering methods as fuzzy c-means (FCM) and k-means clustering. We have applied the proposed methodology to full polarimetric SAR images acquired by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L-band system, during July, in 2012 over an agricultural area in Winnipeg, Canada.

  16. Video steganography based on bit-plane decomposition of wavelet-transformed video

    Science.gov (United States)

    Noda, Hideki; Furuta, Tomofumi; Niimi, Michiharu; Kawaguchi, Eiji

    2004-06-01

    This paper presents a steganography method using lossy compressed video which provides a natural way to send a large amount of secret data. The proposed method is based on wavelet compression for video data and bit-plane complexity segmentation (BPCS) steganography. BPCS steganography makes use of bit-plane decomposition and the characteristics of the human vision system, where noise-like regions in bit-planes of a dummy image are replaced with secret data without deteriorating image quality. In wavelet-based video compression methods such as 3-D set partitioning in hierarchical trees (SPIHT) algorithm and Motion-JPEG2000, wavelet coefficients in discrete wavelet transformed video are quantized into a bit-plane structure and therefore BPCS steganography can be applied in the wavelet domain. 3-D SPIHT-BPCS steganography and Motion-JPEG2000-BPCS steganography are presented and tested, which are the integration of 3-D SPIHT video coding and BPCS steganography, and that of Motion-JPEG2000 and BPCS, respectively. Experimental results show that 3-D SPIHT-BPCS is superior to Motion-JPEG2000-BPCS with regard to embedding performance. In 3-D SPIHT-BPCS steganography, embedding rates of around 28% of the compressed video size are achieved for twelve bit representation of wavelet coefficients with no noticeable degradation in video quality.

  17. A review on applications of the wavelet transform techniques in spectral analysis

    International Nuclear Information System (INIS)

    Medhat, M.E.; Albdel-hafiez, A.; Hassan, M.F.; Ali, M.A.; Awaad, Z.

    2004-01-01

    Starting from 1989, a new technique known as wavelet transforms (WT) has been applied successfully for analysis of different types of spectra. WT offers certain advantages over Fourier transforms for analysis of signals. A review of using this technique through different fields of elemental analysis is presented

  18. Use of switched capacitor filters to implement the discrete wavelet transform

    Science.gov (United States)

    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.

  19. Magnetomyographic recording and identification of uterine contractions using Hilbert-wavelet transforms

    International Nuclear Information System (INIS)

    Furdea, A; Wilson, J D; Eswaran, H; Lowery, C L; Govindan, R B; Preissl, H

    2009-01-01

    We propose a multi-stage approach using Wavelet and Hilbert transforms to identify uterine contraction bursts in magnetomyogram (MMG) signals measured using a 151 magnetic sensor array. In the first stage, we decompose the MMG signals by wavelet analysis into multilevel approximate and detail coefficients. In each level, the signals are reconstructed using the detail coefficients followed by the computation of the Hilbert transform. The Hilbert amplitude of the reconstructed signals from different frequency bands (0.1–1 Hz) is summed up over all the sensors to increase the signal-to-noise ratio. Using a novel clustering technique, affinity propagation, the contractile bursts are distinguished from the noise level. The method is applied on simulated MMG data, using a simple stochastic model to determine its robustness and to seven MMG datasets

  20. Block-based wavelet transform coding of mammograms with region-adaptive quantization

    Science.gov (United States)

    Moon, Nam Su; Song, Jun S.; Kwon, Musik; Kim, JongHyo; Lee, ChoongWoong

    1998-06-01

    To achieve both high compression ratio and information preserving, it is an efficient way to combine segmentation and lossy compression scheme. Microcalcification in mammogram is one of the most significant sign of early stage of breast cancer. Therefore in coding, detection and segmentation of microcalcification enable us to preserve it well by allocating more bits to it than to other regions. Segmentation of microcalcification is performed both in spatial domain and in wavelet transform domain. Peak error controllable quantization step, which is off-line designed, is suitable for medical image compression. For region-adaptive quantization, block- based wavelet transform coding is adopted and different peak- error-constrained quantizers are applied to blocks according to the segmentation result. In view of preservation of microcalcification, the proposed coding scheme shows better performance than JPEG.

  1. Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation

    Science.gov (United States)

    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.

  2. Phase retrieval from a single fringe pattern by using empirical wavelet transform

    International Nuclear Information System (INIS)

    Guo, Xiaopeng; Zhao, Hong; Wang, Xin

    2015-01-01

    Phase retrieval from a single fringe pattern is one of the key tasks in optical metrology. In this paper, we present a new method for phase retrieval from a single fringe pattern based on empirical wavelet transform. In the proposed method, a fringe pattern can be effectively divided into three components: nonuniform background, fringes and random noise, which are described in different sub-pass. So the phase distribution information can be robustly extracted from fringes representing a fundamental frequency component. In simulation and a practical projection fringes test, the performance of the present method is successfully verified by comparing with the conventional wavelet transform method in terms of both image quality and phase estimation errors. (paper)

  3. A Variation on Uncertainty Principle and Logarithmic Uncertainty Principle for Continuous Quaternion Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Mawardi Bahri

    2017-01-01

    Full Text Available The continuous quaternion wavelet transform (CQWT is a generalization of the classical continuous wavelet transform within the context of quaternion algebra. First of all, we show that the directional quaternion Fourier transform (QFT uncertainty principle can be obtained using the component-wise QFT uncertainty principle. Based on this method, the directional QFT uncertainty principle using representation of polar coordinate form is easily derived. We derive a variation on uncertainty principle related to the QFT. We state that the CQWT of a quaternion function can be written in terms of the QFT and obtain a variation on uncertainty principle related to the CQWT. Finally, we apply the extended uncertainty principles and properties of the CQWT to establish logarithmic uncertainty principles related to generalized transform.

  4. Use of muscle synergies and wavelet transforms to identify fatigue during squatting.

    Science.gov (United States)

    Smale, Kenneth B; Shourijeh, Mohammad S; Benoit, Daniel L

    2016-06-01

    The objective of this study was to supplement continuous wavelet transforms with muscle synergies in a fatigue analysis to better describe the combination of decreased firing frequency and altered activation profiles during dynamic muscle contractions. Nine healthy young individuals completed the dynamic tasks before and after they squatted with a standard Olympic bar until complete exhaustion. Electromyography (EMG) profiles were analyzed with a novel concatenated non-negative matrix factorization method that decomposed EMG signals into muscle synergies. Muscle synergy analysis provides the activation pattern of the muscles while continuous wavelet transforms output the temporal frequency content of the EMG signals. Synergy analysis revealed subtle changes in two-legged squatting after fatigue while differences in one-legged squatting were more pronounced and included the shift from a general co-activation of muscles in the pre-fatigue state to a knee extensor dominant weighting post-fatigue. Continuous wavelet transforms showed major frequency content decreases in two-legged squatting after fatigue while very few frequency changes occurred in one-legged squatting. It was observed that the combination of methods is an effective way of describing muscle fatigue and that muscle activation patterns play a very important role in maintaining the overall joint kinetics after fatigue. Copyright © 2016 Elsevier Ltd. All rights reserved.

  5. R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

    Science.gov (United States)

    Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

    2017-01-01

    Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

  6. Cutting force response in milling of Inconel: analysis by wavelet and Hilbert-Huang Transforms

    Directory of Open Access Journals (Sweden)

    Grzegorz Litak

    Full Text Available We study the milling process of Inconel. By continuously increasing the cutting depth we follow the system response and appearance of oscillations of larger amplitude. The cutting force amplitude and frequency analysis has been done by means of wavelets and Hilbert-Huang transform. We report that in our system the force oscillations are closely related to the rotational motion of the tool and advocate for a regenerative mechanism of chatter vibrations. To identify vibrations amplitudes occurrence in time scale we apply wavelet and Hilbert-Huang transforms.

  7. Weighted least squares phase unwrapping based on the wavelet transform

    Science.gov (United States)

    Chen, Jiafeng; Chen, Haiqin; Yang, Zhengang; Ren, Haixia

    2007-01-01

    The weighted least squares phase unwrapping algorithm is a robust and accurate method to solve phase unwrapping problem. This method usually leads to a large sparse linear equation system. Gauss-Seidel relaxation iterative method is usually used to solve this large linear equation. However, this method is not practical due to its extremely slow convergence. The multigrid method is an efficient algorithm to improve convergence rate. However, this method needs an additional weight restriction operator which is very complicated. For this reason, the multiresolution analysis method based on the wavelet transform is proposed. By applying the wavelet transform, the original system is decomposed into its coarse and fine resolution levels and an equivalent equation system with better convergence condition can be obtained. Fast convergence in separate coarse resolution levels speeds up the overall system convergence rate. The simulated experiment shows that the proposed method converges faster and provides better result than the multigrid method.

  8. Wavelets and their uses

    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)

  9. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    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.

  10. Pre-processing data using wavelet transform and PCA based on ...

    Indian Academy of Sciences (India)

    Abazar Solgi

    2017-07-14

    Jul 14, 2017 ... Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Abazar Solgi1,*, Amir Pourhaghi1, Ramin Bahmani2 and Heidar Zarei3. 1. Department of Water Resources Engineering, Shahid Chamran University of ...

  11. Wavelet transform and real-time learning method for myoelectric signal in motion discrimination

    International Nuclear Information System (INIS)

    Liu Haihua; Chen Xinhao; Chen Yaguang

    2005-01-01

    This paper discusses the applicability of the Wavelet transform for analyzing an EMG signal and discriminating motion classes. In many previous works, researchers have dealt with steady EMG and have proposed suitable analyzing methods for the EMG, for example FFT and STFT. Therefore, it is difficult for the previous approaches to discriminate motions from the EMG in the different phases of muscle activity, i.e., pre-activity, in activity, postactivity phases, as well as the period of motion transition from one to another. In this paper, we introduce the Wavelet transform using the Coiflet mother wavelet into our real-time EMG prosthetic hand controller for discriminating motions from steady and unsteady EMG. A preliminary experiment to discriminate three hand motions from four channel EMG in the initial pre-activity and in activity phase is carried out to show the effectiveness of the approach. However, future research efforts are necessary to discriminate more motions much precisely

  12. 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)

  13. 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.

  14. Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Bartosch, T. [Erlanger-Nuernberg Univ., Erlanger (Germany). Lehrstul fuer Nachrichtentechnik I; Seidl, D. [Seismologisches Zentralobservatorium Graefenberg, Erlanegen (Greece). Bundesanstalt fuer Geiwissenschaften und Rohstoffe

    1999-06-01

    Among a variety of spectrogram methods short-time Fourier transform (STFT) and continuous wavelet transform (CWT) were selected to analyse transients in non-stationary signals. Depending on the properties of the tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli (Italy).

  15. Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

    Science.gov (United States)

    Yu, Yali; Wang, Mengxia; Lima, Dimas

    2018-04-01

    In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

  16. Design and application of discrete wavelet packet transform based multiresolution controller for liquid level system.

    Science.gov (United States)

    Paul, Rimi; Sengupta, Anindita

    2017-11-01

    A new controller based on discrete wavelet packet transform (DWPT) for liquid level system (LLS) has been presented here. This controller generates control signal using node coefficients of the error signal which interprets many implicit phenomena such as process dynamics, measurement noise and effect of external disturbances. Through simulation results on LLS problem, this controller is shown to perform faster than both the discrete wavelet transform based controller and conventional proportional integral controller. Also, it is more efficient in terms of its ability to provide better noise rejection. To overcome the wind up phenomenon by considering the saturation due to presence of actuator, anti-wind up technique is applied to the conventional PI controller and compared to the wavelet packet transform based controller. In this case also, packet controller is found better than the other ones. This similar work has been extended for analogous first order RC plant as well as second order plant also. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  17. A study of renal blood flow regulation using the discrete wavelet transform

    Science.gov (United States)

    Pavlov, Alexey N.; Pavlova, Olga N.; Mosekilde, Erik; Sosnovtseva, Olga V.

    2010-02-01

    In this paper we provide a way to distinguish features of renal blood flow autoregulation mechanisms in normotensive and hypertensive rats based on the discrete wavelet transform. Using the variability of the wavelet coefficients we show distinctions that occur between the normal and pathological states. A reduction of this variability in hypertension is observed on the microscopic level of the blood flow in efferent arteriole of single nephrons. This reduction is probably associated with higher flexibility of healthy cardiovascular system.

  18. Simulasi Unjuk Kerja Discrete Wavelet Transform (DWT dan Discrete Cosine Transform (DCT untuk Pengolahan Sinyal Radar di Daerah yang Ber-Noise Tinggi

    Directory of Open Access Journals (Sweden)

    Raisah Hayati

    2014-03-01

    Full Text Available Detection of low signal and determination target locations is the basis and important in the system radar. Performance of radar can enhanced with enhancement signal-to-noise ratio in the receiver. In this research, will show a algorithm in radar signal processing, that is for extract the signal target in the place of noise. Discrete Cosine Transform (DCT and Discrete Wavelet Transform (DWT is the success full mathematic function in the signal processing in the last twenty years. In this research will simulate signal with DCT and DWT, analysis his performance in radar signal processing. DWT signal processing will analysis and compare with mother wavelet Haar, Daubechies-12, Coiflet-5 and Symlet-8. DCT signal processing will analysis and compare with same of window function with use in signal restrictions. Window function have influence signal resolution in domain frequency. Window function that use in this research Rectangular, Hamming, Hanning and Dolph-Chebyshev. The result of simulation and analysis Is: mother wavelet with DWT, wavelet Daubechies-12 and Symlet-8 give the best performance and mother wavelet Haar give bad performance. Wavelet Daubechies-12 give the biggest signal to noise ratio that is 32,0603 dB. Mother wavelet Symlet-8 give 32,6589 dB. Mother wavelet Haar give 14,6692 dB. Testing window function DCT, window Dolph-Chebyshev give the best performance, with give the best separation of signal. Analysis of signal reflection that accept of radar give the result that DWT is better performance than DCT in breaking of noise.

  19. Two-dimensional wavelet transform for reliability-guided phase unwrapping in optical fringe pattern analysis.

    Science.gov (United States)

    Li, Sikun; Wang, Xiangzhao; Su, Xianyu; Tang, Feng

    2012-04-20

    This paper theoretically discusses modulus of two-dimensional (2D) wavelet transform (WT) coefficients, calculated by using two frequently used 2D daughter wavelet definitions, in an optical fringe pattern analysis. The discussion shows that neither is good enough to represent the reliability of the phase data. The differences between the two frequently used 2D daughter wavelet definitions in the performance of 2D WT also are discussed. We propose a new 2D daughter wavelet definition for reliability-guided phase unwrapping of optical fringe pattern. The modulus of the advanced 2D WT coefficients, obtained by using a daughter wavelet under this new daughter wavelet definition, includes not only modulation information but also local frequency information of the deformed fringe pattern. Therefore, it can be treated as a good parameter that represents the reliability of the retrieved phase data. Computer simulation and experimentation show the validity of the proposed method.

  20. Numerical implementation of wavelet and fuzzy transform IFOC for three-phase induction motor

    Directory of Open Access Journals (Sweden)

    Sanjeevikumar Padmanaban

    2016-03-01

    Full Text Available This article elaborates the numerical implementation of a novel, indirect field-oriented control (IFOC for induction motor drive by wave-let discrete transform/fuzzy logic interface system unique combination. The feedback (speed error signal is a mixed component of multiple low and high frequencies. Further, these signals are decomposed by the discrete wave-let transform (WT, then fuzzy logic (FL generates the scaled gains for the proportional-integral (P-I controller parameters. This unique combination improves the high precision speed control of induction motor during both transient as well as steady-state conditions. Numerical simulation model is implemented with proposed control scheme using Matlab/Simulink software and obtained results confirm the expectation.

  1. Implementation in an FPGA circuit of Edge detection algorithm based on the Discrete Wavelet Transforms

    Science.gov (United States)

    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.

  2. Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

    Science.gov (United States)

    Zheng, Yang; Chen, Xihao; Zhu, Rui

    2017-07-01

    Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

  3. ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

    Science.gov (United States)

    El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

    2017-02-07

    Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

  4. Fast digital envelope detector based on generalized harmonic wavelet transform for BOTDR performance improvement

    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)

  5. BER IMPROVEMENT OF WIRELESS LAN IEEE 802.11 STANDARD USING WAVELET PACKET TRANSFORMS

    Directory of Open Access Journals (Sweden)

    Sanjeev Kumar

    2012-09-01

    Full Text Available High data rates and spectral efficiency is the main requirements for wireless communication systems. Orthogonal Frequency Division Multiplexing (OFDM is a special form of multi carrier transmission used to achieve high data rates of the various WLAN standards. WLAN uses an Inverse Fast Fourier Transform (IFFT at the transmitter to modulate a high bit-rate signal onto a number of carriers and ensure orthogonality between the carriers. The FFT-OFDM has a disadvantage that it is inherently inflexible and requires a complex IFFT core. Recently, Wavelet Packet Transform is proposed as an alternate to FFT. It is a multiplexing method in which data is assigned to wavelet sub bands having different time and frequency resolutions. This paper presents a BER analysis of Fourier-based OFDM (FFT-OFDM and Wavelet Packet based OFDM (WPT-OFDM in WLAN standard (IEEE 802.11a. The performance of FFT and WPT OFDM for various modulation techniques such as PSK, DPSK and QAM for varying values of M was evaluated in AWGN Channel.

  6. Analysis of the geomagnetic activity of the Dst index and self-affine fractals using wavelet transforms

    Directory of Open Access Journals (Sweden)

    H. L. Wei

    2004-01-01

    Full Text Available The geomagnetic activity of the Dst index is analyzed using wavelet transforms and it is shown that the Dst index possesses properties associated with self-affine fractals. For example, the power spectral density obeys a power-law dependence on frequency, and therefore the Dst index can be viewed as a self-affine fractal dynamic process. In fact, the behaviour of the Dst index, with a Hurst exponent H≈0.5 (power-law exponent β≈2 at high frequency, is similar to that of Brownian motion. Therefore, the dynamical invariants of the Dst index may be described by a potential Brownian motion model. Characterization of the geomagnetic activity has been studied by analysing the geomagnetic field using a wavelet covariance technique. The wavelet covariance exponent provides a direct effective measure of the strength of persistence of the Dst index. One of the advantages of wavelet analysis is that many inherent problems encountered in Fourier transform methods, such as windowing and detrending, are not necessary.

  7. Optimal wavelet transform for the detection of microaneurysms in retina photographs.

    Science.gov (United States)

    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.

  8. RAINFALL ANALYSIS IN KLANG RIVER BASIN USING CONTINUOUS WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Celso A. G. Santos

    2016-01-01

    Full Text Available The rainfall characteristics within Klang River basin is analyzed by the continuous wavelet transform using monthly rainfall data (1997–2009 from a raingauge and also using daily rainfall data (1998–2013 from the Tropical Rainfall Measuring Mission (TRMM. The wavelet power spectrum showed that some frequency components were presented within the rainfall time series, but the observed time series is short to provide accurate information, thus the daily TRMM rainfall data were used. In such analysis, two main frequency components, i.e., 6 and 12 months, showed to be present during the entire period of 16 years. Such semiannual and annual frequencies were confirmed by the global wavelet power spectra. Finally, the modulation in the 8–16-month and 256– 512-day bands were examined by an average of all scales between 8 and 16 months, and 256 and 512 days, respectively, giving a measure of the average monthly/daily variance versus time, where the periods with low or high variance could be identified.

  9. A New Perceptual Mapping Model Using Lifting Wavelet Transform

    OpenAIRE

    Taha TahaBasheer; Ehkan Phaklen; Ngadiran Ruzelita

    2017-01-01

    Perceptual mappingapproaches have been widely used in visual information processing in multimedia and internet of things (IOT) applications. Accumulative Lifting Difference (ALD) is proposed in this paper as texture mapping model based on low-complexity lifting wavelet transform, and combined with luminance masking for creating an efficient perceptual mapping model to estimate Just Noticeable Distortion (JND) in digital images. In addition to low complexity operations, experiments results sho...

  10. REMOTELY SENSEDC IMAGE COMPRESSION BASED ON WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Heung K. Lee

    1996-06-01

    Full Text Available In this paper, we present an image compression algorithm that is capable of significantly reducing the vast mount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet trans-form to remove the spatial redundancy. The transformed images are than encoded by hilbert-curve scanning and run-length-encoding, followed by huffman coding. We also present the performance of the proposed algorithm with KITSAT-1 image as well as the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by peak signal to noise ratio (PSNR and classification capability.

  11. Improved CEEMDAN-wavelet transform de-noising method and its application in well logging noise reduction

    Science.gov (United States)

    Zhang, Jingxia; Guo, Yinghai; Shen, Yulin; Zhao, Difei; Li, Mi

    2018-06-01

    The use of geophysical logging data to identify lithology is an important groundwork in logging interpretation. Inevitably, noise is mixed in during data collection due to the equipment and other external factors and this will affect the further lithological identification and other logging interpretation. Therefore, to get a more accurate lithological identification it is necessary to adopt de-noising methods. In this study, a new de-noising method, namely improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet transform, is proposed, which integrates the superiorities of improved CEEMDAN and wavelet transform. Improved CEEMDAN, an effective self-adaptive multi-scale analysis method, is used to decompose non-stationary signals as the logging data to obtain the intrinsic mode function (IMF) of N different scales and one residual. Moreover, one self-adaptive scale selection method is used to determine the reconstruction scale k. Simultaneously, given the possible frequency aliasing problem between adjacent IMFs, a wavelet transform threshold de-noising method is used to reduce the noise of the (k-1)th IMF. Subsequently, the de-noised logging data are reconstructed by the de-noised (k-1)th IMF and the remaining low-frequency IMFs and the residual. Finally, empirical mode decomposition, improved CEEMDAN, wavelet transform and the proposed method are applied for analysis of the simulation and the actual data. Results show diverse performance of these de-noising methods with regard to accuracy for lithological identification. Compared with the other methods, the proposed method has the best self-adaptability and accuracy in lithological identification.

  12. Robust electrocardiogram (ECG) beat classification using discrete wavelet transform

    International Nuclear Information System (INIS)

    Minhas, Fayyaz-ul-Amir Afsar; Arif, Muhammad

    2008-01-01

    This paper presents a robust technique for the classification of six types of heartbeats through an electrocardiogram (ECG). Features extracted from the QRS complex of the ECG using a wavelet transform along with the instantaneous RR-interval are used for beat classification. The wavelet transform utilized for feature extraction in this paper can also be employed for QRS delineation, leading to reduction in overall system complexity as no separate feature extraction stage would be required in the practical implementation of the system. Only 11 features are used for beat classification with the classification accuracy of ∼99.5% through a KNN classifier. Another main advantage of this method is its robustness to noise, which is illustrated in this paper through experimental results. Furthermore, principal component analysis (PCA) has been used for feature reduction, which reduces the number of features from 11 to 6 while retaining the high beat classification accuracy. Due to reduction in computational complexity (using six features, the time required is ∼4 ms per beat), a simple classifier and noise robustness (at 10 dB signal-to-noise ratio, accuracy is 95%), this method offers substantial advantages over previous techniques for implementation in a practical ECG analyzer

  13. Human Iris Recognition System using Wavelet Transform and LVQ

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kwan Yong; Lim, Shin Young [Electronics and Telecommunications Research Institute (Korea); Cho, Seong Won [Hongik University (Korea)

    2000-07-01

    The popular methods to check the identity of individuals include passwords and ID cards. These conventional methods for user identification and authentication are not altogether reliable because they can be stolen and forgotten. As an alternative of the existing methods, biometric technology has been paid much attention for the last few decades. In this paper, we propose an efficient system for recognizing the identity of a living person by analyzing iris patterns which have a high level of stability and distinctiveness than other biometric measurements. The proposed system is based on wavelet transform and a competitive neural network with the improved mechanisms. After preprocessing the iris data acquired through a CCD camera, feature vectors are extracted by using Haar wavelet transform. LVQ(Learning Vector Quantization) is exploited to classify these feature vectors. We improve the overall performance of the proposed system by optimizing the size of feature vectors and by introducing an efficient initialization of the weight vectors and a new method for determining the winner in order to increase the recognition accuracy of LVQ. From the experiments, we confirmed that the proposed system has a great potential of being applied to real applications in an efficient and effective way. (author). 14 refs., 13 figs., 7 tabs.

  14. Invariant 2D object recognition using the wavelet transform and structured neural networks

    Science.gov (United States)

    Khalil, Mahmoud I.; Bayoumi, Mohamed M.

    1999-03-01

    This paper applies the dyadic wavelet transform and the structured neural networks approach to recognize 2D objects under translation, rotation, and scale transformation. Experimental results are presented and compared with traditional methods. The experimental results showed that this refined technique successfully classified the objects and outperformed some traditional methods especially in the presence of noise.

  15. Investigating Multi-Array Antenna Signal Convergence using Wavelet Transform and Krylov Sequence

    Directory of Open Access Journals (Sweden)

    Muhammad Ahmed Sikander

    2018-01-01

    Full Text Available In the present world, wireless communication is becoming immensely popular for plethora of applications. Technology has been advancing at an accelerated rate leading to make communication reliable. Still, there are issues need to be address to minimize errors in the transmission. This research study expounds on the rapid convergence of the signal. Convergence is considered to be an important aspect in wireless communication. For rapid convergence, two ambiguities should be addressed; Eigenvalue spread and sparse identification or sparsity of the signal. Eigen value spread is defining as the ratio of minimum to maximum Eigenvalue, whereas sparsity is defining as the loosely bounded system. In this research, two of these attributes are investigated for MAA (Multi-Array Antenna signal using the cascading of Wavelet and Krylov processes. Specifically, the MAA signal is applied in the research because nowadays there are many physical hindrances in the communication path. These hurdles weaken the signal strength which in turn effects the quality of the reception. WT (Wavelet Transform is used to address the Eigenvalue problem and the Krylov sequence is used to attempt the sparse identification of the MAA signal. The results show that the convergence of the MMA signal is improved by applying Wavelet transform and Krylov Subspace.

  16. Investigating multi-array antenna signal convergence using wavelet transform and krylov sequence

    International Nuclear Information System (INIS)

    Sikander, M.A.; Hussain, R.; Hussain, R.

    2018-01-01

    In the present world, wireless communication is becoming immensely popular for plethora of applications. Technology has been advancing at an accelerated rate leading to make communication reliable. Still, there are issues need to be address to minimize errors in the transmission. This research study expounds on the rapid convergence of the signal. Convergence is considered to be an important aspect in wireless communication. For rapid convergence, two ambiguities should be addressed; Eigenvalue spread and sparse identification or sparsity of the signal. Eigen value spread is defining as the ratio of minimum to maximum Eigenvalue, whereas sparsity is defining as the loosely bounded system. In this research, two of these attributes are investigated for MAA (Multi-Array Antenna) signal using the cascading of Wavelet and Krylov processes. Specifically, the MAA signal is applied in the research because nowadays there are many physical hindrances in the communication path. These hurdles weaken the signal strength which in turn effects the quality of the reception. WT (Wavelet Transform) is used to address the Eigenvalue problem and the Krylov sequence is used to attempt the sparse identification of the MAA signal. The results show that the convergence of the MMA signal is improved by applying Wavelet transform and Krylov Subspace. (author)

  17. Modeling and Forecast Biological Oxygen Demand (BOD using Combination Support Vector Machine with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Abazar Solgi

    2017-06-01

    Full Text Available Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated. Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1, the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was

  18. A new approach to voltage sag detection based on wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Gencer, Oezguer; Oeztuerk, Semra; Erfidan, Tarik [Kocaeli University, Faculty of Engineering, Department of Electrical Engineering, Veziroglu Kampuesue, Eski Goelcuek Yolu, Kocaeli (Turkey)

    2010-02-15

    In this work, a new voltage sag detection method based on wavelet transform is developed. Voltage sag detection algorithms, so far have proved their efficiency and computational ability. Using several windowing techniques take long computational times for disturbance detection. Also researchers have been working on separating voltage sags from other voltage disturbances for the last decade. Due to increasing power quality standards new high performance disturbance detection algorithms are necessary to obtain high power quality standards. For this purpose, the wavelet technique is used for detecting voltage sag duration and magnitude. The developed voltage sag detection algorithm is implemented with high speed microcontroller. Test results show that, the new approach provides very accurate and satisfactory voltage sag detection. (author)

  19. Use of Wavelet Transform to Detect Compensated and Decompensated Stages in the Congestive Heart Failure Patient

    Directory of Open Access Journals (Sweden)

    Pratibha Sharma

    2017-09-01

    Full Text Available This research work is aimed at improving health care, reducing cost, and the occurrence of emergency hospitalization in patients with Congestive Heart Failure (CHF by analyzing heart and lung sounds to distinguish between the compensated and decompensated states. Compensated state defines stable state of the patient but with lack of retention of fluids in lungs, whereas decompensated state leads to unstable state of the patient with lots of fluid retention in the lungs, where the patient needs medication. Acoustic signals from the heart and the lung were analyzed using wavelet transforms to measure changes in the CHF patient’s status from the decompensated to compensated and vice versa. Measurements were taken on CHF patients diagnosed to be in compensated and decompensated states by using a digital stethoscope and electrocardiogram (ECG in order to monitor their progress in the management of their disease. Analysis of acoustic signals of the heart due to the opening and closing of heart valves as well as the acoustic signals of the lungs due to respiration and the ECG signals are presented. Fourier, short-time Fourier, and wavelet transforms are evaluated to determine the best method to detect shifts in the status of a CHF patient. The power spectra obtained through the Fourier transform produced results that differentiate the signals from healthy people and CHF patients, while the short-time Fourier transform (STFT technique did not provide the desired results. The most promising results were obtained by using wavelet analysis. Wavelet transforms provide better resolution, in time, for higher frequencies, and a better resolution, in frequency, for lower frequencies.

  20. End-point detection in potentiometric titration by continuous wavelet transform.

    Science.gov (United States)

    Jakubowska, Małgorzata; Baś, Bogusław; Kubiak, Władysław W

    2009-10-15

    The aim of this work was construction of the new wavelet function and verification that a continuous wavelet transform with a specially defined dedicated mother wavelet is a useful tool for precise detection of end-point in a potentiometric titration. The proposed algorithm does not require any initial information about the nature or the type of analyte and/or the shape of the titration curve. The signal imperfection, as well as random noise or spikes has no influence on the operation of the procedure. The optimization of the new algorithm was done using simulated curves and next experimental data were considered. In the case of well-shaped and noise-free titration data, the proposed method gives the same accuracy and precision as commonly used algorithms. But, in the case of noisy or badly shaped curves, the presented approach works good (relative error mainly below 2% and coefficients of variability below 5%) while traditional procedures fail. Therefore, the proposed algorithm may be useful in interpretation of the experimental data and also in automation of the typical titration analysis, specially in the case when random noise interfere with analytical signal.

  1. Novel discrimination parameters for neutron-gamma discrimination with liquid scintillation detectors using wavelet transform

    International Nuclear Information System (INIS)

    Singh, H.; Singh, S.

    2015-01-01

    It has been observed that the discrimination performance of the wavelet transform method strongly depends on definition of discrimination parameters. These parameters are usually obtained from a combination of scaling functions at different scales, which represents the energy density of the wavelet coefficients. In this paper, the discrete wavelet transform (DWT) at minimum possible values of scale was investigated. Novel pulse shape discrimination parameters have been proposed for neutron and gamma discrimination in a mixed radiation field and tested with modeled pulses. The performance of these parameters was also validated in terms of quality of discrimination using experimental data of mixed events from an AmBe source collected with BC501 liquid scintillation detector. The quality of discrimination was evaluated by calculating a figure of merit (FOM) with all parameters under same experimental and simulation conditions. The FOM obtained with the proposed novel parameters was also compared with the charge comparison method. The proposed parameters exhibit better FOM as compared to the charge comparison method when high levels of noise are present in the data

  2. Wavelet transform with fuzzy tuning based indirect field oriented speed control of three-phase induction motor drive

    DEFF Research Database (Denmark)

    Sanjeevikumar, P.; Daya, J.L. Febin; Wheeler, Patrick

    2015-01-01

    by the proposed controller for an improved transient and steady state performances. The discrete wavelet transform has been used to decompose the error speed into different frequency components and the fuzzy logic is used to generate the scaling gains of the wavelet controller. The complete model of the proposed...

  3. 3D Scan-Based Wavelet Transform and Quality Control for Video Coding

    Directory of Open Access Journals (Sweden)

    Parisot Christophe

    2003-01-01

    Full Text Available Wavelet coding has been shown to achieve better compression than DCT coding and moreover allows scalability. 2D DWT can be easily extended to 3D and thus applied to video coding. However, 3D subband coding of video suffers from two drawbacks. The first is the amount of memory required for coding large 3D blocks; the second is the lack of temporal quality due to the sequence temporal splitting. In fact, 3D block-based video coders produce jerks. They appear at blocks temporal borders during video playback. In this paper, we propose a new temporal scan-based wavelet transform method for video coding combining the advantages of wavelet coding (performance, scalability with acceptable reduced memory requirements, no additional CPU complexity, and avoiding jerks. We also propose an efficient quality allocation procedure to ensure a constant quality over time.

  4. Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

    Directory of Open Access Journals (Sweden)

    D. Seidl

    1999-06-01

    Full Text Available Among a variety of spectrogram methods Short-Time Fourier Transform (STFT and Continuous Wavelet Transform (CWT were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.

  5. ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation

    Directory of Open Access Journals (Sweden)

    Sarunya Kanjanawattana

    2017-07-01

    Full Text Available Image classification plays a vital role in many areas of study, such as data mining and image processing; however, serious problems collectively referred to as the course of dimensionality have been encountered in previous studies as factors that reduce system performance. Furthermore, we also confront the problem of different graph characteristics even if graphs belong to same types. In this study, we propose a novel method of graph-type classification. Using our approach, we open up a new solution of high-dimensional images and address problems of different characteristics by converting graph images to one dimension with a discrete Fourier transformation and creating numeric datasets using wavelet and Hough transformations. Moreover, we introduce a new classifier, which is a combination between artificial neuron networks (ANNs and support vector machines (SVMs, which we call ANNSVM, to enhance accuracy. The objectives of our study are to propose an effective graph-type classification method that includes finding a new data representative used for classification instead of two-dimensional images and to investigate what features make our data separable. To evaluate the method of our study, we conducted five experiments with different methods and datasets. The input dataset we focused on was a numeric dataset containing wavelet coefficients and outputs of a Hough transformation. From our experimental results, we observed that the highest accuracy was provided using our method with Coiflet 1, which achieved a 0.91 accuracy.

  6. Time-Frequency-Wavenumber Analysis of Surface Waves Using the Continuous Wavelet Transform

    Science.gov (United States)

    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.

  7. 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...

  8. 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.

  9. Wavelets a primer

    CERN Document Server

    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.

  10. A new relative radiometric consistency processing method for change detection based on wavelet transform and a low-pass filter

    Institute of Scientific and Technical Information of China (English)

    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.

  11. 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)

  12. Wavelet transform and Huffman coding based electrocardiogram compression algorithm: Application to telecardiology

    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

  13. Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

    Science.gov (United States)

    Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

    2008-09-01

    In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

  14. Wavelet transform for the evaluation of peak intensities in flow-injection analysis

    NARCIS (Netherlands)

    Bos, M.; Hoogendam, E.

    1992-01-01

    The application of the wavelet transform in the determination of peak intensities in flow-injection analysis was studied with regard to its properties of minimizing the effects of noise and baseline drift. The results indicate that for white noise and a favourable peak shape a signal-to-noise ratio

  15. Full-frame compression of discrete wavelet and cosine transforms

    Science.gov (United States)

    Lo, Shih-Chung B.; Li, Huai; Krasner, Brian; Freedman, Matthew T.; Mun, Seong K.

    1995-04-01

    At the foreground of computerized radiology and the filmless hospital are the possibilities for easy image retrieval, efficient storage, and rapid image communication. This paper represents the authors' continuous efforts in compression research on full-frame discrete wavelet (FFDWT) and full-frame discrete cosine transforms (FFDCT) for medical image compression. Prior to the coding, it is important to evaluate the global entropy in the decomposed space. It is because of the minimum entropy, that a maximum compression efficiency can be achieved. In this study, each image was split into the top three most significant bit (MSB) and the remaining remapped least significant bit (RLSB) images. The 3MSB image was compressed by an error-free contour coding and received an average of 0.1 bit/pixel. The RLSB image was either transformed to a multi-channel wavelet or the cosine transform domain for entropy evaluation. Ten x-ray chest radiographs and ten mammograms were randomly selected from our clinical database and were used for the study. Our results indicated that the coding scheme in the FFDCT domain performed better than in FFDWT domain for high-resolution digital chest radiographs and mammograms. From this study, we found that decomposition efficiency in the DCT domain for relatively smooth images is higher than that in the DWT. However, both schemes worked just as well for low resolution digital images. We also found that the image characteristics of the `Lena' image commonly used in the compression literature are very different from those of radiological images. The compression outcome of the radiological images can not be extrapolated from the compression result based on the `Lena.'

  16. PREVISIÓN DE CRISIS EPILÉPTICAS USANDO TRANSFORMADA WAVELET Y CORRELACIÓN CRUZADA PREVENTION OF EPILEPTICAL CRISIS USING WAVELET TRANSFORM AND CROSS-CORRELATION

    Directory of Open Access Journals (Sweden)

    Claudia C. Botero Suárez

    2007-07-01

    Full Text Available Este artículo describe la detección de actividad precrisis mediante la aplicación de la correlación cruzada junto con la transformada Wavelet. La transformada Wavelet es aplicada a los datos EEG puros para la reducción y pre-procesamiento de las señales. Esta técnica de extracción de características provee las señales simplificadas para ser procesadas por medio de la técnica de correlación cruzada. El análisis ha sido realizado con un grupo de datos tanto precrisis como intercrisis, (incluyendo crisis agudas inducidas y crisis espontáneas recurrentes, con el fin de determinar su sensitividad y especificidad (tasa de falsas predicciones. Son determinados, adicionalmente, el período de ocurrencia de crisis y el horizonte de previsión de crisis.This paper describes the detection of a pre-crisis activity through the application of Cross-Correlation together with the Wavelet Transform. The Wavelet Transform is applied in the data reduction and pre-processing of signals. This feature extract technique provides the simplified signals to process by means of the Cross-Correlation technique. The analysis with a group of pre-crisis and inter-crisis data (including both induced acute crises and recurrent spontaneous crises, to determinate its sensitivity and its specificity (False Prediction Rate has been done. The seizure occurrence period and the seizure prediction horizon are calculated additionally.

  17. 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

  18. Regional Land Subsidence Analysis in Eastern Beijing Plain by InSAR Time Series and Wavelet Transforms

    Directory of Open Access Journals (Sweden)

    Mingliang Gao

    2018-02-01

    Full Text Available Land subsidence is the disaster phenomenon of environmental geology with regionally surface altitude lowering caused by the natural or man-made factors. Beijing, the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of more than 100 mm/year. In this study, we employ two SAR datasets acquired by Envisat and TerraSAR-X satellites to investigate the surface deformation in Beijing Plain from 2003 to 2013 based on the multi-temporal InSAR technique. Furthermore, we also use observation wells to provide in situ hydraulic head levels to perform the evolution of land subsidence and spatial-temporal changes of groundwater level. Then, we analyze the accumulated displacement and hydraulic head level time series using continuous wavelet transform to separate periodic signal components. Finally, cross wavelet transform (XWT and wavelet transform coherence (WTC are implemented to analyze the relationship between the accumulated displacement and hydraulic head level time series. The results show that the subsidence centers in the northern Beijing Plain is spatially consistent with the groundwater drop funnels. According to the analysis of well based results located in different areas, the long-term groundwater exploitation in the northern subsidence area has led to the continuous decline of the water level, resulting in the inelastic and permanent compaction, while for the monitoring wells located outside the subsidence area, the subsidence time series show obvious elastic deformation characteristics (seasonal characteristics as the groundwater level changes. Moreover, according to the wavelet transformation, the land subsidence time series at monitoring well site lags several months behind the groundwater level change.

  19. A New Perceptual Mapping Model Using Lifting Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Taha TahaBasheer

    2017-01-01

    Full Text Available Perceptual mappingapproaches have been widely used in visual information processing in multimedia and internet of things (IOT applications. Accumulative Lifting Difference (ALD is proposed in this paper as texture mapping model based on low-complexity lifting wavelet transform, and combined with luminance masking for creating an efficient perceptual mapping model to estimate Just Noticeable Distortion (JND in digital images. In addition to low complexity operations, experiments results show that the proposed modelcan tolerate much more JND noise than models proposed before

  20. An Improved Split-Step Wavelet Transform Method for Anomalous Radio Wave Propagation Modelling

    Directory of Open Access Journals (Sweden)

    A. Iqbal

    2014-12-01

    Full Text Available Anomalous tropospheric propagation caused by ducting phenomenon is a major problem in wireless communication. Thus, it is important to study the behavior of radio wave propagation in tropospheric ducts. The Parabolic Wave Equation (PWE method is considered most reliable to model anomalous radio wave propagation. In this work, an improved Split Step Wavelet transform Method (SSWM is presented to solve PWE for the modeling of tropospheric propagation over finite and infinite conductive surfaces. A large number of numerical experiments are carried out to validate the performance of the proposed algorithm. Developed algorithm is compared with previously published techniques; Wavelet Galerkin Method (WGM and Split-Step Fourier transform Method (SSFM. A very good agreement is found between SSWM and published techniques. It is also observed that the proposed algorithm is about 18 times faster than WGM and provide more details of propagation effects as compared to SSFM.

  1. Selection of the wavelet function for the frequencies estimation; Seleccion de la funcion wavelet para la estimacion de frecuencias

    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)

  2. Numerical implementation of wavelet and fuzzy transform IFOC for three-phase induction motor

    DEFF Research Database (Denmark)

    Padamanaban, Sanjeevi Kumar; Daya, J.L. Febin; Blaabjerg, Frede

    2016-01-01

    This article elaborates the numerical implementation of a novel, indirect field-oriented control (IFOC) for induction motor drive by wave-let discrete transform/fuzzy logic interface system unique combination. The feedback (speed) error signal is a mixed component of multiple low and high frequen...

  3. 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.

  4. Separation of complex fringe patterns using two-dimensional continuous wavelet transform.

    Science.gov (United States)

    Pokorski, Krzysztof; Patorski, Krzysztof

    2012-12-10

    A method for processing fringe patterns containing additively superimposed multiple fringe sets is presented. It enables to analyze different fringe families present in a single image separately. The proposed method is based on a two-dimensional continuous wavelet transform. A robust ridge extraction algorithm for a single fringe set extraction is presented. The method is fully automatic and requires no user interference. Spectral separation of fringe families is not required. Simulations are presented to verify performance and advantage of the proposed method over the Fourier transform based technique. Method validity has been confirmed using experimental images.

  5. DETECTION OF MICROCALCIFICATION IN DIGITAL MAMMOGRAMS USING ONE DIMENSIONAL WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    T. Balakumaran

    2010-11-01

    Full Text Available Mammography is the most efficient method for breast cancer early detection. Clusters of microcalcifications are the early sign of breast cancer and their detection is the key to improve prognosis of breast cancer. Microcalcifications appear in mammogram image as tiny localized granular points, which is often difficult to detect by naked eye because of their small size. Automatic and accurately detection of microcalcifications has received much more attention from radiologists and physician. An efficient method for automatic detection of clustered microcalcifications in digitized mammograms is the use of Computer Aided Diagnosis (CAD systems. This paper presents a one dimensional wavelet-based multiscale products scheme for microcalcification detection in mammogram images. The detection of microcalcifications were achieved by decomposing the each line of mammograms by 1D wavelet transform into different frequency sub-bands, suppressing the low-frequency subband, and finally reconstructing the mammogram from the subbands containing only significant high frequencies features. The significant features are obtained by multiscale products. Preliminary results indicate that the proposed scheme is better in suppressing the background and detecting the microcalcification clusters than any other wavelet decomposition methods.

  6. Real-time wavelet-transform spectrum analyzer for the investigation of 1/fα noise

    Science.gov (United States)

    Brogioli, Doriano; Vailati, Alberto

    2003-04-01

    A wavelet-transform spectrum analyzer operating in real time within the frequency range 3×10-5-1.3×105Hz has been implemented on a low-cost digital signal processing (DSP) board operating at 150 MHz. The wavelet decomposition of the signal allows one to efficiently process nonstationary signals dominated by large amplitude events fairly well localized in time, thus providing the natural tool to analyze processes characterized by 1/fα power spectrum. The parallel architecture of the DSP allows the real-time processing of the wavelet transform of the signal sampled at 0.3 MHz. The bandwidth is about 220 dB, almost 10 decades. The power spectrum of the signal is processed in real time from the mean square value of the wavelet coefficients within each frequency band. The performances of the spectrum analyzer have been investigated by performing dynamic light scattering experiments on colloidal suspensions and by comparing the measured spectra with the correlation functions data obtained with a traditional multitau correlator. In order to assess the potentialities of the spectrum analyzer in the investigation of processes involving a wide range of time scales, we have performed measurements on a model system where fluctuations in the scattered intensities are generated by the number fluctuations in a dilute colloidal suspension illuminated by a wide beam. This system is characterized by a power-law spectrum with exponent -3/2 in the scattered intensity fluctuations. The spectrum analyzer allows one to recover the power spectrum with a dynamic range spanning about 8 decades. The advantages of wavelet analysis versus correlation analysis in the investigation of processes characterized by a wide distribution of time scales and nonstationary processes are briefly discussed.

  7. Implementing wavelet packet transform for valve failure detection using vibration and acoustic emission signals

    International Nuclear Information System (INIS)

    Sim, H Y; Ramli, R; Abdullah, M A K

    2012-01-01

    The efficiency of reciprocating compressors relies heavily on the health condition of its moving components, most importantly its valves. Previous studies showed good correlation between the dynamic response and the physical condition of the valves. These can be achieved by employing vibration technique which is capable of monitoring the response of the valve, and acoustic emission technique which is capable of detecting the valves' material deformation. However, the relationship/comparison between the two techniques is rarely investigated. In this paper, the two techniques were examined using time-frequency analysis. Wavelet packet transform (WPT) was chosen as the multi-resolution analysis technique over continuous wavelet transform (CWT), and discrete wavelet transform (DWT). This is because WPT could overcome the high computational time and high redundancy problem in CWT and could provide detailed analysis of the high frequency components compared to DWT. The features of both signals can be extracted by evaluating the normalised WPT coefficients for different time window under different valve conditions. By comparing the normalised coefficients over a certain time frame and frequency range, the feature vectors revealing the condition of valves can be constructed. One way analysis of variance was employed on these feature vectors to test the significance of data under different valve conditions. It is believed that AE signals can give a better representation of the valve condition as it can detect both the fluid motion and material deformation of valves as compared to the vibration signals.

  8. 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.

  9. Accurate reconstruction in digital holographic microscopy using Fresnel dual-tree complex wavelet transform

    Science.gov (United States)

    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.

  10. Control Strategy Based on Wavelet Transform and Neural Network for Hybrid Power System

    Directory of Open Access Journals (Sweden)

    Y. D. Song

    2013-01-01

    Full Text Available This paper deals with an energy management of a hybrid power generation system. The proposed control strategy for the energy management is based on the combination of wavelet transform and neural network arithmetic. The hybrid system in this paper consists of an emulated wind turbine generator, PV panels, DC and AC loads, lithium ion battery, and super capacitor, which are all connected on a DC bus with unified DC voltage. The control strategy is responsible for compensating the difference between the generated power from the wind and solar generators and the demanded power by the loads. Wavelet transform decomposes the power difference into smoothed component and fast fluctuated component. In consideration of battery protection, the neural network is introduced to calculate the reference power of battery. Super capacitor (SC is controlled to regulate the DC bus voltage. The model of the hybrid system is developed in detail under Matlab/Simulink software environment.

  11. Ambiguity attacks on robust blind image watermarking scheme based on redundant discrete wavelet transform and singular value decomposition

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2017-12-01

    Full Text Available Among emergent applications of digital watermarking are copyright protection and proof of ownership. Recently, Makbol and Khoo (2013 have proposed for these applications a new robust blind image watermarking scheme based on the redundant discrete wavelet transform (RDWT and the singular value decomposition (SVD. In this paper, we present two ambiguity attacks on this algorithm that have shown that this algorithm fails when used to provide robustness applications like owner identification, proof of ownership, and transaction tracking. Keywords: Ambiguity attack, Image watermarking, Singular value decomposition, Redundant discrete wavelet transform

  12. Classification of Fetal Heart Rate Tracings Based on Wavelet-Transform & Self-Organizing Map Neural Networks

    National Research Council Canada - National Science Library

    Vasios, G

    2001-01-01

    .... We demonstrate that it is possible to distinguish between healthy subjects and acidemic fetuses by way of wavelet transform analysis of the fetal heart rate recordings and fetal pulse oximetry (FSpO2...

  13. Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

    Science.gov (United States)

    Chen, Jinglong; Li, Zipeng; Pan, Jun; Chen, Gaige; Zi, Yanyang; Yuan, Jing; Chen, Binqiang; He, Zhengjia

    2016-03-01

    As a significant role in industrial equipment, rotating machinery fault diagnosis (RMFD) always draws lots of attention for guaranteeing product quality and improving economic benefit. But non-stationary vibration signal with a large amount of noise on abnormal condition of weak fault or compound fault in many cases would lead to this task challenging. As one of the most powerful non-stationary signal processing techniques, wavelet transform (WT) has been extensively studied and widely applied in RMFD. Numerous publications about the study and applications of WT for RMFD have been presented to academic journals, technical reports and conference proceedings. Many previous publications admit that WT can be realized by means of inner product principle of signal and wavelet base. This paper verifies the essence on inner product operation of WT by simulation and field experiments. Then the development process of WT based on inner product is concluded and the applications of major developments in RMFD are also summarized. Finally, super wavelet transform as an important prospect of WT based on inner product are presented and discussed. It is expected that this paper can offer an in-depth and comprehensive references for researchers and help them with finding out further research topics.

  14. Improving the performance of neutral network in differentiation of breast tumors using wavelet transformation on dynamic MRI

    International Nuclear Information System (INIS)

    Abdolmaleki, P.; Abrishami-Moghddam, H.; Gity, M.; Mokhtari- Dizaji, M.; Mostafa, A.

    2005-01-01

    A computer aided diagnosis system was established using the wavelet transform and neural network to differentiate malignant from benign in a group of patients with histo-pathologically proved breast lesions based on the data derived independently from time-intensity profile. Materials and Methods: The performance of the artificial neural network was evaluated using a database with 105 patients' records each of which consisted of 8 quantitative parameters mostly derived from time- intensity profile using wavelet transform. These findings were encoded as features for a three-layered neural network to predict the outcome of biopsy. The network was trained and tested using the jackknife method and its performance was then compared to that of the radiologists in terms of sensitivity, specificity and accuracy using receiver operating characteristic curve (ROC) analysis. Results: The network was able to classify correctly the 84 original cases and yielded a comparable diagnostic accuracy (80%), compared to that of the radiologist (85%) by performing a constructive association between extracted quantitative data and corresponding pathological results (r=0.63, p<0.001). Conclusion: An artificial neural network supported by wavelet transform can be trained to differentiate malignant from benign breast tumors with a reasonable degree of accuracy

  15. Quantum computation of multifractal exponents through the quantum wavelet transform

    International Nuclear Information System (INIS)

    Garcia-Mata, Ignacio; Giraud, Olivier; Georgeot, Bertrand

    2009-01-01

    We study the use of the quantum wavelet transform to extract efficiently information about the multifractal exponents for multifractal quantum states. We show that, combined with quantum simulation algorithms, it enables to build quantum algorithms for multifractal exponents with a polynomial gain compared to classical simulations. Numerical results indicate that a rough estimate of fractality could be obtained exponentially fast. Our findings are relevant, e.g., for quantum simulations of multifractal quantum maps and of the Anderson model at the metal-insulator transition.

  16. Analysis of Satellite Drag Coefficient Based on Wavelet Transform

    Science.gov (United States)

    Liu, Wei; Wang, Ronglan; Liu, Siqing

    Abstract: Drag coefficient sequence was obtained by solving Tiangong1 continuous 55days GPS orbit data with different arc length. The same period solar flux f10.7 and geomagnetic index Ap ap series were high and low frequency multi-wavelet decomposition. Statistical analysis results of the layers sliding correlation between space environmental parameters and decomposition of Cd, showed that the satellite drag coefficient sequence after wavelet decomposition and the corresponding level of f10.7 Ap sequence with good lag correlation. It also verified that the Cd prediction is feasible. Prediction residuals of Cd with different regression models and different sample length were analysed. The results showed that the case was best when setting sample length 20 days and f10.7 regression model were used. It also showed that NRLMSIS-00 model's response in the region of 350km (Tiangong's altitude) and low-middle latitude (Tiangong's inclination) is excessive in ascent stage of geomagnetic activity Ap and is inadequate during fall off segment. Additionally, the low-frequency decomposition components NRLMSIS-00 model's response is appropriate in f10.7 rising segment. High frequency decomposition section, Showed NRLMSIS-00 model's response is small-scale inadequate during f10.7 ascent segment and is reverse in decline of f10.7. Finally, the potential use of a summary and outlook were listed; This method has an important reference value to improve the spacecraft orbit prediction accuracy. Key words: wavelet transform; drag coefficient; lag correlation; Tiangong1;space environment

  17. 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.

  18. Fault Classification and Location in Transmission Lines Using Traveling Waves Modal Components and Continuous Wavelet Transform (CWT

    Directory of Open Access Journals (Sweden)

    Farhad Namdari

    2016-06-01

    Full Text Available Accurate fault classification and localization are the bases of protection for transmission systems. This paper presents a new method for classifying and showing location of faults by travelling waves and modal analysis. In the proposed method, characteristics of different faults are investigated using Clarke transformation and initial current traveling wave; then, appropriate indices are introduced to identify different types of faults. Continuous wavelet transform (CWT is employed to extract information of current and voltage travelling waves. Fault location and classification algorithm is being designed according to wavelet transform coefficients relating to current and voltage modal components. The performance of the proposed method is tested for different fault conditions (different fault distance, different fault resistances, and different fault inception angles by using PSCAD and MATLAB with satisfactory results

  19. A novel application of wavelet based SVM to transient phenomena identification of power transformers

    International Nuclear Information System (INIS)

    Jazebi, S.; Vahidi, B.; Jannati, M.

    2011-01-01

    A novel differential protection approach is introduced in the present paper. The proposed scheme is a combination of Support Vector Machine (SVM) and wavelet transform theories. Two common transients such as magnetizing inrush current and internal fault are considered. A new wavelet feature is extracted which reduces the computational cost and enhances the discrimination accuracy of SVM. Particle swarm optimization technique (PSO) has been applied to tune SVM parameters. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer in PSCAD/EMTDC software. The method has the advantages of high accuracy and low computational burden (less than a quarter of a cycle). The other advantage is that the method is not dependent on a specific threshold. Sympathetic and recovery inrush currents also have been simulated and investigated. Results show that the proposed method could remain stable even in noisy environments.

  20. 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

  1. Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Bo Chen

    2014-01-01

    Full Text Available The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited.

  2. Analysis of Mold Friction in a Continuous Casting Using Wavelet Transform

    Science.gov (United States)

    Ma, Yong; Fang, Bohan; Ding, Qiqi; Wang, Fangyin

    2018-04-01

    Mold friction (MDF) is an important parameter reflecting the lubrication condition between the initial shell and the mold during continuous casting. In this article, based on practical MDF from the slab continuous casting driven by a mechanical vibration device, the characteristics of friction were analyzed by continuous wavelet transform (CWT) and discrete wavelet transform (DWT) in different casting conditions, such as normal casting, level fluctuation, and alarming of the temperature measurement system. The results show that the CWT of friction accurately captures the subtle changes in friction force, such as the periodic characteristic of MDF during normal casting and the disordered feature of MDF during level fluctuation. Most important, the results capture the occurrence of abnormal casting and display the friction frequency characteristics at this abnormal time. In addition, in this article, there are some abnormal casting conditions, and the friction signal is stable until there is a sudden large change when abnormal casting, such as split breakout and submerged entry nozzle breakage, occurs. The DWT has a good ability to capture the friction characteristics for such abnormal situations. In particular, the potential abnormal features of MDF were presented in advance, which provides strong support for identifying abnormal casting and even preventing abnormal casting.

  3. Multisensor signal denoising based on matching synchrosqueezing wavelet transform for mechanical fault condition assessment

    Science.gov (United States)

    Yi, Cancan; Lv, Yong; Xiao, Han; Huang, Tao; You, Guanghui

    2018-04-01

    Since it is difficult to obtain the accurate running status of mechanical equipment with only one sensor, multisensor measurement technology has attracted extensive attention. In the field of mechanical fault diagnosis and condition assessment based on vibration signal analysis, multisensor signal denoising has emerged as an important tool to improve the reliability of the measurement result. A reassignment technique termed the synchrosqueezing wavelet transform (SWT) has obvious superiority in slow time-varying signal representation and denoising for fault diagnosis applications. The SWT uses the time-frequency reassignment scheme, which can provide signal properties in 2D domains (time and frequency). However, when the measured signal contains strong noise components and fast varying instantaneous frequency, the performance of SWT-based analysis still depends on the accuracy of instantaneous frequency estimation. In this paper, a matching synchrosqueezing wavelet transform (MSWT) is investigated as a potential candidate to replace the conventional synchrosqueezing transform for the applications of denoising and fault feature extraction. The improved technology utilizes the comprehensive instantaneous frequency estimation by chirp rate estimation to achieve a highly concentrated time-frequency representation so that the signal resolution can be significantly improved. To exploit inter-channel dependencies, the multisensor denoising strategy is performed by using a modulated multivariate oscillation model to partition the time-frequency domain; then, the common characteristics of the multivariate data can be effectively identified. Furthermore, a modified universal threshold is utilized to remove noise components, while the signal components of interest can be retained. Thus, a novel MSWT-based multisensor signal denoising algorithm is proposed in this paper. The validity of this method is verified by numerical simulation, and experiments including a rolling

  4. Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

    DEFF Research Database (Denmark)

    Bajric, Rusmir; Zuber, Ninoslav; Skrimpas, Georgios Alexandros

    2016-01-01

    , the vibration signals are decomposed into a series of subbands signals with the use of amultiresolution analytical property of the discrete wavelet transform.Then, 22 condition indicators are extracted fromthe TSA signal, residual signal, and difference signal.Through the case study analysis, a new approach...

  5. Analysis of Energy Overshoot of High Frequency Waves with Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    WEN Fan

    2000-01-01

    A study is made on the overshoot phenomena in wind-generated waves. The surface displace ments of time-growing waves are measured at four fetches in a wind wave channel. The evolution of high frequency waves is displayed with wavelet transform. The results are compared with Sutherland's. It is found that high frequency wave components experience much stronger energy overshoot in the evolution.The energy of high frequency waves decreases greatly after overshoot

  6. LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

    Science.gov (United States)

    Cheng, Jun; Zhang, Jun; Tian, Jinwen

    2015-12-01

    Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

  7. An Extension of Fourier-Wavelet Volume Rendering by View Interpolation

    NARCIS (Netherlands)

    Westenberg, Michel A.; Roerdink, Jos B.T.M.

    2001-01-01

    This paper describes an extension to Fourier-wavelet volume rendering (FWVR), which is a Fourier domain implementation of the wavelet X-ray transform. This transform combines integration along the line of sight with a simultaneous 2-D wavelet transform in the view plane perpendicular to this line.

  8. Separation of transient and oscillatory cerebral activities using over-complete rational dilation wavelet transforms

    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).

  9. A DNA Structure-Based Bionic Wavelet Transform and Its Application to DNA Sequence Analysis

    Directory of Open Access Journals (Sweden)

    Fei Chen

    2003-01-01

    Full Text Available DNA sequence analysis is of great significance for increasing our understanding of genomic functions. An important task facing us is the exploration of hidden structural information stored in the DNA sequence. This paper introduces a DNA structure-based adaptive wavelet transform (WT – the bionic wavelet transform (BWT – for DNA sequence analysis. The symbolic DNA sequence can be separated into four channels of indicator sequences. An adaptive symbol-to-number mapping, determined from the structural feature of the DNA sequence, was introduced into WT. It can adjust the weight value of each channel to maximise the useful energy distribution of the whole BWT output. The performance of the proposed BWT was examined by analysing synthetic and real DNA sequences. Results show that BWT performs better than traditional WT in presenting greater energy distribution. This new BWT method should be useful for the detection of the latent structural features in future DNA sequence analysis.

  10. Acoustic–gravity waves during solar eclipses: Detection and characterization using wavelet transforms

    Czech Academy of Sciences Publication Activity Database

    Šauli, Petra; Roux, S. G.; Abry, P.; Boška, Josef

    2007-01-01

    Roč. 69, 17-18 (2007), s. 2465-2484 ISSN 1364-6826 R&D Projects: GA ČR GA205/06/1619; GA AV ČR IAA300420504 Grant - others:CNRS(FR) 18098 Institutional research plan: CEZ:AV0Z30420517 Keywords : Acoustic–gravity wave * Vertical ionospheric sounding * F-layer * Wavelet transform * Wave-packet characterization Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.566, year: 2007

  11. Big data extraction with adaptive wavelet analysis (Presentation Video)

    Science.gov (United States)

    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.

  12. Shift-invariant discrete wavelet transform analysis for retinal image classification.

    Science.gov (United States)

    Khademi, April; Krishnan, Sridhar

    2007-12-01

    This work involves retinal image classification and a novel analysis system was developed. From the compressed domain, the proposed scheme extracts textural features from wavelet coefficients, which describe the relative homogeneity of localized areas of the retinal images. Since the discrete wavelet transform (DWT) is shift-variant, a shift-invariant DWT was explored to ensure that a robust feature set was extracted. To combat the small database size, linear discriminant analysis classification was used with the leave one out method. 38 normal and 48 abnormal (exudates, large drusens, fine drusens, choroidal neovascularization, central vein and artery occlusion, histoplasmosis, arteriosclerotic retinopathy, hemi-central retinal vein occlusion and more) were used and a specificity of 79% and sensitivity of 85.4% were achieved (the average classification rate is 82.2%). The success of the system can be accounted to the highly robust feature set which included translation, scale and semi-rotational, features. Additionally, this technique is database independent since the features were specifically tuned to the pathologies of the human eye.

  13. Application of wavelet transform for PDZ domain classification.

    Directory of Open Access Journals (Sweden)

    Khaled Daqrouq

    Full Text Available PDZ domains have been identified as part of an array of signaling proteins that are often unrelated, except for the well-conserved structural PDZ domain they contain. These domains have been linked to many disease processes including common Avian influenza, as well as very rare conditions such as Fraser and Usher syndromes. Historically, based on the interactions and the nature of bonds they form, PDZ domains have most often been classified into one of three classes (class I, class II and others - class III, that is directly dependent on their binding partner. In this study, we report on three unique feature extraction approaches based on the bigram and trigram occurrence and existence rearrangements within the domain's primary amino acid sequences in assisting PDZ domain classification. Wavelet packet transform (WPT and Shannon entropy denoted by wavelet entropy (WE feature extraction methods were proposed. Using 115 unique human and mouse PDZ domains, the existence rearrangement approach yielded a high recognition rate (78.34%, which outperformed our occurrence rearrangements based method. The recognition rate was (81.41% with validation technique. The method reported for PDZ domain classification from primary sequences proved to be an encouraging approach for obtaining consistent classification results. We anticipate that by increasing the database size, we can further improve feature extraction and correct classification.

  14. The parallel algorithm for the 2D discrete wavelet transform

    Science.gov (United States)

    Barina, David; Najman, Pavel; Kleparnik, Petr; Kula, Michal; Zemcik, Pavel

    2018-04-01

    The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists of a small number of operations, it is preferred for processing using single-core CPUs. However, considering a parallel processing using multi-core processors, this scheme is inappropriate due to a large number of steps. On such architectures, the number of steps corresponds to the number of points that represent the exchange of data. Consequently, these points often form a performance bottleneck. Our approach appropriately rearranges calculations inside the transform, and thereby reduces the number of steps. In other words, we propose a new scheme that is friendly to parallel environments. When evaluating on multi-core CPUs, we consistently overcome the original lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and 8-core Intel Xeon processors.

  15. 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)

  16. Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech.

    Science.gov (United States)

    Campo, D.; Quintero, O. L.; Bastidas, M.

    2016-04-01

    We propose a study of the mathematical properties of voice as an audio signal. This work includes signals in which the channel conditions are not ideal for emotion recognition. Multiresolution analysis- discrete wavelet transform - was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states. ANNs proved to be a system that allows an appropriate classification of such states. This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features. Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.

  17. A new time-adaptive discrete bionic wavelet transform for enhancing speech from adverse noise environment

    Science.gov (United States)

    Palaniswamy, Sumithra; Duraisamy, Prakash; Alam, Mohammad Showkat; Yuan, Xiaohui

    2012-04-01

    Automatic speech processing systems are widely used in everyday life such as mobile communication, speech and speaker recognition, and for assisting the hearing impaired. In speech communication systems, the quality and intelligibility of speech is of utmost importance for ease and accuracy of information exchange. To obtain an intelligible speech signal and one that is more pleasant to listen, noise reduction is essential. In this paper a new Time Adaptive Discrete Bionic Wavelet Thresholding (TADBWT) scheme is proposed. The proposed technique uses Daubechies mother wavelet to achieve better enhancement of speech from additive non- stationary noises which occur in real life such as street noise and factory noise. Due to the integration of human auditory system model into the wavelet transform, bionic wavelet transform (BWT) has great potential for speech enhancement which may lead to a new path in speech processing. In the proposed technique, at first, discrete BWT is applied to noisy speech to derive TADBWT coefficients. Then the adaptive nature of the BWT is captured by introducing a time varying linear factor which updates the coefficients at each scale over time. This approach has shown better performance than the existing algorithms at lower input SNR due to modified soft level dependent thresholding on time adaptive coefficients. The objective and subjective test results confirmed the competency of the TADBWT technique. The effectiveness of the proposed technique is also evaluated for speaker recognition task under noisy environment. The recognition results show that the TADWT technique yields better performance when compared to alternate methods specifically at lower input SNR.

  18. Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

    Science.gov (United States)

    Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

    2014-09-01

    This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Combined Power Quality Disturbances Recognition Using Wavelet Packet Entropies and S-Transform

    Directory of Open Access Journals (Sweden)

    Zhigang Liu

    2015-08-01

    Full Text Available Aiming at the combined power quality +disturbance recognition, an automated recognition method based on wavelet packet entropy (WPE and modified incomplete S-transform (MIST is proposed in this paper. By combining wavelet packet Tsallis singular entropy, energy entropy and MIST, a 13-dimension vector of different power quality (PQ disturbances including single disturbances and combined disturbances is extracted. Then, a ruled decision tree is designed to recognize the combined disturbances. The proposed method is tested and evaluated using a large number of simulated PQ disturbances and some real-life signals, which include voltage sag, swell, interruption, oscillation transient, impulsive transient, harmonics, voltage fluctuation and their combinations. In addition, the comparison of the proposed recognition approach with some existing techniques is made. The experimental results show that the proposed method can effectively recognize the single and combined PQ disturbances.

  20. Digital Watermarks Using Discrete Wavelet Transformation and Spectrum Spreading

    Directory of Open Access Journals (Sweden)

    Ryousuke Takai

    2003-12-01

    Full Text Available In recent tears, digital media makes rapid progress through the development of digital technology. Digital media normally assures fairly high quality, nevertheless can be easily reproduced in a perfect form. This perfect reproducibility takes and advantage from a certain point of view, while it produces an essential disadvantage, since digital media is frequently copied illegally. Thus the problem of the copyright protection becomes a very important issue. A solution of this problem is to embed digital watermarks that is not perceived clearly by usual people, but represents the proper right of original product. In our method, the images data in the frequency domain are transformed by the Discrete Wavelet Transform and analyzed by the multi resolution approximation, [1]. Further, the spectrum spreading is executed by using PN-sequences. Choi and Aizawa [7] embed watermarks by using block correlation of DCT coefficients. Thus, we apply Discrete Cosine Transformation, abbreviated to DCT, instead of the Fourier transformation in order to embed watermarks.If the value of this variance is high then we decide that the block has bigger magnitude for visual fluctuations. Henceforth, we may embed stronger watermarks, which gives resistance for images processing, such as attacks and/or compressions.

  1. Two-dimensional wavelet transform feature extraction for porous silicon chemical sensors.

    Science.gov (United States)

    Murguía, José S; Vergara, Alexander; Vargas-Olmos, Cecilia; Wong, Travis J; Fonollosa, Jordi; Huerta, Ramón

    2013-06-27

    Designing reliable, fast responding, highly sensitive, and low-power consuming chemo-sensory systems has long been a major goal in chemo-sensing. This goal, however, presents a difficult challenge because having a set of chemo-sensory detectors exhibiting all these aforementioned ideal conditions are still largely un-realizable to-date. This paper presents a unique perspective on capturing more in-depth insights into the physicochemical interactions of two distinct, selectively chemically modified porous silicon (pSi) film-based optical gas sensors by implementing an innovative, based on signal processing methodology, namely the two-dimensional discrete wavelet transform. Specifically, the method consists of using the two-dimensional discrete wavelet transform as a feature extraction method to capture the non-stationary behavior from the bi-dimensional pSi rugate sensor response. Utilizing a comprehensive set of measurements collected from each of the aforementioned optically based chemical sensors, we evaluate the significance of our approach on a complex, six-dimensional chemical analyte discrimination/quantification task problem. Due to the bi-dimensional aspects naturally governing the optical sensor response to chemical analytes, our findings provide evidence that the proposed feature extractor strategy may be a valuable tool to deepen our understanding of the performance of optically based chemical sensors as well as an important step toward attaining their implementation in more realistic chemo-sensing applications. Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Shannon Entropy-Based Wavelet Transform Method for Autonomous Coherent Structure Identification in Fluid Flow Field Data

    Directory of Open Access Journals (Sweden)

    Kartik V. Bulusu

    2015-09-01

    Full Text Available The coherent secondary flow structures (i.e., swirling motions in a curved artery model possess a variety of spatio-temporal morphologies and can be encoded over an infinitely-wide range of wavelet scales. Wavelet analysis was applied to the following vorticity fields: (i a numerically-generated system of Oseen-type vortices for which the theoretical solution is known, used for bench marking and evaluation of the technique; and (ii experimental two-dimensional, particle image velocimetry data. The mother wavelet, a two-dimensional Ricker wavelet, can be dilated to infinitely large or infinitesimally small scales. We approached the problem of coherent structure detection by means of continuous wavelet transform (CWT and decomposition (or Shannon entropy. The main conclusion of this study is that the encoding of coherent secondary flow structures can be achieved by an optimal number of binary digits (or bits corresponding to an optimal wavelet scale. The optimal wavelet-scale search was driven by a decomposition entropy-based algorithmic approach and led to a threshold-free coherent structure detection method. The method presented in this paper was successfully utilized in the detection of secondary flow structures in three clinically-relevant blood flow scenarios involving the curved artery model under a carotid artery-inspired, pulsatile inflow condition. These scenarios were: (i a clean curved artery; (ii stent-implanted curved artery; and (iii an idealized Type IV stent fracture within the curved artery.

  3. Algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales

    International Nuclear Information System (INIS)

    Li Tianduo; Xiao Gang; Di Yuming; Han Feng; Qiu Xiaoling

    1999-01-01

    The γ energy spectrum is expanded in allied energy-frequency space. By the different characterization of the evolution of wavelet transform modulus maxima across scales between energy spectrum and noise, the algorithm for removing the noise from γ energy spectrum by analyzing the evolution of the wavelet transform maxima across scales is presented. The results show, in contrast to the methods in energy space or in frequency space, the method has the advantages that the peak of energy spectrum can be indicated accurately and the energy spectrum can be reconstructed with a good approximation

  4. Wavelets in neuroscience

    CERN Document Server

    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 ...

  5. Simultaneous Determination of Electrochemical Impedance of Lithium-ion Rechargeable Batteries with Measurement of Charge-discharge Curves by Wavelet Transformation

    International Nuclear Information System (INIS)

    Itagaki, Masayuki; Ueno, Masaki; Hoshi, Yoshinao; Shitanda, Isao

    2017-01-01

    Highlights: • Wavelet transformation (WT) was used to obtain electrochemical impedance (EI) from time domain data. • Complex Morlet mother wavelet was employed to transform current and voltage time series from time domain to frequency domain. • An analytical method to determine EI of LIRB at arbitrary state of charge was proposed. • EI of LIRB was determined at arbitrary state of charge without stopping galvanostatic polarization for charge and discharge. - Abstract: A new analytical method was developed to determine the electrochemical impedance of lithium-ion rechargeable batteries (LIRB) at an arbitrary state of charge (SOC). Wavelet transformation (WT) is one of the waveform analysis methods, which allows the determination of frequency domain data as a function of time. The frequency domain data are obtained by convolution integral of a mother wavelet and original time domain data via the WT. A complex Morlet mother wavelet is used to obtain the complex number data in the frequency domain. The time series data of input current and output voltage signals are recorded by superimposing the double pulse current as an input signal to constant charge current for the charge of LIRB without stopping galvanostatic polarization. The double pulse current is composed of symmetrical positive and negative square waves. In this case, the SOC of LIRB is not affected by the input signal because the total amount of charge calculated from double pulse current is 0C. The impedance spectrum of LIRB at SOC 25% is determined in the frequency range from 0.1 to 100 Hz during charge/discharge cycles without stopping galvanostatic polarization for the charge/discharge.

  6. [A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

    Science.gov (United States)

    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.

  7. Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

    Czech Academy of Sciences Publication Activity Database

    Farokhi, Sajad; Shamsuddin, S.M.; Sheikh, U.U.; Flusser, Jan; Khansari, M.; Jafari-Khouzani, K.

    2014-01-01

    Roč. 31, č. 1 (2014), s. 13-27 ISSN 1051-2004 R&D Projects: GA ČR GAP103/11/1552 Institutional support: RVO:67985556 Keywords : Zernike moments * Undecimated discrete wavelet transform * Decision fusion * Near infrared * Face recognition Subject RIV: JD - Computer Applications, Robotics Impact factor: 1.256, year: 2014 http://library.utia.cas.cz/separaty/2014/ZOI/flusser-0428536.pdf

  8. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles.

    Science.gov (United States)

    Chowdhury, Suman Kanti; Nimbarte, Ashish D; Jaridi, Majid; Creese, Robert C

    2013-10-01

    Assessment of neuromuscular fatigue is essential for early detection and prevention of risks associated with work-related musculoskeletal disorders. In recent years, discrete wavelet transform (DWT) of surface electromyography (SEMG) has been used to evaluate muscle fatigue, especially during dynamic contractions when the SEMG signal is non-stationary. However, its application to the assessment of work-related neck and shoulder muscle fatigue is not well established. Therefore, the purpose of this study was to establish DWT analysis as a suitable method to conduct quantitative assessment of neck and shoulder muscle fatigue under dynamic repetitive conditions. Ten human participants performed 40min of fatiguing repetitive arm and neck exertions while SEMG data from the upper trapezius and sternocleidomastoid muscles were recorded. The ten of the most commonly used wavelet functions were used to conduct the DWT analysis. Spectral changes estimated using power of wavelet coefficients in the 12-23Hz frequency band showed the highest sensitivity to fatigue induced by the dynamic repetitive exertions. Although most of the wavelet functions tested in this study reasonably demonstrated the expected power trend with fatigue development and recovery, the overall performance of the "Rbio3.1" wavelet in terms of power estimation and statistical significance was better than the remaining nine wavelets. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Adapted wavelet analysis from theory to software

    CERN Document Server

    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

  10. A Fault Diagnosis Model of Surface to Air Missile Equipment Based on Wavelet Transformation and Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Zhheng Ni

    2016-01-01

    Full Text Available At present, the fault signals of surface to air missile equipment are hard to collect and the accuracy of fault diagnosis is very low. To solve the above problems, based on the superiority of wavelet transformation on processing non-stationary signals and the advantage of SVM on pattern classification, this paper proposes a fault diagnosis model and takes the typical analog circuit diagnosis of one power distribution system as an example to verify the fault diagnosis model based on Wavelet Transformation and SVM. The simulation results show that the model is able to achieve fault diagnosis based on a small amount of training samples, which improves the accuracy of fault diagnosis.

  11. 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-.

  12. Detection of combustion start in the controlled auto ignition engine by wavelet transform of the engine block vibration signal

    International Nuclear Information System (INIS)

    Kim, Seonguk; Min, Kyoungdoug

    2008-01-01

    The CAI (controlled auto ignition) engine ignites fuel and air mixture by trapping high temperature burnt gas using a negative valve overlap. Due to auto ignition in CAI combustion, efficiency improvements and low level NO x emission can be obtained. Meanwhile, the CAI combustion regime is restricted and control parameters are limited. The start of combustion data in the compressed ignition engine are most critical for controlling the overall combustion. In this research, the engine block vibration signal is transformed by the Meyer wavelet to analyze CAI combustion more easily and accurately. Signal acquisition of the engine block vibration is a more suitable method for practical use than measurement of in-cylinder pressure. A new method for detecting combustion start in CAI engines through wavelet transformation of the engine block vibration signal was developed and results indicate that it is accurate enough to analyze the start of combustion. Experimental results show that wavelet transformation of engine block vibration can track the start of combustion in each cycle. From this newly developed method, the start of combustion data in CAI engines can be detected more easily and used as input data for controlling CAI combustion

  13. Detection of combustion start in the controlled auto ignition engine by wavelet transform of the engine block vibration signal

    Science.gov (United States)

    Kim, Seonguk; Min, Kyoungdoug

    2008-08-01

    The CAI (controlled auto ignition) engine ignites fuel and air mixture by trapping high temperature burnt gas using a negative valve overlap. Due to auto ignition in CAI combustion, efficiency improvements and low level NOx emission can be obtained. Meanwhile, the CAI combustion regime is restricted and control parameters are limited. The start of combustion data in the compressed ignition engine are most critical for controlling the overall combustion. In this research, the engine block vibration signal is transformed by the Meyer wavelet to analyze CAI combustion more easily and accurately. Signal acquisition of the engine block vibration is a more suitable method for practical use than measurement of in-cylinder pressure. A new method for detecting combustion start in CAI engines through wavelet transformation of the engine block vibration signal was developed and results indicate that it is accurate enough to analyze the start of combustion. Experimental results show that wavelet transformation of engine block vibration can track the start of combustion in each cycle. From this newly developed method, the start of combustion data in CAI engines can be detected more easily and used as input data for controlling CAI combustion.

  14. A prediction method based on wavelet transform and multiple models fusion for chaotic time series

    International Nuclear Information System (INIS)

    Zhongda, Tian; Shujiang, Li; Yanhong, Wang; Yi, Sha

    2017-01-01

    In order to improve the prediction accuracy of chaotic time series, a prediction method based on wavelet transform and multiple models fusion is proposed. The chaotic time series is decomposed and reconstructed by wavelet transform, and approximate components and detail components are obtained. According to different characteristics of each component, least squares support vector machine (LSSVM) is used as predictive model for approximation components. At the same time, an improved free search algorithm is utilized for predictive model parameters optimization. Auto regressive integrated moving average model (ARIMA) is used as predictive model for detail components. The multiple prediction model predictive values are fusion by Gauss–Markov algorithm, the error variance of predicted results after fusion is less than the single model, the prediction accuracy is improved. The simulation results are compared through two typical chaotic time series include Lorenz time series and Mackey–Glass time series. The simulation results show that the prediction method in this paper has a better prediction.

  15. From Fourier analysis to wavelets

    CERN Document Server

    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.

  16. Rapid limit tests for metal impurities in pharmaceutical materials by X-ray fluorescence spectroscopy using wavelet transform filtering.

    Science.gov (United States)

    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.

  17. Wavelet Transform: Application to Acoustic Logging La transformée en ondelettes : application à la diagraphie acoustique

    OpenAIRE

    Thirion N.; Mars J.; Volant P.; Mari J. L.

    2006-01-01

    The wavelet transform can be used to develop the process which allows group and phase velocity measurement of dispersive waves. The method has been applied to acoustic data to measure formation velocities. The behavior and the accuracy of the method have been checked on synthetic full waveform acoustic data. The method was applied to dispersive waves of the Stoneley type and to flexural modes whose low frequency components are propagated at the formation shear velocity. A raw measurement of t...

  18. A New Approach to High-accuracy Road Orthophoto Mapping Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Ming Yang

    2011-12-01

    Full Text Available Existing orthophoto map based on satellite photography and aerial photography is not precise enough for road marking. This paper proposes a new approach to high-accuracy orthophoto mapping. The approach uses inverse perspective transformation to process the image information and generates the orthophoto fragment. The offline interpolation algorithm is used to process the location information. It processes the dead reckoning and the EKF location information, and uses the result to transform the fragments to the global coordinate system. At last it uses wavelet transform to divides the image to two frequency bands and uses weighted median algorithm to deal with them separately. The result of experiment shows that the map produced with this method has high accuracy.

  19. Wavelet-LMS algorithm-based echo cancellers

    Science.gov (United States)

    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).

  20. Instrument-independent analysis of music by means of the continuous wavelet transform

    Science.gov (United States)

    Olmo, Gabriella; Dovis, Fabio; Benotto, Paolo; Calosso, Claudio; Passaro, Pierluigi

    1999-10-01

    This paper deals with the problem of automatic recognition of music. Segments of digitized music are processed by means of a Continuous Wavelet Transform, properly chosen so as to match the spectral characteristics of the signal. In order to achieve a good time-scale representation of the signal components a novel wavelet has been designed suited to the musical signal features. particular care has been devoted towards an efficient implementation, which operates in the frequency domain, and includes proper segmentation and aliasing reduction techniques to make the analysis of long signals feasible. The method achieves very good performance in terms of both time and frequency selectivity, and can yield the estimate and the localization in time of both the fundamental frequency and the main harmonics of each tone. The analysis is used as a preprocessing step for a recognition algorithm, which we show to be almost independent on the instrument reproducing the sounds. Simulations are provided to demonstrate the effectiveness of the proposed method.

  1. Motion compensation via redundant-wavelet multihypothesis.

    Science.gov (United States)

    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.

  2. Detection of single-phase CTA occult vessel occlusions in acute ischemic stroke using CT perfusion-based wavelet-transformed angiography

    Energy Technology Data Exchange (ETDEWEB)

    Kunz, Wolfgang G.; Sommer, Wieland H.; Meinel, Felix G.; Ertl-Wagner, Birgit; Thierfelder, Kolja M. [Ludwig-Maximilian-University Hospital Munich, Institute for Clinical Radiology, Munich (Germany); Havla, Lukas; Dietrich, Olaf [Ludwig-Maximilian-University Hospital Munich, Josef Lissner Laboratory for Biomedical Imaging of the Institute for Clinical Radiology, Munich (Germany); Dorn, Franziska [Ludwig-Maximilian-University Hospital Munich, Department of Neuroradiology, Munich (Germany); Buchholz, Grete [Ludwig-Maximilian-University Hospital Munich, Department of Neurology, Munich (Germany)

    2017-06-15

    To determine the detection rate of intracranial vessel occlusions using CT perfusion-based wavelet-transformed angiography (waveletCTA) in acute ischemic stroke patients, in whom single-phase CTA (spCTA) failed to detect an occlusion. Subjects were selected from a cohort of 791 consecutive patients who underwent multiparametric CT including whole-brain CT perfusion. Inclusion criteria were (1) significant cerebral blood flow (CBF) deficit, (2) no evidence of vessel occlusion on spCTA and (3) follow-up-confirmed acute ischemic infarction. waveletCTA was independently analysed by two readers regarding presence and location of vessel occlusions. Logistic regression analysis was performed to identify predictors of waveletCTA-detected occlusions. Fifty-nine patients fulfilled the inclusion criteria. Overall, an occlusion was identified using waveletCTA in 31 (52.5 %) patients with negative spCTA. Out of 47 patients with middle cerebral artery infarction, 27 occlusions (57.4 %) were detected by waveletCTA, mainly located in the M2 (15) and M3 segments (8). The presence of waveletCTA-detected occlusions was associated with larger CBF deficit volumes (odds ratio (OR) = 1.335, p = 0.010) and shorter times from symptom onset (OR = 0.306, p = 0.041). waveletCTA is able to detect spCTA occult vessel occlusions in about half of acute ischemic stroke patients and may potentially identify more patients eligible for endovascular therapy. (orig.)

  3. Discrete Wavelet Transform for Fault Locations in Underground Distribution System

    Science.gov (United States)

    Apisit, C.; Ngaopitakkul, A.

    2010-10-01

    In this paper, a technique for detecting faults in underground distribution system is presented. Discrete Wavelet Transform (DWT) based on traveling wave is employed in order to detect the high frequency components and to identify fault locations in the underground distribution system. The first peak time obtained from the faulty bus is employed for calculating the distance of fault from sending end. The validity of the proposed technique is tested with various fault inception angles, fault locations and faulty phases. The result is found that the proposed technique provides satisfactory result and will be very useful in the development of power systems protection scheme.

  4. A polarized digital shearing speckle pattern interferometry system based on temporal wavelet transformation.

    Science.gov (United States)

    Feng, Ziang; Gao, Zhan; Zhang, Xiaoqiong; Wang, Shengjia; Yang, Dong; Yuan, Hao; Qin, Jie

    2015-09-01

    Digital shearing speckle pattern interferometry (DSSPI) has been recognized as a practical tool in testing strain. The DSSPI system which is based on temporal analysis is attractive because of its ability to measure strain dynamically. In this paper, such a DSSPI system with Wollaston prism has been built. The principles and system arrangement are described and the preliminary experimental result of the displacement-derivative test of an aluminum plate is shown with the wavelet transformation method and the Fourier transformation method. The simulations have been conducted with the finite element method. The comparison of the results shows that quantitative measurement of displacement-derivative has been realized.

  5. A Novel Intelligent Method for the State of Charge Estimation of Lithium-Ion Batteries Using a Discrete Wavelet Transform-Based Wavelet Neural Network

    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.

  6. Removing divergences in the negative moments of the multi-fractal parition function with the wavelet transformation

    NARCIS (Netherlands)

    Z.R. Struzik

    1998-01-01

    textabstractWe present a promising technique which is capable of accessing the divergence free component of the partition function for the negative moments of the multi-fractal analysis of data using the wavelet transformation. It is based on implicitly bounding the local logarithmic slope of the

  7. Multiresolution signal decomposition schemes. Part 2: Morphological wavelets

    NARCIS (Netherlands)

    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

  8. Computational Intelligence and Wavelet Transform Based Metamodel for Efficient Generation of Not-Yet Simulated Waveforms

    Science.gov (United States)

    Oltean, Gabriel; Ivanciu, Laura-Nicoleta

    2016-01-01

    The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms), efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer), and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination). The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each parameter on the

  9. Computational Intelligence and Wavelet Transform Based Metamodel for Efficient Generation of Not-Yet Simulated Waveforms.

    Directory of Open Access Journals (Sweden)

    Gabriel Oltean

    Full Text Available The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms, efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer, and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination. The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each

  10. Computational Intelligence and Wavelet Transform Based Metamodel for Efficient Generation of Not-Yet Simulated Waveforms.

    Science.gov (United States)

    Oltean, Gabriel; Ivanciu, Laura-Nicoleta

    2016-01-01

    The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms), efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer), and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination). The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each parameter on the

  11. WAVELET-BASED ALGORITHM FOR DETECTION OF BEARING FAULTS IN A GAS TURBINE ENGINE

    Directory of Open Access Journals (Sweden)

    Sergiy Enchev

    2014-07-01

    Full Text Available Presented is a gas turbine engine bearing diagnostic system that integrates information from various advanced vibration analysis techniques to achieve robust bearing health state awareness. This paper presents a computational algorithm for identifying power frequency variations and integer harmonics by using wavelet-based transform. The continuous wavelet transform with  the complex Morlet wavelet is adopted to detect the harmonics presented in a power signal. The algorithm based on the discrete stationary wavelet transform is adopted to denoise the wavelet ridges.

  12. [An automatic peak detection method for LIBS spectrum based on continuous wavelet transform].

    Science.gov (United States)

    Chen, Peng-Fei; Tian, Di; Qiao, Shu-Jun; Yang, Guang

    2014-07-01

    Spectrum peak detection in the laser-induced breakdown spectroscopy (LIBS) is an essential step, but the presence of background and noise seriously disturb the accuracy of peak position. The present paper proposed a method applied to automatic peak detection for LIBS spectrum in order to enhance the ability of overlapping peaks searching and adaptivity. We introduced the ridge peak detection method based on continuous wavelet transform to LIBS, and discussed the choice of the mother wavelet and optimized the scale factor and the shift factor. This method also improved the ridge peak detection method with a correcting ridge method. The experimental results show that compared with other peak detection methods (the direct comparison method, derivative method and ridge peak search method), our method had a significant advantage on the ability to distinguish overlapping peaks and the precision of peak detection, and could be be applied to data processing in LIBS.

  13. Detecting the quality of glycerol monolaurate: a method for using Fourier transform infrared spectroscopy with wavelet transform and modified uninformative variable elimination.

    Science.gov (United States)

    Chen, Xiaojing; Wu, Di; He, Yong; Liu, Shou

    2009-04-06

    Glycerol monolaurate (GML) products contain many impurities, such as lauric acid and glucerol. The GML content is an important quality indicator for GML production. A hybrid variable selection algorithm, which is a combination of wavelet transform (WT) technology and modified uninformative variable eliminate (MUVE) method, was proposed to extract useful information from Fourier transform infrared (FT-IR) transmission spectroscopy for the determination of GML content. FT-IR spectra data were compressed by WT first; the irrelevant variables in the compressed wavelet coefficients were eliminated by MUVE. In the MUVE process, simulated annealing (SA) algorithm was employed to search the optimal cutoff threshold. After the WT-MUVE process, variables for the calibration model were reduced from 7366 to 163. Finally, the retained variables were employed as inputs of partial least squares (PLS) model to build the calibration model. For the prediction set, the correlation coefficient (r) of 0.9910 and root mean square error of prediction (RMSEP) of 4.8617 were obtained. The prediction result was better than the PLS model with full-spectra data. It was indicated that proposed WT-MUVE method could not only make the prediction more accurate, but also make the calibration model more parsimonious. Furthermore, the reconstructed spectra represented the projection of the selected wavelet coefficients into the original domain, affording the chemical interpretation of the predicted results. It is concluded that the FT-IR transmission spectroscopy technique with the proposed method is promising for the fast detection of GML content.

  14. The wavelet transform and the suppression theory of binocular vision for stereo image compression

    Energy Technology Data Exchange (ETDEWEB)

    Reynolds, W.D. Jr [Argonne National Lab., IL (United States); Kenyon, R.V. [Illinois Univ., Chicago, IL (United States)

    1996-08-01

    In this paper a method for compression of stereo images. The proposed scheme is a frequency domain approach based on the suppression theory of binocular vision. By using the information in the frequency domain, complex disparity estimation techniques can be avoided. The wavelet transform is used to obtain a multiresolution analysis of the stereo pair by which the subbands convey the necessary frequency domain information.

  15. Adaptive wavelet collocation methods for initial value boundary problems of nonlinear PDE's

    Science.gov (United States)

    Cai, Wei; Wang, Jian-Zhong

    1993-01-01

    We have designed a cubic spline wavelet decomposition for the Sobolev space H(sup 2)(sub 0)(I) where I is a bounded interval. Based on a special 'point-wise orthogonality' of the wavelet basis functions, a fast Discrete Wavelet Transform (DWT) is constructed. This DWT transform will map discrete samples of a function to its wavelet expansion coefficients in O(N log N) operations. Using this transform, we propose a collocation method for the initial value boundary problem of nonlinear PDE's. Then, we test the efficiency of the DWT transform and apply the collocation method to solve linear and nonlinear PDE's.

  16. 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.

  17. Image Enhancement In HSI Space Using Wavelet Transform

    Science.gov (United States)

    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.

  18. An introduction to random vibrations, spectral & wavelet analysis

    CERN Document Server

    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

  19. 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)

  20. Wavelet Based Diagnosis and Protection of Electric Motors

    OpenAIRE

    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

  1. 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.

  2. A New Quantum Watermarking Based on Quantum Wavelet Transforms

    International Nuclear Information System (INIS)

    Heidari, Shahrokh; Pourarian, Mohammad Rasoul; Naseri, Mosayeb; Gheibi, Reza; Baghfalaki, Masoud; Farouk, Ahmed

    2017-01-01

    Quantum watermarking is a technique to embed specific information, usually the owner’s identification, into quantum cover data such for copyright protection purposes. In this paper, a new scheme for quantum watermarking based on quantum wavelet transforms is proposed which includes scrambling, embedding and extracting procedures. The invisibility and robustness performances of the proposed watermarking method is confirmed by simulation technique. The invisibility of the scheme is examined by the peak-signal-to-noise ratio (PSNR) and the histogram calculation. Furthermore the robustness of the scheme is analyzed by the Bit Error Rate (BER) and the Correlation Two-Dimensional (Corr 2-D) calculation. The simulation results indicate that the proposed watermarking scheme indicate not only acceptable visual quality but also a good resistance against different types of attack. (paper)

  3. Wavelet library for constrained devices

    Science.gov (United States)

    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.

  4. 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.

  5. Covariant Transform

    OpenAIRE

    Kisil, Vladimir V.

    2010-01-01

    The paper develops theory of covariant transform, which is inspired by the wavelet construction. It was observed that many interesting types of wavelets (or coherent states) arise from group representations which are not square integrable or vacuum vectors which are not admissible. Covariant transform extends an applicability of the popular wavelets construction to classic examples like the Hardy space H_2, Banach spaces, covariant functional calculus and many others. Keywords: Wavelets, cohe...

  6. Adaptive Gain and Analog Wavelet Transform for Low-Power Infrared Image Sensors

    Directory of Open Access Journals (Sweden)

    P. Villard

    2012-01-01

    Full Text Available A decorrelation and analog-to-digital conversion scheme aiming to reduce the power consumption of infrared image sensors is presented in this paper. To exploit both intraframe redundancy and inherent photon shot noise characteristics, a column based 1D Haar analog wavelet transform combined with variable gain amplification prior to A/D conversion is used. This allows to use only an 11-bit ADC, instead of a 13-bit one, and to save 15% of data transfer. An 8×16 pixels test circuit demonstrates this functionality.

  7. On the Use of Wavelet Transform for Quench Precursors Characterisation in the LHC Superconducting Dipole Magnets

    CERN Document Server

    Calvi, M; Bottura, L; Masi, A; Siemko, A

    2006-01-01

    Premature training quenches are caused by transient energy released within the magnet coil while it is energized. Signals recorded across the so-called quench antenna carry information about these disturbances. A new method for identifying and characterizing those events is proposed, which applies the wavelet transform approach to the recorded signals. Such an approach takes into account the time of occurrence as well as frequency content of the events. The choice of the optimal mother wavelet is discussed, and the results obtained from the application of the method to actual signals are given. The criteria to recognize the interesting events are presented as well as the methodology to classify their global behavior.

  8. Energy detection based on undecimated discrete wavelet transform and its application in magnetic anomaly detection.

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

    Full Text Available Magnetic anomaly detection (MAD is a passive approach for detection of a ferromagnetic target, and its performance is often limited by external noises. In consideration of one major noise source is the fractal noise (or called 1/f noise with a power spectral density of 1/fa (0wavelet decomposition can play the role of a Karhunen-Loève-type expansion to the 1/f-type signal by its decorrelation abilities, an effective energy detection method based on undecimated discrete wavelet transform (UDWT is proposed in this paper. Firstly, the foundations of magnetic anomaly detection and UDWT are introduced in brief, while a possible detection system based on giant magneto-impedance (GMI magnetic sensor is also given out. Then our proposed energy detection based on UDWT is described in detail, and the probabilities of false alarm and detection for given the detection threshold in theory are presented. It is noticeable that no a priori assumptions regarding the ferromagnetic target or the magnetic noise probability are necessary for our method, and different from the discrete wavelet transform (DWT, the UDWT is shift invariant. Finally, some simulations are performed and the results show that the detection performance of our proposed detector is better than that of the conventional energy detector even utilized in the Gaussian white noise, especially when the spectral parameter α is less than 1.0. In addition, a real-world experiment was done to demonstrate the advantages of the proposed method.

  9. 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

  10. Wavelet processing techniques for digital mammography

    Science.gov (United States)

    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).

  11. 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.

  12. 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

  13. The use of wavelet transform in oil conveying pipeline's leakage detection and location

    International Nuclear Information System (INIS)

    Han Jian; Wang Yongtao; Mu Haiwei

    2006-01-01

    This paper uses the negative press wave method basing on wavelet transform to detect the singularity point of the pressure signal of oil conveying pipeline and uses the detected singularity point to locate the leakage, this method has better effect in real applying. And this paper analyzes the merits and shortcomings of this method in detecting and locating the leakage of oil conveying pipeline, so it offers reference for studying weak leakage of oil conveying pipeline in the future. (authors)

  14. Experimental study on the crack detection with optimized spatial wavelet analysis and windowing

    Science.gov (United States)

    Ghanbari Mardasi, Amir; Wu, Nan; Wu, Christine

    2018-05-01

    In this paper, a high sensitive crack detection is experimentally realized and presented on a beam under certain deflection by optimizing spatial wavelet analysis. Due to the crack existence in the beam structure, a perturbation/slop singularity is induced in the deflection profile. Spatial wavelet transformation works as a magnifier to amplify the small perturbation signal at the crack location to detect and localize the damage. The profile of a deflected aluminum cantilever beam is obtained for both intact and cracked beams by a high resolution laser profile sensor. Gabor wavelet transformation is applied on the subtraction of intact and cracked data sets. To improve detection sensitivity, scale factor in spatial wavelet transformation and the transformation repeat times are optimized. Furthermore, to detect the possible crack close to the measurement boundaries, wavelet transformation edge effect, which induces large values of wavelet coefficient around the measurement boundaries, is efficiently reduced by introducing different windowing functions. The result shows that a small crack with depth of less than 10% of the beam height can be localized with a clear perturbation. Moreover, the perturbation caused by a crack at 0.85 mm away from one end of the measurement range, which is covered by wavelet transform edge effect, emerges by applying proper window functions.

  15. Significance tests for the wavelet cross spectrum and wavelet linear coherence

    Directory of Open Access Journals (Sweden)

    Z. Ge

    2008-12-01

    with simulated signals, nearly constant phase angles of the wavelet cross spectrum are found to coincide with large values in the wavelet linear coherence between the winds and the waves. Not limited to geophysics, the significance tests developed in the present work can also be applied to many other quantitative studies using the continuous wavelet transform.

  16. Decompositions of bubbly flow PIV velocity fields using discrete wavelets multi-resolution and multi-section image method

    International Nuclear Information System (INIS)

    Choi, Je-Eun; Takei, Masahiro; Doh, Deog-Hee; Jo, Hyo-Jae; Hassan, Yassin A.; Ortiz-Villafuerte, Javier

    2008-01-01

    Currently, wavelet transforms are widely used for the analyses of particle image velocimetry (PIV) velocity vector fields. This is because the wavelet provides not only spatial information of the velocity vectors, but also of the time and frequency domains. In this study, a discrete wavelet transform is applied to real PIV images of bubbly flows. The vector fields obtained by a self-made cross-correlation PIV algorithm were used for the discrete wavelet transform. The performances of the discrete wavelet transforms were investigated by changing the level of power of discretization. The images decomposed by wavelet multi-resolution showed conspicuous characteristics of the bubbly flows for the different levels. A high spatial bubble concentrated area could be evaluated by the constructed discrete wavelet transform algorithm, in which high-leveled wavelets play dominant roles in revealing the flow characteristics

  17. Wavelet-Based Methodology for Evolutionary Spectra Estimation of Nonstationary Typhoon Processes

    Directory of Open Access Journals (Sweden)

    Guang-Dong Zhou

    2015-01-01

    Full Text Available Closed-form expressions are proposed to estimate the evolutionary power spectral density (EPSD of nonstationary typhoon processes by employing the wavelet transform. Relying on the definition of the EPSD and the concept of the wavelet transform, wavelet coefficients of a nonstationary typhoon process at a certain time instant are interpreted as the Fourier transform of a new nonstationary oscillatory process, whose modulating function is equal to the modulating function of the nonstationary typhoon process multiplied by the wavelet function in time domain. Then, the EPSD of nonstationary typhoon processes is deduced in a closed form and is formulated as a weighted sum of the squared moduli of time-dependent wavelet functions. The weighted coefficients are frequency-dependent functions defined by the wavelet coefficients of the nonstationary typhoon process and the overlapping area of two shifted wavelets. Compared with the EPSD, defined by a sum of the squared moduli of the wavelets in frequency domain in literature, this paper provides an EPSD estimation method in time domain. The theoretical results are verified by uniformly modulated nonstationary typhoon processes and non-uniformly modulated nonstationary typhoon processes.

  18. Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles

    International Nuclear Information System (INIS)

    Lee, Seongjun; Kim, Jonghoon

    2015-01-01

    Sophisticated data of the experimental DCV (discharging/charging voltage) of a lithium-ion battery is required for high-accuracy SOC (state-of-charge) estimation algorithms based on the state-space ECM (electrical circuit model) in BMSs (battery management systems). However, when sensing noisy DCV signals, erroneous SOC estimation (which results in low BMS performance) is inevitable. Therefore, this manuscript describes the design and implementation of a DWT (discrete wavelet transform)-based denoising technique for DCV signals. The steps for denoising a noisy DCV measurement in the proposed approach are as follows. First, using MRA (multi-resolution analysis), the noise-riding DCV signal is decomposed into different frequency sub-bands (low- and high-frequency components, A n and D n ). Specifically, signal processing of the high frequency component D n that focuses on a short-time interval is necessary to reduce noise in the DCV measurement. Second, a hard-thresholding-based denoising rule is applied to adjust the wavelet coefficients of the DWT to achieve a clear separation between the signal and the noise. Third, the desired de-noised DCV signal is reconstructed by taking the IDWT (inverse discrete wavelet transform) of the filtered detailed coefficients. Finally, this signal is sent to the ECM-based SOC estimation algorithm using an EKF (extended Kalman filter). Experimental results indicate the robustness of the proposed approach for reliable SOC estimation. - Highlights: • Sophisticated data of the experimental DCV is required for high-accuracy SOC. • DWT (discrete wavelet transform)-based denoising technique is newly investigated. • Three steps for denoising a noisy DCV measurement in this work are implemented. • Experimental results indicate the robustness of the proposed work for reliable SOC

  19. Power-law behaviour evaluation from foreign exchange market data using a wavelet transform method

    Science.gov (United States)

    Wei, H. L.; Billings, S. A.

    2009-09-01

    Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A simple new statistical approach, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

  20. Power-law behaviour evaluation from foreign exchange market data using a wavelet transform method

    Energy Technology Data Exchange (ETDEWEB)

    Wei, H.L., E-mail: w.hualiang@sheffield.ac.u [Department of Automatic Control and Systems Engineering, the University of Sheffield, Mappin Street, Sheffield, S1 3JD (United Kingdom); Billings, S.A., E-mail: s.billings@sheffield.ac.u [Department of Automatic Control and Systems Engineering, the University of Sheffield, Mappin Street, Sheffield, S1 3JD (United Kingdom)

    2009-09-07

    Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A simple new statistical approach, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

  1. Power-law behaviour evaluation from foreign exchange market data using a wavelet transform method

    International Nuclear Information System (INIS)

    Wei, H.L.; Billings, S.A.

    2009-01-01

    Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A simple new statistical approach, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

  2. Noise removal for medical X-ray images in wavelet domain

    International Nuclear Information System (INIS)

    Wang, Ling; Lu, Jianming; Li, Yeqiu; Yahagi, Takashi; Okamoto, Takahide

    2006-01-01

    Many important problems in engineering and science are well-modeled by Poisson noise, the noise of medical X-ray image is Poisson noise. In this paper, we propose a method of noise removal for degraded medical X-ray image using improved preprocessing and improved BayesShrink (IBS) method in wavelet domain. Firstly, we pre-process the medical X-ray image, Secondly, we apply the Daubechies (db) wavelet transform to medical X-ray image to acquire scaling and wavelet coefficients. Thirdly, we apply the proposed IBS method to process wavelet coefficients. Finally, we compute the inverse wavelet transform for the thresholded coefficeints. Experimental results show that the proposed method always outperforms traditional methods. (author)

  3. Psychoacoustic Music Analysis Based on the Discrete Wavelet Packet Transform

    Directory of Open Access Journals (Sweden)

    Xing He

    2008-01-01

    Full Text Available Psychoacoustical computational models are necessary for the perceptual processing of acoustic signals and have contributed significantly in the development of highly efficient audio analysis and coding. In this paper, we present an approach for the psychoacoustic analysis of musical signals based on the discrete wavelet packet transform. The proposed method mimics the multiresolution properties of the human ear closer than other techniques and it includes simultaneous and temporal auditory masking. Experimental results show that this method provides better masking capabilities and it reduces the signal-to-masking ratio substantially more than other approaches, without introducing audible distortion. This model can lead to greater audio compression by permitting further bit rate reduction and more secure watermarking by providing greater signal space for information hiding.

  4. 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

  5. A wavelet transform algorithm for peak detection and application to powder x-ray diffraction data.

    Science.gov (United States)

    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.

  6. 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.

  7. 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...

  8. Multiplexed wavelet transform technique for detection of microcalcification in digitized mammograms.

    Science.gov (United States)

    Mini, M G; Devassia, V P; Thomas, Tessamma

    2004-12-01

    Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent-child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.

  9. 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

  10. 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.

  11. A Comparative Analysis for Selection of Appropriate Mother Wavelet for Detection of Stationary Disturbances

    Science.gov (United States)

    Kamble, Saurabh Prakash; Thawkar, Shashank; Gaikwad, Vinayak G.; Kothari, D. P.

    2017-12-01

    Detection of disturbances is the first step of mitigation. Power electronics plays a crucial role in modern power system which makes system operation efficient but it also bring stationary disturbances in the power system and added impurities to the supply. It happens because of the non-linear loads used in modern day power system which inject disturbances like harmonic disturbances, flickers, sag etc. in power grid. These impurities can damage equipments so it is necessary to mitigate these impurities present in the supply very quickly. So, digital signal processing techniques are incorporated for detection purpose. Signal processing techniques like fast Fourier transform, short-time Fourier transform, Wavelet transform etc. are widely used for the detection of disturbances. Among all, wavelet transform is widely used because of its better detection capabilities. But, which mother wavelet has to use for detection is still a mystery. Depending upon the periodicity, the disturbances are classified as stationary and non-stationary disturbances. This paper presents the importance of selection of mother wavelet for analyzing stationary disturbances using discrete wavelet transform. Signals with stationary disturbances of various frequencies are generated using MATLAB. The analysis of these signals is done using various mother wavelets like Daubechies and bi-orthogonal wavelets and the measured root mean square value of stationary disturbance is obtained. The measured value obtained by discrete wavelet transform is compared with the exact RMS value of the frequency component and the percentage differences are presented which helps to select optimum mother wavelet.

  12. Nanoscale displacement measurement by a digital nano-moire method with wavelet transformation

    International Nuclear Information System (INIS)

    Liu, C-M; Chen, L-W; Wang, C-C

    2006-01-01

    A digital nano-moire method with wavelet transformation is explored to measure nanoscale in-plane displacement fields. By applying e-beam lithography, a periodic PMMA nanostructure array is fabricated directly on the specimen and used as the specimen grating. Moire patterns are generated by overlapping the images of the PMMA specimen grating obtained from AFM scanning and the virtual reference grating produced by a digital image generating process. Then, the overlapped images are filtered by the 2D wavelet transformation (WT) to capture the target moire patterns. Existing methods, by overlapping the monitor-generated scanning lines with the image of the specimen grating, cause a mismatch problem. Previously, the carrier moire method was explored with the aim of curing the mismatch problem. Unfortunately, the carrier moire method, in addition to suffering from increased complexity of mathematical calculations, is incapable of directly obtaining the displacement field. Thus, the mismatch problem will result in inconveniences and restrictions in the practical application. Instead of using monitor-generated scanning lines, the proposed method applies the virtual reference grating, and thus puts the mismatch problem to rest. Nevertheless, the resultant moire image suffers from low contrast which, if left untreated, might distort the measurement result. Therefore, the WT, known for its sharpened abilities of characteristic and edge detection, is used to capture the target moire patterns and improve the measurement accuracy. The proposed method has been carried out in the laboratory. Experimental results have shown that the proposed method is convenient and efficient for nanoscale displacement measurement

  13. EXPERIMENTAL INVESTIGATION FOR FAULT DI AGNOSIS BASED ON FFT AND WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    MIHAIL PRICOP

    2016-06-01

    Full Text Available Belts are components of the mechanical systems of rotation commonly used for mechanical power transmission and changes in rotational speeds in the shafts. Various failures of the drive belts (foot shear, tooth wear, hollowed teeth, back cracks are common in rotating machines and can cause economic losses. To increase efficiency, reliability and safety of the machines the use of new fault diagnosis techniques of belts, identification and classification is required. In this paper Fast Fourier Transform (FFT and Wavelet transform complementary methods are used for fault monitoring of drive belts, analyzing in this way the limitations and advantages of using these methods. Experimental investigations for the fault diagnosis of drive belts are made using experimental platform and Bruel & Kjaer equipment for measuring vibration and PULSE and MATLAB software for recorded signal processing. The results were analyzed and presented.

  14. Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

    Directory of Open Access Journals (Sweden)

    Patcharoen Theerasak

    2018-04-01

    Full Text Available This paper describes the combination of discrete wavelet transforms (DWT and artificial intelligence (AI, which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51 and unbalanced current (60C protection relay for an application of shunt capacitor bank protection in the future.

  15. Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

    Science.gov (United States)

    Patcharoen, Theerasak; Yoomak, Suntiti; Ngaopitakkul, Atthapol; Pothisarn, Chaichan

    2018-04-01

    This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

  16. Short-term wind power forecasting in Portugal by neural networks and wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-04-15

    This paper proposes artificial neural networks in combination with wavelet transform for short-term wind power forecasting in Portugal. The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. Results from a real-world case study are presented. A comparison is carried out, taking into account the results obtained with other approaches. Finally, conclusions are duly drawn. (author)

  17. 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)

  18. Wavelet-domain de-noising of OCT images of human brain malignant glioma

    Science.gov (United States)

    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.

  19. Time variation of the electromagnetic transfer function of the earth estimated by using wavelet transform.

    Science.gov (United States)

    Suto, Noriko; Harada, Makoto; Izutsu, Jun; Nagao, Toshiyasu

    2006-07-01

    In order to accurately estimate the geomagnetic transfer functions in the area of the volcano Mt. Iwate (IWT), we applied the interstation transfer function (ISTF) method to the three-component geomagnetic field data observed at Mt. Iwate station (IWT), using the Kakioka Magnetic Observatory, JMA (KAK) as remote reference station. Instead of the conventional Fourier transform, in which temporary transient noises badly degrade the accuracy of long term properties, continuous wavelet transform has been used. The accuracy of the results was as high as that of robust estimations of transfer functions obtained by the Fourier transform method. This would provide us with possibilities for routinely monitoring the transfer functions, without sophisticated statistical procedures, to detect changes in the underground electrical conductivity structure.

  20. Wavelet based free-form deformations for nonrigid registration

    Science.gov (United States)

    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.

  1. THE APPLICATION OF CONTINUOUS WAVELET TRANSFORM BASED FOREGROUND SUBTRACTION METHOD IN 21 cm SKY SURVEYS

    International Nuclear Information System (INIS)

    Gu Junhua; Xu Haiguang; Wang Jingying; Chen Wen; An Tao

    2013-01-01

    We propose a continuous wavelet transform based non-parametric foreground subtraction method for the detection of redshifted 21 cm signal from the epoch of reionization. This method works based on the assumption that the foreground spectra are smooth in frequency domain, while the 21 cm signal spectrum is full of saw-tooth-like structures, thus their characteristic scales are significantly different. We can distinguish them in the wavelet coefficient space easily and perform the foreground subtraction. Compared with the traditional spectral fitting based method, our method is more tolerant to complex foregrounds. Furthermore, we also find that when the instrument has uncorrected response error, our method can also work significantly better than the spectral fitting based method. Our method can obtain similar results with the Wp smoothing method, which is also a non-parametric method, but our method consumes much less computing time

  2. Estimation of the Tool Condition by Applying the Wavelet Transform to Acoustic Emission Signals

    International Nuclear Information System (INIS)

    Gomez, M. P.; Piotrkowski, R.; Ruzzante, J. E.; D'Attellis, C. E.

    2007-01-01

    This work follows the search of parameters to evaluate the tool condition in machining processes. The selected sensing technique is acoustic emission and it is applied to a turning process of steel samples. The obtained signals are studied using the wavelet transformation. The tool wear level is quantified as a percentage of the final wear specified by the Standard ISO 3685. The amplitude and relevant scale obtained of acoustic emission signals could be related with the wear level

  3. A New Prediction Model for Transformer Winding Hotspot Temperature Fluctuation Based on Fuzzy Information Granulation and an Optimized Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Li Zhang

    2017-12-01

    Full Text Available Winding hotspot temperature is the key factor affecting the load capacity and service life of transformers. For the early detection of transformer winding hotspot temperature anomalies, a new prediction model for the hotspot temperature fluctuation range based on fuzzy information granulation (FIG and the chaotic particle swarm optimized wavelet neural network (CPSO-WNN is proposed in this paper. The raw data are firstly processed by FIG to extract useful information from each time window. The extracted information is then used to construct a wavelet neural network (WNN prediction model. Furthermore, the structural parameters of WNN are optimized by chaotic particle swarm optimization (CPSO before it is used to predict the fluctuation range of the hotspot temperature. By analyzing the experimental data with four different prediction models, we find that the proposed method is more effective and is of guiding significance for the operation and maintenance of transformers.

  4. Assessment of Fluctuation Patterns Similarity in Temperature and Vapor Pressure Using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    A. Araghi

    2014-12-01

    Full Text Available Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. Wavelet transform is a mathematical based powerful method which has been widely used in signal processing and time series analysis in recent years. In this research, trend and main periodic patterns similarity in temperature and vapor pressure has been studied in Babolsar, Tehran and Shahroud synoptic stations during 55 years period (from 1956 to 2010, using wavelet method and the sequential Mann-Kendall trend test. The results show that long term fluctuation patterns in temperature and vapor pressure have more correlations in the arid and semi-arid climates, as well as short term oscillation patterns in temperature and vapor pressure in the humid climates, and these dominant periods increase with the aridity of region.

  5. Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

    Science.gov (United States)

    Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

    2018-04-01

    Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

  6. QRS analysis using wavelet transformation for the prediction of response to cardiac resynchronization therapy: a prospective pilot study.

    Science.gov (United States)

    Vassilikos, Vassilios P; Mantziari, Lilian; Dakos, Georgios; Kamperidis, Vasileios; Chouvarda, Ioanna; Chatzizisis, Yiannis S; Kalpidis, Panagiotis; Theofilogiannakos, Efstratios; Paraskevaidis, Stelios; Karvounis, Haralambos; Mochlas, Sotirios; Maglaveras, Nikolaos; Styliadis, Ioannis H

    2014-01-01

    Wider QRS and left bundle branch block morphology are related to response to cardiac resynchronization therapy (CRT). A novel time-frequency analysis of the QRS complex may provide additional information in predicting response to CRT. Signal-averaged electrocardiograms were prospectively recorded, before CRT, in orthogonal leads and QRS decomposition in three frequency bands was performed using the Morlet wavelet transformation. Thirty eight patients (age 65±10years, 31 males) were studied. CRT responders (n=28) had wider baseline QRS compared to non-responders and lower QRS energies in all frequency bands. The combination of QRS duration and mean energy in the high frequency band had the best predicting ability (AUC 0.833, 95%CI 0.705-0.962, p=0.002) followed by the maximum energy in the high frequency band (AUC 0.811, 95%CI 0.663-0.960, p=0.004). Wavelet transformation of the QRS complex is useful in predicting response to CRT. © 2013.

  7. Locating and classification of structure-borne sound occurrence using wavelet transformation

    International Nuclear Information System (INIS)

    Winterstein, Martin; Thurnreiter, Martina

    2011-01-01

    For the surveillance of nuclear facilities with respect to detached or loose parts within the pressure boundary structure-borne sound detector systems are used. The impact of loose parts on the wall causes energy transfer to the wall that is measured a so called singular sound event. The run-time differences of sound signals allow a rough locating of the loose part. The authors performed a finite element based simulation of structure-borne sound measurements using real geometries. New knowledge on sound wave propagation, signal analysis and processing, neuronal networks or hidden Markov models were considered. Using the wavelet transformation it is possible to improve the localization of structure-borne sound events.

  8. 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.

  9. Wavelet denoising method; application to the flow rate estimation for water level control

    International Nuclear Information System (INIS)

    Park, Gee Young; Park, Jin Ho; Lee, Jung Han; Kim, Bong Soo; Seong, Poong Hyun

    2003-01-01

    The wavelet transform decomposes a signal into time- and frequency-domain signals and it is well known that a noise-corrupted signal could be reconstructed or estimated when a proper denoising method is involved in the wavelet transform. Among the wavelet denoising methods proposed up to now, the wavelets by Mallat and Zhong can reconstruct best the pure transient signal from a highly corrupted signal. But there has been no systematic way of discriminating the original signal from the noise in a dyadic wavelet transform. In this paper, a systematic method is proposed for noise discrimination, which could be implemented easily into a digital system. For demonstrating the potential role of the wavelet denoising method in the nuclear field, this method is applied to the steam or feedwater flow rate estimation of the secondary loop. And the configuration of the S/G water level control system is proposed for incorporating the wavelet denoising method in estimating the flow rate value at low operating powers

  10. 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.

  11. Combined wavelet transform-artificial neural network use in tablet active content determination by near-infrared spectroscopy.

    Science.gov (United States)

    Chalus, Pascal; Walter, Serge; Ulmschneider, Michel

    2007-05-22

    The pharmaceutical industry faces increasing regulatory pressure to optimize quality control. Content uniformity is a basic release test for solid dosage forms. To accelerate test throughput and comply with the Food and Drug Administration's process analytical technology initiative, attention is increasingly turning to nondestructive spectroscopic techniques, notably near-infrared (NIR) spectroscopy (NIRS). However, validation of NIRS using requisite linearity and standard error of prediction (SEP) criteria remains a challenge. This study applied wavelet transformation of the NIR spectra of a commercial tablet to build a model using conventional partial least squares (PLS) regression and an artificial neural network (ANN). Wavelet coefficients in the PLS and ANN models reduced SEP by up to 60% compared to PLS models using mathematical spectra pretreatment. ANN modeling yielded high-linearity calibration and a correlation coefficient exceeding 0.996.

  12. Combining Haar Wavelet and Karhunen Loeve Transforms for Medical Images Watermarking

    Directory of Open Access Journals (Sweden)

    Mohamed Ali Hajjaji

    2014-01-01

    Full Text Available This paper presents a novel watermarking method, applied to the medical imaging domain, used to embed the patient’s data into the corresponding image or set of images used for the diagnosis. The main objective behind the proposed technique is to perform the watermarking of the medical images in such a way that the three main attributes of the hidden information (i.e., imperceptibility, robustness, and integration rate can be jointly ameliorated as much as possible. These attributes determine the effectiveness of the watermark, resistance to external attacks, and increase the integration rate. In order to improve the robustness, a combination of the characteristics of Discrete Wavelet and Karhunen Loeve Transforms is proposed. The Karhunen Loeve Transform is applied on the subblocks (sized 8×8 of the different wavelet coefficients (in the HL2, LH2, and HH2 subbands. In this manner, the watermark will be adapted according to the energy values of each of the Karhunen Loeve components, with the aim of ensuring a better watermark extraction under various types of attacks. For the correct identification of inserted data, the use of an Errors Correcting Code (ECC mechanism is required for the check and, if possible, the correction of errors introduced into the inserted data. Concerning the enhancement of the imperceptibility factor, the main goal is to determine the optimal value of the visibility factor, which depends on several parameters of the DWT and the KLT transforms. As a first step, a Fuzzy Inference System (FIS has been set up and then applied to determine an initial visibility factor value. Several features extracted from the Cooccurrence matrix are used as an input to the FIS and used to determine an initial visibility factor for each block; these values are subsequently reweighted in function of the eigenvalues extracted from each subblock. Regarding the integration rate, the previous works insert one bit per coefficient. In our

  13. A continuous wavelet transform approach for harmonic parameters estimation in the presence of impulsive noise

    Science.gov (United States)

    Dai, Yu; Xue, Yuan; Zhang, Jianxun

    2016-01-01

    Impulsive noise caused by some random events has the main character of short rise-time and wide frequency spectrum range, so it has the potential to degrade the performance and reliability of the harmonic estimation. This paper focuses on the harmonic estimation procedure based on continuous wavelet transform (CWT) when the analyzed signal is corrupted by the impulsive noise. The digital CWT of both the time-varying sinusoidal signal and the impulsive noise are analyzed, and there are two cross ridges in the time-frequency plane of CWT, which are generated by the signal and the noise separately. In consideration of the amplitude of the noise and the number of the spike event, two inequalities are derived to provide limitations on the wavelet parameters. Based on the amplitude distribution of the noise, the optimal wavelet parameters determined by solving these inequalities are used to suppress the contamination of the noise, as well as increase the amplitude of the ridge corresponding to the signal, so the parameters of each harmonic component can be estimated accurately. The proposed procedure is applied to a numerical simulation and a bone vibration signal test giving satisfactory results of stationary and time-varying harmonic parameter estimation.

  14. Digital transceiver implementation for wavelet packet modulation

    Science.gov (United States)

    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.

  15. 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.

  16. Damage monitoring of aircraft structures made of composite materials using wavelet transforms

    Science.gov (United States)

    Molchanov, D.; Safin, A.; Luhyna, N.

    2016-10-01

    The present article is dedicated to the study of the acoustic properties of composite materials and the application of non-destructive testing methods to aircraft components. A mathematical model of a wavelet transformed signal is presented. The main acoustic (vibration) properties of different composite material structures were researched. Multiple vibration parameter dependencies on the noise reduction factor were derived. The main steps of a research procedure and new method algorithm are presented. The data obtained was compared with the data from a three dimensional laser-Doppler scanning vibrometer, to validate the results. The new technique was tested in the laboratory and on civil aircraft at a training airfield.

  17. Wavelet Filter Banks for Super-Resolution SAR Imaging

    Science.gov (United States)

    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.

  18. SECURE VISUAL SECRET SHARING BASED ON DISCRETE WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    S. Jyothi Lekshmi

    2015-08-01

    Full Text Available Visual Cryptography Scheme (VCS is an encryption method to encode secret written materials. This method converts the secret written material into an image. Then encode this secret image into n shadow images called shares. For the recreation of the original secret, all or some selected subsets of shares are needed; individual shares are of no use on their own. The secret image can be recovered simply by selecting some subset of these n shares, makes transparencies of them and stacking on top of each other. Nowadays, the data security has an important role. The shares can be altered by an attacker. So providing security to the shares is important. This paper proposes a method of adding security to cryptographic shares. This method uses two dimensional discrete wavelet transform to hide visual secret shares. Then the hidden secrets are distributed among participants through the internet. All hidden shares are extracted to reconstruct the secret.

  19. Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

    Science.gov (United States)

    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.

  20. SeismicWaveTool: Continuous and discrete wavelet analysis and filtering for multichannel seismic data

    Science.gov (United States)

    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

  1. Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

    Science.gov (United States)

    Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

    2018-05-29

    Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

  2. A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

    International Nuclear Information System (INIS)

    Tang, Hui; Tong, Dan; Dong Bao, Xu; Dillenseger, Jean-Louis

    2015-01-01

    Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time

  3. A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform

    Energy Technology Data Exchange (ETDEWEB)

    Tang, Hui, E-mail: corinna@seu.edu.cn [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096 (China); Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210000 (China); Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000 (France); Southeast University, Nanjing 210000 (China); Tong, Dan; Dong Bao, Xu [Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 210096 (China); Dillenseger, Jean-Louis [INSERM, U1099, Rennes F-35000 (France); Université de Rennes 1, LTSI, Rennes F-35000 (France); Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000 (France); Southeast University, Nanjing 210000 (China)

    2015-04-15

    Purpose: In digital x-ray radiography, an antiscatter grid is inserted between the patient and the image receptor to reduce scattered radiation. If the antiscatter grid is used in a stationary way, gridline artifacts will appear in the final image. In most of the gridline removal image processing methods, the useful information with spatial frequencies close to that of the gridline is usually lost or degraded. In this study, a new stationary gridline suppression method is designed to preserve more of the useful information. Methods: The method is as follows. The input image is first recursively decomposed into several smaller subimages using a multiscale 2D discrete wavelet transform. The decomposition process stops when the gridline signal is found to be greater than a threshold in one or several of these subimages using a gridline detection module. An automatic Gaussian band-stop filter is then applied to the detected subimages to remove the gridline signal. Finally, the restored image is achieved using the corresponding 2D inverse discrete wavelet transform. Results: The processed images show that the proposed method can remove the gridline signal efficiently while maintaining the image details. The spectra of a 1D Fourier transform of the processed images demonstrate that, compared with some existing gridline removal methods, the proposed method has better information preservation after the removal of the gridline artifacts. Additionally, the performance speed is relatively high. Conclusions: The experimental results demonstrate the efficiency of the proposed method. Compared with some existing gridline removal methods, the proposed method can preserve more information within an acceptable execution time.

  4. Determination of phase from the ridge of CWT using generalized Morse wavelet

    Science.gov (United States)

    Kocahan, Ozlem; Tiryaki, Erhan; Coskun, Emre; Ozder, Serhat

    2018-03-01

    The selection of wavelet is an important step in order to determine the phase from the fringe patterns. In the present work, a new wavelet for phase retrieval from the ridge of continuous wavelet transform (CWT) is presented. The phase distributions have been extracted from the optical fringe pattern by choosing the zero order generalized morse wavelet (GMW) as a mother wavelet. The aim of the study is to reveal the ways in which the two varying parameters of GMW affect the phase calculation. To show the validity of this method, an experimental study has been conducted by using the diffraction phase microscopy (DPM) setup; consequently, the profiles of red blood cells have been retrieved. The results for the CWT ridge technique with GMW have been compared with the results for the Morlet wavelet and the Paul wavelet; the results are almost identical for Paul and zero order GMW because of their degree of freedom. Also, for further discussion, the Fourier transform and the Stockwell transform have been applied comparatively. The outcome of the comparison reveals that GMWs are highly applicable to the research in various areas, predominantly biomedicine.

  5. Evaluation of the Use of Second Generation Wavelets in the Coherent Vortex Simulation Approach

    Science.gov (United States)

    Goldstein, D. E.; Vasilyev, O. V.; Wray, A. A.; Rogallo, R. S.

    2000-01-01

    The objective of this study is to investigate the use of the second generation bi-orthogonal wavelet transform for the field decomposition in the Coherent Vortex Simulation of turbulent flows. The performances of the bi-orthogonal second generation wavelet transform and the orthogonal wavelet transform using Daubechies wavelets with the same number of vanishing moments are compared in a priori tests using a spectral direct numerical simulation (DNS) database of isotropic turbulence fields: 256(exp 3) and 512(exp 3) DNS of forced homogeneous turbulence (Re(sub lambda) = 168) and 256(exp 3) and 512(exp 3) DNS of decaying homogeneous turbulence (Re(sub lambda) = 55). It is found that bi-orthogonal second generation wavelets can be used for coherent vortex extraction. The results of a priori tests indicate that second generation wavelets have better compression and the residual field is closer to Gaussian. However, it was found that the use of second generation wavelets results in an integral length scale for the incoherent part that is larger than that derived from orthogonal wavelets. A way of dealing with this difficulty is suggested.

  6. Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph.

    Science.gov (United States)

    Acharya, Rajendra; Tan, Peck Ha; Subramaniam, Tavintharan; Tamura, Toshiyo; Chua, Kuang Chua; Goh, Seach Chyr Ernest; Lim, Choo Min; Goh, Shu Yi Diana; Chung, Kang Rui Conrad; Law, Chelsea

    2008-02-01

    Diabetes is a disorder of metabolism-the way our bodies use digested food for growth and energy. The most common form of diabetes is Type 2 diabetes. Abnormal plantar pressures are considered to play a major role in the pathologies of neuropathic ulcers in the diabetic foot. The purpose of this study was to examine the plantar pressure distribution in normal, diabetic Type 2 with and without neuropathy subjects. Foot scans were obtained using the F-scan (Tekscan USA) pressure measurement system. Various discrete wavelet coefficients were evaluated from the foot images. These extracted parameters were extracted using the discrete wavelet transform (DWT) and presented to the Gaussian mixture model (GMM) and a four-layer feed forward neural network for classification. We demonstrated a sensitivity of 100% and a specificity of more than 85% for the classifiers.

  7. Prediction of periodically correlated processes by wavelet transform and multivariate methods with applications to climatological data

    Science.gov (United States)

    Ghanbarzadeh, Mitra; Aminghafari, Mina

    2015-05-01

    This article studies the prediction of periodically correlated process using wavelet transform and multivariate methods with applications to climatological data. Periodically correlated processes can be reformulated as multivariate stationary processes. Considering this fact, two new prediction methods are proposed. In the first method, we use stepwise regression between the principal components of the multivariate stationary process and past wavelet coefficients of the process to get a prediction. In the second method, we propose its multivariate version without principal component analysis a priori. Also, we study a generalization of the prediction methods dealing with a deterministic trend using exponential smoothing. Finally, we illustrate the performance of the proposed methods on simulated and real climatological data (ozone amounts, flows of a river, solar radiation, and sea levels) compared with the multivariate autoregressive model. The proposed methods give good results as we expected.

  8. Removing Eddy-current probe wobble noise from steam generator tubes testing using wavelet transform

    International Nuclear Information System (INIS)

    Lopez, Luiz Antonio Negro Martin; Ting, Daniel Kao Sun; Upadhyaya, Belle R.

    2005-01-01

    One of the most import nondestructive evaluation (NDE) applied to steam generator tubes inspection is the electromagnetic Eddy-Current testing (ECT). The signals generated in this NDE, in general, contain many noises which make difficult the interpretation and analysis of ECT signals. One of the noises present in the signals is the probe wobble noise, which is caused by the existing slack between the probe and the tube. In this work, Wavelet Transform (WT) is used in the probe wobble de-noising. WT is a relatively recent mathematical tool, which allows local analysis of non stationary signals such as ECT signals. This is a great advantage of WT when compared with other analysis tools such as Fourier Transform. However, using WT involves wavelets and coefficients selection as well as choosing the number of decomposition level needed. This work presents a probe wobble de-noising method when used in conjunction with the traditional ECT evaluation. Comparative results using several WT applied do Eddy-Current signals are presented in a reliable way, in other words, without loss of inherent defect information. A stainless steel tube, with 2 artificial defects generated by electro-erosion, was inspected by a ZETEC MIZ-17ET ECT equipment. The signals were de-noised through several different WT and the results are presented. The method offer good results and is a promising method because allows for the removal of Eddy-Current signals probe wobble effect without loss of essential signal information. (author)

  9. Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy

    Science.gov (United States)

    Serra, Roger; Lopez, Lautaro

    2018-05-01

    Different approaches on the detection of damages based on dynamic measurement of structures have appeared in the last decades. They were based, amongst others, on changes in natural frequencies, modal curvatures, strain energy or flexibility. Wavelet analysis has also been used to detect the abnormalities on modal shapes induced by damages. However the majority of previous work was made with non-corrupted by noise signals. Moreover, the damage influence for each mode shape was studied separately. This paper proposes a new methodology based on combined modal wavelet transform strategy to cope with noisy signals, while at the same time, able to extract the relevant information from each mode shape. The proposed methodology will be then compared with the most frequently used and wide-studied methods from the bibliography. To evaluate the performance of each method, their capacity to detect and localize damage will be analyzed in different cases. The comparison will be done by simulating the oscillations of a cantilever steel beam with and without defect as a numerical case. The proposed methodology proved to outperform classical methods in terms of noisy signals.

  10. Image Denoising Using Singular Value Difference in the Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Min Wang

    2018-01-01

    Full Text Available Singular value (SV difference is the difference in the singular values between a noisy image and the original image; it varies regularly with noise intensity. This paper proposes an image denoising method using the singular value difference in the wavelet domain. First, the SV difference model is generated for different noise variances in the three directions of the wavelet transform and the noise variance of a new image is used to make the calculation by the diagonal part. Next, the single-level discrete 2-D wavelet transform is used to decompose each noisy image into its low-frequency and high-frequency parts. Then, singular value decomposition (SVD is used to obtain the SVs of the three high-frequency parts. Finally, the three denoised high-frequency parts are reconstructed by SVD from the SV difference, and the final denoised image is obtained using the inverse wavelet transform. Experiments show the effectiveness of this method compared with relevant existing methods.

  11. Wavelets in medical imaging

    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.

  12. Wavelets in medical imaging

    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.

  13. 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...

  14. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching.

    Science.gov (United States)

    Du, Pan; Kibbe, Warren A; Lin, Simon M

    2006-09-01

    A major problem for current peak detection algorithms is that noise in mass spectrometry (MS) spectra gives rise to a high rate of false positives. The false positive rate is especially problematic in detecting peaks with low amplitudes. Usually, various baseline correction algorithms and smoothing methods are applied before attempting peak detection. This approach is very sensitive to the amount of smoothing and aggressiveness of the baseline correction, which contribute to making peak detection results inconsistent between runs, instrumentation and analysis methods. Most peak detection algorithms simply identify peaks based on amplitude, ignoring the additional information present in the shape of the peaks in a spectrum. In our experience, 'true' peaks have characteristic shapes, and providing a shape-matching function that provides a 'goodness of fit' coefficient should provide a more robust peak identification method. Based on these observations, a continuous wavelet transform (CWT)-based peak detection algorithm has been devised that identifies peaks with different scales and amplitudes. By transforming the spectrum into wavelet space, the pattern-matching problem is simplified and in addition provides a powerful technique for identifying and separating the signal from the spike noise and colored noise. This transformation, with the additional information provided by the 2D CWT coefficients can greatly enhance the effective signal-to-noise ratio. Furthermore, with this technique no baseline removal or peak smoothing preprocessing steps are required before peak detection, and this improves the robustness of peak detection under a variety of conditions. The algorithm was evaluated with SELDI-TOF spectra with known polypeptide positions. Comparisons with two other popular algorithms were performed. The results show the CWT-based algorithm can identify both strong and weak peaks while keeping false positive rate low. The algorithm is implemented in R and will be

  15. Wavelet transform approach for fitting financial time series data

    Science.gov (United States)

    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.

  16. Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering.

    Science.gov (United States)

    Xia, Yong; Han, Junze; Wang, Kuanquan

    2015-01-01

    Based on the idea of telemedicine, 24-hour uninterrupted monitoring on electrocardiograms (ECG) has started to be implemented. To create an intelligent ECG monitoring system, an efficient and quick detection algorithm for the characteristic waveforms is needed. This paper aims to give a quick and effective method for detecting QRS-complexes and R-waves in ECGs. The real ECG signal from the MIT-BIH Arrhythmia Database is used for the performance evaluation. The method proposed combined a wavelet transform and the K-means clustering algorithm. A wavelet transform is adopted in the data analysis and preprocessing. Then, based on the slope information of the filtered data, a segmented K-means clustering method is adopted to detect the QRS region. Detection of the R-peak is based on comparing the local amplitudes in each QRS region, which is different from other approaches, and the time cost of R-wave detection is reduced. Of the tested 8 records (total 18201 beats) from the MIT-BIH Arrhythmia Database, an average R-peak detection sensitivity of 99.72 and a positive predictive value of 99.80% are gained; the average time consumed detecting a 30-min original signal is 5.78s, which is competitive with other methods.

  17. Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion

    Directory of Open Access Journals (Sweden)

    Triwiyanto Triwiyanto

    2017-01-01

    Full Text Available Studying muscle fatigue plays an important role in preventing the risks associated with musculoskeletal disorders. The effect of elbow-joint angle on time-frequency parameters during a repetitive motion provides valuable information in finding the most accurate position of the angle causing muscle fatigue. Therefore, the purpose of this study is to analyze the effect of muscle fatigue on the spectral and time-frequency domain parameters derived from electromyography (EMG signals using the Continuous Wavelet Transform (CWT. Four male participants were recruited to perform a repetitive motion (flexion and extension movements from a non-fatigue to fatigue condition. EMG signals were recorded from the biceps muscle. The recorded EMG signals were then analyzed offline using the complex Morlet wavelet. The time-frequency domain data were analyzed using the time-averaged wavelet spectrum (TAWS and the Scale-Average Wavelet Power (SAWP parameters. The spectral domain data were analyzed using the Instantaneous Mean Frequency (IMNF and the Instantaneous Mean Power Spectrum (IMNP parameters. The index of muscle fatigue was observed by calculating the increase of the IMNP and the decrease of the IMNF parameters. After performing a repetitive motion from non-fatigue to fatigue condition, the average of the IMNF value decreased by 15.69% and the average of the IMNP values increased by 84%, respectively. This study suggests that the reliable frequency band to detect muscle fatigue is 31.10-36.19Hz with linear regression parameters of 0.979mV^2Hz^(-1 and 0.0095mV^2Hz^(-1 for R^2 and slope, respectively.

  18. Wavelet-based moment invariants for pattern recognition

    Science.gov (United States)

    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.

  19. Wavelet Analysis of Ultrasonic Echo Waveform and Application to Nondestructive Evaluation

    International Nuclear Information System (INIS)

    Park, Ik Keun; Park, Un Su; Ahn, Hyung Keun; Kwun, Sook In; Byeon, Jai Won

    2000-01-01

    Recently, advanced signal analysis which is called 'time-frequency analysis' has been used widely in nondestructive evaluation applications. Wavelet transform(WT) and Wigner Distribution are the most advanced techniques for processing signals with time-varying spectra. Wavelet analysis method is an attractive technique for evaluation of material characterization nondestructively. Wavelet transform is applied to the time-frequency analysis of ultrasonic echo waveform obtained by an ultrasonic pulse-echo technique. In this study, the feasibility of noise suppression of ultrasonic flaw signal and frequency-dependent ultrasonic group velocity and attenuation coefficient using wavelet analysis of ultrasonic echo waveform have been verified experimentally. The Gabor function is adopted the analyzing wavelet. The wavelet analysis shows that the variations of ultrasonic group velocity and attenuation coefficient due to the change of material characterization can be evaluated at each frequency. Furthermore, to assure the enhancement of detectability and new sizing performance, both computer simulated results and experimental measurements using wavelet signal processing are used to demonstrate the effectiveness of the noise suppression of ultrasonic flaw signal obtained from austenitic stainless steel weld including EDM notch

  20. 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

  1. Response of Autonomic Nervous System to Body Positions: Fourier and Wavelet Analysis

    OpenAIRE

    Xu, Aiguo; Gonnella, G.; Federici, A.; Stramaglia, S.; Simone, F.; Zenzola, A.; Santostasi, R.

    2003-01-01

    Two mathematical methods, the Fourier and wavelet transforms, were used to study the short term cardiovascular control system. Time series, picked from electrocardiogram and arterial blood pressure lasting 6 minutes, were analyzed in supine position (SUP), during the first (HD1), and the second parts (HD2) of $90^{\\circ}$ head down tilt and during recovery (REC). The wavelet transform was performed using the Haar function of period $T=2^j$ ($% j=1$,2,$... $,6) to obtain wavelet coefficients. ...

  2. Automated pathologies detection in retina digital images based on complex continuous wavelet transform phase angles.

    Science.gov (United States)

    Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel

    2014-10-01

    An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity.

  3. State recognition of the viscoelastic sandwich structure based on the adaptive redundant second generation wavelet packet transform, permutation entropy and the wavelet support vector machine

    International Nuclear Information System (INIS)

    Qu, Jinxiu; Zhang, Zhousuo; Guo, Ting; Luo, Xue; Sun, Chuang; Li, Bing; Wen, Jinpeng

    2014-01-01

    The viscoelastic sandwich structure is widely used in mechanical equipment, yet the structure always suffers from damage during long-term service. Therefore, state recognition of the viscoelastic sandwich structure is very necessary for monitoring structural health states and keeping the equipment running with high reliability. Through the analysis of vibration response signals, this paper presents a novel method for this task based on the adaptive redundant second generation wavelet packet transform (ARSGWPT), permutation entropy (PE) and the wavelet support vector machine (WSVM). In order to tackle the non-linearity existing in the structure vibration response, the PE is introduced to reveal the state changes of the structure. In the case of complex non-stationary vibration response signals, in order to obtain more effective information regarding the structural health states, the ARSGWPT, which can adaptively match the characteristics of a given signal, is proposed to process the vibration response signals, and then multiple PE features are extracted from the resultant wavelet packet coefficients. The WSVM, which can benefit from the conventional SVM as well as wavelet theory, is applied to classify the various structural states automatically. In this study, to achieve accurate and automated state recognition, the ARSGWPT, PE and WSVM are combined for signal processing, feature extraction and state classification, respectively. To demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed, and the different degrees of preload on the structure are used to characterize the various looseness states. The test results show that the proposed method can reliably recognize the different looseness states of the viscoelastic sandwich structure, and the WSVM can achieve a better classification performance than the conventional SVM. Moreover, the superiority of the proposed ARSGWPT in processing the complex vibration response

  4. Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Wei Sun

    2015-01-01

    Full Text Available Electric power is a kind of unstorable energy concerning the national welfare and the people’s livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM which is optimized by fruit fly algorithm (FOA for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.

  5. 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

  6. Rate-distortion analysis of directional wavelets.

    Science.gov (United States)

    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

  7. 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.

  8. Sub-module Short Circuit Fault Diagnosis in Modular Multilevel Converter Based on Wavelet Transform and Adaptive Neuro Fuzzy Inference System

    DEFF Research Database (Denmark)

    Liu, Hui; Loh, Poh Chiang; Blaabjerg, Frede

    2015-01-01

    for continuous operation and post-fault maintenance. In this article, a fault diagnosis technique is proposed for the short circuit fault in a modular multi-level converter sub-module using the wavelet transform and adaptive neuro fuzzy inference system. The fault features are extracted from output phase voltage...

  9. Diameter and axial position measurement of micrometric particles by in-line digital holography using wavelet transform

    International Nuclear Information System (INIS)

    Torres, Y M; Amezquita, R; Monroy, F

    2011-01-01

    In this paper, the size and axial position of micrometric particles is obtained for an in-line Fraunhofer holography setup. The hologram reconstruction was realized using the wavelet transform. By digital image processing tools, the size distribution histogram for the particles in the sample was obtained. The contrast measurement in the amplitude reconstruction presents a peak when the axial coordinate and the register distance are equal. This fact lets the axial position in the sample be determined.

  10. Wavelet transform and ANNs for detection and classification of power signal disturbances

    International Nuclear Information System (INIS)

    Memon, A.P.; Uqaili, M.A.; Memon, Z.A.

    2012-01-01

    This article proposes WT (Wavelet Transform) and an ANN (Artificial Neural Network) based approach for detection and classification of EPQDs (Electrical Power Quality Disturbances). A modified WT known as ST (Stockwell Transform) is suggested for feature extraction and PNN (probabilistic Neural Network) for pattern classification. The ST possesses outstanding time-frequency resolution characteristics and its phase correction techniques determine the phase of the WT to the zero time point The feature vectors for the input of PNN are extracted using ST technique and these obtained features are discrete, logical, and unaffected to noisy data of distorted signals. The data of the models required to develop the distorted EPQ (Electrical Power Quality) signals, is obtained within the ranges specified by IEEE 1159-1995 in its literatures. The features vectors including noisy time varying data during steady state or transient condition and extracted using the ST, are trained through PNN for pattern classification. Their simulation results demonstrate that the proposed methodology is successful and can classify EPQDs even under a noisy environment very efficiently with an average classification accuracy of 96%. (author)

  11. Research on Methods of Infrared and Color Image Fusion Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Zhao Rentao

    2014-06-01

    Full Text Available There is significant difference in the imaging features of infrared image and color image, but their fusion images also have very good complementary information. In this paper, based on the characteristics of infrared image and color image, first of all, wavelet transform is applied to the luminance component of the infrared image and color image. In multi resolution the relevant regional variance is regarded as the activity measure, relevant regional variance ratio as the matching measure, and the fusion image is enhanced in the process of integration, thus getting the fused images by final synthesis module and multi-resolution inverse transform. The experimental results show that the fusion image obtained by the method proposed in this paper is better than the other methods in keeping the useful information of the original infrared image and the color information of the original color image. In addition, the fusion image has stronger adaptability and better visual effect.

  12. QIM blind video watermarking scheme based on Wavelet transform and principal component analysis

    Directory of Open Access Journals (Sweden)

    Nisreen I. Yassin

    2014-12-01

    Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.

  13. Fringe pattern demodulation using the one-dimensional continuous wavelet transform: field-programmable gate array implementation.

    Science.gov (United States)

    Abid, Abdulbasit

    2013-03-01

    This paper presents a thorough discussion of the proposed field-programmable gate array (FPGA) implementation for fringe pattern demodulation using the one-dimensional continuous wavelet transform (1D-CWT) algorithm. This algorithm is also known as wavelet transform profilometry. Initially, the 1D-CWT is programmed using the C programming language and compiled into VHDL using the ImpulseC tool. This VHDL code is implemented on the Altera Cyclone IV GX EP4CGX150DF31C7 FPGA. A fringe pattern image with a size of 512×512 pixels is presented to the FPGA, which processes the image using the 1D-CWT algorithm. The FPGA requires approximately 100 ms to process the image and produce a wrapped phase map. For performance comparison purposes, the 1D-CWT algorithm is programmed using the C language. The C code is then compiled using the Intel compiler version 13.0. The compiled code is run on a Dell Precision state-of-the-art workstation. The time required to process the fringe pattern image is approximately 1 s. In order to further reduce the execution time, the 1D-CWT is reprogramed using Intel Integrated Primitive Performance (IPP) Library Version 7.1. The execution time was reduced to approximately 650 ms. This confirms that at least sixfold speedup was gained using FPGA implementation over a state-of-the-art workstation that executes heavily optimized implementation of the 1D-CWT algorithm.

  14. Digital Modulation Identification Model Using Wavelet Transform and Statistical Parameters

    Directory of Open Access Journals (Sweden)

    P. Prakasam

    2008-01-01

    Full Text Available A generalized modulation identification scheme is developed and presented. With the help of this scheme, the automatic modulation classification and recognition of wireless communication signals with a priori unknown parameters are possible effectively. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to identify. The developed scheme based on wavelet transform and statistical parameters has been used to identify M-ary PSK, M-ary QAM, GMSK, and M-ary FSK modulations. The simulated results show that the correct modulation identification is possible to a lower bound of 5 dB. The identification percentage has been analyzed based on the confusion matrix. When SNR is above 5 dB, the probability of detection of the proposed system is more than 0.968. The performance of the proposed scheme has been compared with existing methods and found it will identify all digital modulation schemes with low SNR.

  15. Application and Analysis of Wavelet Transform in Image Edge Detection

    Institute of Scientific and Technical Information of China (English)

    Jianfang gao[1

    2016-01-01

    For the image processing technology, technicians have been looking for a convenient and simple detection method for a long time, especially for the innovation research on image edge detection technology. Because there are a lot of original information at the edge during image processing, thus, we can get the real image data in terms of the data acquisition. The usage of edge is often in the case of some irregular geometric objects, and we determine the contour of the image by combining with signal transmitted data. At the present stage, there are different algorithms in image edge detection, however, different types of algorithms have divergent disadvantages so It is diffi cult to detect the image changes in a reasonable range. We try to use wavelet transformation in image edge detection, making full use of the wave with the high resolution characteristics, and combining multiple images, in order to improve the accuracy of image edge detection.

  16. A wavelet multiscale denoising algorithm for magnetic resonance (MR) images

    International Nuclear Information System (INIS)

    Yang, Xiaofeng; Fei, Baowei

    2011-01-01

    Based on the Radon transform, a wavelet multiscale denoising method is proposed for MR images. The approach explicitly accounts for the Rician nature of MR data. Based on noise statistics we apply the Radon transform to the original MR images and use the Gaussian noise model to process the MR sinogram image. A translation invariant wavelet transform is employed to decompose the MR 'sinogram' into multiscales in order to effectively denoise the images. Based on the nature of Rician noise we estimate noise variance in different scales. For the final denoised sinogram we apply the inverse Radon transform in order to reconstruct the original MR images. Phantom, simulation brain MR images, and human brain MR images were used to validate our method. The experiment results show the superiority of the proposed scheme over the traditional methods. Our method can reduce Rician noise while preserving the key image details and features. The wavelet denoising method can have wide applications in MRI as well as other imaging modalities

  17. 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.

  18. KOMPRESI CITRA MEDIS MENGGUNAKAN PACKET WAVELET TRANSFORM DAN RUN LENGTH ENCODING

    Directory of Open Access Journals (Sweden)

    I Made Ari Dwi Suta Atmaja

    2018-03-01

    Full Text Available Citra medis memegang peranan yang sangat penting dalam dunia medis saat ini. Biasanya citra medis membutuhkan penyimpanan yang cukup besar pada komputer. Penelitian ini bertujuan untuk melakukan kompresi pada citra medis menggunakan wavelet packet transform (PWT dan run length encoding (RLE. Tiga jenis codec yaitu Haar, Daubechies dan Biorthogonal digunakan dalam penelitian ini. Penelitian ini membandingkan rasio kompresi, waktu kompresi dan dekompresi untuk setiap citra. Penelitian ini menggunakan tiga nilai threshold yaitu 30, 40 dan 50. Percobaan yang dilakukan menggunakan lima citra medis yang mewakili jenis citra hasil X-ray, USG dan CT-Scan sebagai data testing. Penelitian ini menunjukkan bahwa codec Haar dan Biorthogonal memberikan hasil yang lebih baik dibandingkan codec Daubechies dalam hal kualitas citra (PSNR dan rasio. Akan tetapi untuk waktu kompresi, codec Daubechies lebih cepat meskipun tidak secara signifikan.

  19. 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 ...

  20. Wavelet analysis of epileptic spikes

    Science.gov (United States)

    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.

  1. Wavelet analysis of epileptic spikes

    CERN Document Server

    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.

  2. 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

  3. Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

    OpenAIRE

    Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

    2012-01-01

    An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with vari...

  4. Operational modal analysis and wavelet transformation for damage identification in wind turbine blades

    DEFF Research Database (Denmark)

    Ulriksen, Martin Dalgaard; Tcherniak, Dmitri; Kirkegaard, Poul Henning

    2014-01-01

    The presented study demonstrates an application of a previously proposed modal and wavelet analysis-based damage identification method to a wind turbine blade. A trailing edge debonding was introduced to a SSP 34m blade mounted on a test rig. Operational modal analysis (OMA) was conducted to obtain...... are captured in the CWT by significantly magnified transform coefficients, thus providing combined damage detection, localization, and size assessment. It was found that due to the nature of the proposed method, the value of the identification results highly depends on the number of employed measurement points....... Since only a limited number of measurement points were utilized in the experiments, valid damage identification can only be obtained when employing high-frequency modes....

  5. High-precision terahertz frequency modulated continuous wave imaging method using continuous wavelet transform

    Science.gov (United States)

    Zhou, Yu; Wang, Tianyi; Dai, Bing; Li, Wenjun; Wang, Wei; You, Chengwu; Wang, Kejia; Liu, Jinsong; Wang, Shenglie; Yang, Zhengang

    2018-02-01

    Inspired by the extensive application of terahertz (THz) imaging technologies in the field of aerospace, we exploit a THz frequency modulated continuous-wave imaging method with continuous wavelet transform (CWT) algorithm to detect a multilayer heat shield made of special materials. This method uses the frequency modulation continuous-wave system to catch the reflected THz signal and then process the image data by the CWT with different basis functions. By calculating the sizes of the defects area in the final images and then comparing the results with real samples, a practical high-precision THz imaging method is demonstrated. Our method can be an effective tool for the THz nondestructive testing of composites, drugs, and some cultural heritages.

  6. Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

    Science.gov (United States)

    Li, Xianye; Meng, Xiangfeng; Yang, Xiulun; Wang, Yurong; Yin, Yongkai; Peng, Xiang; He, Wenqi; Dong, Guoyan; Chen, Hongyi

    2018-03-01

    A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.

  7. Analysis of the tennis racket vibrations during forehand drives: Selection of the mother wavelet.

    Science.gov (United States)

    Blache, Y; Hautier, C; Lefebvre, F; Djordjevic, A; Creveaux, T; Rogowski, I

    2017-08-16

    The time-frequency analysis of the tennis racket and hand vibrations is of great interest for discomfort and pathology prevention. This study aimed to (i) to assess the stationarity of the vibratory signal of the racket and hand and (ii) to identify the best mother wavelet to perform future time-frequency analysis, (iii) to determine if the stroke spin, racket characteristics and impact zone can influence the selection of the best mother wavelet. A total of 2364 topspin and flat forehand drives were performed by fourteen male competitive tennis players with six different rackets. One tri-axial and one mono-axial accelerometer were taped on the racket throat and dominant hand respectively. The signal stationarity was tested through the wavelet spectrum test. Eighty-nine mother wavelet were tested to select the best mother wavelet based on continuous and discrete transforms. On average only 25±17%, 2±5%, 5±7% and 27±27% of the signal tested respected the hypothesis of stationarity for the three axes of the racket and the hand respectively. Regarding the two methods for the detection of the best mother wavelet, the Daubechy 45 wavelet presented the highest average ranking. No effect of the stroke spin, racket characteristics and impact zone was observed for the selection of the best mother wavelet. It was concluded that alternative approach to Fast Fourier Transform should be used to interpret tennis vibration signals. In the case where wavelet transform is chosen, the Daubechy 45 mother wavelet appeared to be the most suitable. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Wavelet based analysis of multi-electrode EEG-signals in epilepsy

    Science.gov (United States)

    Hein, Daniel A.; Tetzlaff, Ronald

    2005-06-01

    For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

  9. Method and system for progressive mesh storage and reconstruction using wavelet-encoded height fields

    Science.gov (United States)

    Baxes, Gregory A. (Inventor); Linger, Timothy C. (Inventor)

    2011-01-01

    Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.

  10. Alleviating Border Effects in Wavelet Transforms for Nonlinear Time-varying Signal Analysis

    Directory of Open Access Journals (Sweden)

    SU, H.

    2011-08-01

    Full Text Available Border effects are very common in many finite signals analysis and processing approaches using convolution operation. Alleviating the border effects that can occur in the processing of finite-length signals using wavelet transform is considered in this paper. Traditional methods for alleviating the border effects are suitable to compression or coding applications. We propose an algorithm based on Fourier series which is proved to be appropriate to the application of time-frequency analysis of nonlinear signals. Fourier series extension method preserves the time-varying characteristics of the signals. A modified signal duration expression for measuring the extent of border effects region is presented. The proposed algorithm is confirmed to be efficient to alleviate the border effects in comparison to the current methods through the numerical examples.

  11. Harmonic analysis of electric locomotive and traction power system based on wavelet singular entropy

    Science.gov (United States)

    Dun, Xiaohong

    2018-05-01

    With the rapid development of high-speed railway and heavy-haul transport, the locomotive and traction power system has become the main harmonic source of China's power grid. In response to this phenomenon, the system's power quality issues need timely monitoring, assessment and governance. Wavelet singular entropy is an organic combination of wavelet transform, singular value decomposition and information entropy theory, which combines the unique advantages of the three in signal processing: the time-frequency local characteristics of wavelet transform, singular value decomposition explores the basic modal characteristics of data, and information entropy quantifies the feature data. Based on the theory of singular value decomposition, the wavelet coefficient matrix after wavelet transform is decomposed into a series of singular values that can reflect the basic characteristics of the original coefficient matrix. Then the statistical properties of information entropy are used to analyze the uncertainty of the singular value set, so as to give a definite measurement of the complexity of the original signal. It can be said that wavelet entropy has a good application prospect in fault detection, classification and protection. The mat lab simulation shows that the use of wavelet singular entropy on the locomotive and traction power system harmonic analysis is effective.

  12. 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....

  13. Detection algorithm for glass bottle mouth defect by continuous wavelet transform based on machine vision

    Science.gov (United States)

    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.

  14. Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

    Science.gov (United States)

    Hussain, M. S.; Mamun, Md.

    2012-01-01

    Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.

  15. Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

    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.

  16. Wavelet-based multiscale window transform and energy and vorticity analysis

    Science.gov (United States)

    Liang, Xiang San

    A new methodology, Multiscale Energy and Vorticity Analysis (MS-EVA), is developed to investigate sub-mesoscale, meso-scale, and large-scale dynamical interactions in geophysical fluid flows which are intermittent in space and time. The development begins with the construction of a wavelet-based functional analysis tool, the multiscale window transform (MWT), which is local, orthonormal, self-similar, and windowed on scale. The MWT is first built over the real line then modified onto a finite domain. Properties are explored, the most important one being the property of marginalization which brings together a quadratic quantity in physical space with its phase space representation. Based on MWT the MS-EVA is developed. Energy and enstrophy equations for the large-, meso-, and sub-meso-scale windows are derived and their terms interpreted. The processes thus represented are classified into four categories: transport; transfer, conversion, and dissipation/diffusion. The separation of transport from transfer is made possible with the introduction of the concept of perfect transfer. By the property of marginalization, the classical energetic analysis proves to be a particular case of the MS-EVA. The MS-EVA developed is validated with classical instability problems. The validation is carried out through two steps. First, it is established that the barotropic and baroclinic instabilities are indicated by the spatial averages of certain transfer term interaction analyses. Then calculations of these indicators are made with an Eady model and a Kuo model. The results agree precisely with what is expected from their analytical solutions, and the energetics reproduced reveal a consistent and important aspect of the unknown dynamic structures of instability processes. As an application, the MS-EVA is used to investigate the Iceland-Faeroe frontal (IFF) variability. A MS-EVA-ready dataset is first generated, through a forecasting study with the Harvard Ocean Prediction System

  17. 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.

  18. 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

  19. Implementation of Wavelet-Based Robust Differential Control for Electric Vehicle Application

    DEFF Research Database (Denmark)

    Daya, Febin; Padmanaban, Sanjeevikumar; Blaabjerg, Frede

    2015-01-01

    This research letter presents the modeling and simulation of electronic differential, employing a novel wavelet controller for two brushless dc motors. The proposed controller uses discrete wavelet transform to decompose the error between actual and reference speed. Error signal that is actually...

  20. EIT Imaging Regularization Based on Spectral Graph Wavelets.

    Science.gov (United States)

    Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

    2017-09-01

    The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.

  1. Characterizations of p-Wavelets on Positive Half Line Using the Walsh-Fourier Transform

    Directory of Open Access Journals (Sweden)

    Abdullah Abdullah

    2016-03-01

    Full Text Available In this paper, we study the characterization of wavelets on positive half line by means of two basic equations in the Fourier domain. We also give another characterization of wavelets.

  2. WASP (Wavelet Analysis of Secondary Particles Angular Distributions) package. Version 1.0. User's guide

    International Nuclear Information System (INIS)

    Solov'ev, A.G.

    2001-01-01

    WASP package is a C++ program aimed to analyze angular distributions of secondary particles generated in nuclear interactions. (WASP is designed for data analysis of the STAR and ALICE experiments). It uses a wavelet analysis for this purpose and the vanishing momentum or gaussian wavelets are chosen for transformations. WASP provides an user-friendly Graphical User Interface (GUI) which makes it quite simple to use. WASP design, a brief description of the used wavelet transformation algorithm and GUI are presented in this user's guide

  3. 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)

  4. Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain

    Science.gov (United States)

    Jin, Xin; Jiang, Qian; Yao, Shaowen; Zhou, Dongming; Nie, Rencan; Lee, Shin-Jye; He, Kangjian

    2018-01-01

    In order to promote the performance of infrared and visual image fusion and provide better visual effects, this paper proposes a hybrid fusion method for infrared and visual image by the combination of discrete stationary wavelet transform (DSWT), discrete cosine transform (DCT) and local spatial frequency (LSF). The proposed method has three key processing steps. Firstly, DSWT is employed to decompose the important features of the source image into a series of sub-images with different levels and spatial frequencies. Secondly, DCT is used to separate the significant details of the sub-images according to the energy of different frequencies. Thirdly, LSF is applied to enhance the regional features of DCT coefficients, and it can be helpful and useful for image feature extraction. Some frequently-used image fusion methods and evaluation metrics are employed to evaluate the validity of the proposed method. The experiments indicate that the proposed method can achieve good fusion effect, and it is more efficient than other conventional image fusion methods.

  5. Wavelet Spectral Finite Elements for Wave Propagation in Composite Plates with Damages - Years 3-4

    Science.gov (United States)

    2014-05-23

    governing differential equation, transforming to frequency domain using wavelet transform, performing uncoupling of wavelet coefficients using...transformed to the frequency domain where 1024 sampling points are used. For spatial variation, 30 Fourier series coefficients are considered. In FE...using Lead Zirconate Titanate ( PZT ) wafer active patch (15 mm diameter and 2 mm thickness) which was bonded to the structure. A 3D Laser Doppler

  6. Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations

    Science.gov (United States)

    García Plaza, E.; Núñez López, P. J.

    2018-01-01

    The wavelet packet transform method decomposes a time signal into several independent time-frequency signals called packets. This enables the temporary location of transient events occurring during the monitoring of the cutting processes, which is advantageous in monitoring condition and fault diagnosis. This paper proposes the monitoring of surface roughness using a single low cost sensor that is easily implemented in numerical control machine tools in order to make on-line decisions on workpiece surface finish quality. Packet feature extraction in vibration signals was applied to correlate the sensor signals to measured surface roughness. For the successful application of the WPT method, mother wavelets, packet decomposition level, and appropriate packet selection methods should be considered, but are poorly understood aspects in the literature. In this novel contribution, forty mother wavelets, optimal decomposition level, and packet reduction methods were analysed, as well as identifying the effective frequency range providing the best packet feature extraction for monitoring surface finish. The results show that mother wavelet biorthogonal 4.4 in decomposition level L3 with the fusion of the orthogonal vibration components (ax + ay + az) were the best option in the vibration signal and surface roughness correlation. The best packets were found in the medium-high frequency DDA (6250-9375 Hz) and high frequency ADA (9375-12500 Hz) ranges, and the feed acceleration component ay was the primary source of information. The packet reduction methods forfeited packets with relevant features to the signal, leading to poor results for the prediction of surface roughness. WPT is a robust vibration signal processing method for the monitoring of surface roughness using a single sensor without other information sources, satisfactory results were obtained in comparison to other processing methods with a low computational cost.

  7. Evolutive Optimization of Wavelets and Shapelets for Bioelectrical Signal Analysis

    OpenAIRE

    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...

  8. Framelets and wavelets algorithms, analysis, and applications

    CERN Document Server

    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...

  9. Scalets, wavelets and (complex) turning point quantization

    Science.gov (United States)

    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.

  10. Using Wavelets in Economics. An Application on the Analysis of Wage-Price Relation

    Directory of Open Access Journals (Sweden)

    Vasile-Aurel Caus

    2017-03-01

    Full Text Available In the last decades, more and more approaches of economic issues have used mathematical tools, and among the most recent ones, spectral and wavelet methods are to be distinguished. If in the case of spectral analysis the approaches and results are sufficiently clear, while the use of wavelet decomposition, especially in the analysis of time series, is not fully valorized. The purpose of this paper is to emphasize how these methods are useful for time series analysis. After theoretical considerations on Fourier transforms versus wavelet transforms, we have presented the methodology we have used and the results obtained by using wavelets in the analysis of wage-price relation, based on some empirical data. The data we have used is concerning the Romanian economy - the inflation and the average nominal wage denominated in US dollars, between January 1991 and May 2016, highlighting that the relation between nominal salary and prices can be revealed more accurately by use of wavelets.

  11. Combining a wavelet transform with a channelized Hotelling observer for tumor detection in 3D PET oncology imaging

    Science.gov (United States)

    Lartizien, Carole; Tomei, Sandrine; Maxim, Voichita; Odet, Christophe

    2007-03-01

    This study evaluates new observer models for 3D whole-body Positron Emission Tomography (PET) imaging based on a wavelet sub-band decomposition and compares them with the classical constant-Q CHO model. Our final goal is to develop an original method that performs guided detection of abnormal activity foci in PET oncology imaging based on these new observer models. This computer-aided diagnostic method would highly benefit to clinicians for diagnostic purpose and to biologists for massive screening of rodents populations in molecular imaging. Method: We have previously shown good correlation of the channelized Hotelling observer (CHO) using a constant-Q model with human observer performance for 3D PET oncology imaging. We propose an alternate method based on combining a CHO observer with a wavelet sub-band decomposition of the image and we compare it to the standard CHO implementation. This method performs an undecimated transform using a biorthogonal B-spline 4/4 wavelet basis to extract the features set for input to the Hotelling observer. This work is based on simulated 3D PET images of an extended MCAT phantom with randomly located lesions. We compare three evaluation criteria: classification performance using the signal-to-noise ratio (SNR), computation efficiency and visual quality of the derived 3D maps of the decision variable λ. The SNR is estimated on a series of test images for a variable number of training images for both observers. Results: Results show that the maximum SNR is higher with the constant-Q CHO observer, especially for targets located in the liver, and that it is reached with a smaller number of training images. However, preliminary analysis indicates that the visual quality of the 3D maps of the decision variable λ is higher with the wavelet-based CHO and the computation time to derive a 3D λ-map is about 350 times shorter than for the standard CHO. This suggests that the wavelet-CHO observer is a good candidate for use in our guided

  12. Simultaneous spectrophotometric determination of overlapping spectra of paracetamol and caffeine in laboratory prepared mixtures and pharmaceutical preparations using continuous wavelet and derivative transform

    Directory of Open Access Journals (Sweden)

    Ahmed Ashour

    2015-03-01

    Full Text Available In the present paper, two spectrophotometric methods were used for the simultaneous analysis of paracetamol (PCT and caffeine (CAF in their laboratory prepared mixtures and pharmaceutical preparations. Simple spectrophotometric analysis of PCT and CAF is not possible due to their complete spectral overlap. The proposed methods are based on the application of continuous wavelet transform (CWT and derivative transform (using Savitsky–Golay filters on the ratio spectra to predict each of CAF and PCT. Several wavelet families were tested. Coif1 and Sym2 were found to give best results under optimum conditions. The transformed signals of ratio spectra were used to plot the calibration curves for both components. The predictability of the built calibrations was validated through their application on several synthetic mixtures of both drugs. The proposed methods were used for the prediction of CAF and PCT in pharmaceutical preparation. The obtained results were statistically compared to a reference HPLC method. No significant differences were found between the obtained results and those from the reference method. Being simple, rapid, cheap and sensitive, the proposed methods are recommended for the routine daily analysis of these two drugs in their mixtures in quality control laboratories.

  13. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Directory of Open Access Journals (Sweden)

    Wonhee Lee

    2014-02-01

    Full Text Available A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT. Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU.

  14. Wavelet Transform Based Higher Order Statistical Analysis of Wind and Wave Time Histories

    Science.gov (United States)

    Habib Huseni, Gulamhusenwala; Balaji, Ramakrishnan

    2017-10-01

    Wind, blowing on the surface of the ocean, imparts the energy to generate the waves. Understanding the wind-wave interactions is essential for an oceanographer. This study involves higher order spectral analyses of wind speeds and significant wave height time histories, extracted from European Centre for Medium-Range Weather Forecast database at an offshore location off Mumbai coast, through continuous wavelet transform. The time histories were divided by the seasons; pre-monsoon, monsoon, post-monsoon and winter and the analysis were carried out to the individual data sets, to assess the effect of various seasons on the wind-wave interactions. The analysis revealed that the frequency coupling of wind speeds and wave heights of various seasons. The details of data, analysing technique and results are presented in this paper.

  15. Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

    Science.gov (United States)

    Lee, Wonhee; Park, Chan Gook

    2014-01-01

    A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU). PMID:24556675

  16. WAVELET TRANSFORM ANALYSIS OF ELECTROMYOGRAPHY KUNG FU STRIKES DATA

    Directory of Open Access Journals (Sweden)

    Ana Carolina de Miranda Marzullo

    2009-11-01

    Full Text Available In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP values instead of root mean square (rms values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF. EMG data of the deltoid anterior (DA, triceps brachii (TB and brachioradialis (BR muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023 for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007 for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners

  17. Visibility of wavelet quantization noise

    Science.gov (United States)

    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.

  18. A data-driven wavelet-based approach for generating jumping loads

    Science.gov (United States)

    Chen, Jun; Li, Guo; Racic, Vitomir

    2018-06-01

    This paper suggests an approach to generate human jumping loads using wavelet transform and a database of individual jumping force records. A total of 970 individual jumping force records of various frequencies were first collected by three experiments from 147 test subjects. For each record, every jumping pulse was extracted and decomposed into seven levels by wavelet transform. All the decomposition coefficients were stored in an information database. Probability distributions of jumping cycle period, contact ratio and energy of the jumping pulse were statistically analyzed. Inspired by the theory of DNA recombination, an approach was developed by interchanging the wavelet coefficients between different jumping pulses. To generate a jumping force time history with N pulses, wavelet coefficients were first selected randomly from the database at each level. They were then used to reconstruct N pulses by the inverse wavelet transform. Jumping cycle periods and contract ratios were then generated randomly based on their probabilistic functions. These parameters were assigned to each of the N pulses which were in turn scaled by the amplitude factors βi to account for energy relationship between successive pulses. The final jumping force time history was obtained by linking all the N cycles end to end. This simulation approach can preserve the non-stationary features of the jumping load force in time-frequency domain. Application indicates that this approach can be used to generate jumping force time history due to single people jumping and also can be extended further to stochastic jumping loads due to groups and crowds.

  19. Wavelet applications for modeling in the atmospheric sciences: Current status and potential extensions. Final report

    International Nuclear Information System (INIS)

    Envair, J.H.; Ekstrom, P.

    1995-11-01

    Wavelets are elementary mathematical functions used to construct, transform, and analyze higher functions and observational data. This report describes the results of an exploratory research effort to evaluate wavelet applications for numerically integrating differential equations associated with air pollution transport and conversion models. It is intended to provide a primer on wavelets, and specifically outlines the use of wavelets in a model that addresses derivative-evaluation and boundary-condition problems. Several factors complicate the use of wavelets for integrating differential equations. First, an enormous range of different wavelet types exists, making the choice of wavelet family for a given application challenging. Moreover, in contrast to the Fourier series, the functional derivatives necessary for numerical approximation are difficult to evaluate and consolidate in terms of wavelet expansions, introducing appreciable complexity into any attempt at wavelet-based integration. On the positive side, wavelet techniques do hold promise for effectively interfacing plume and other subgrid-scale phenomena in grid models. Moreover, workable techniques for derivative evaluation and simulation of boundary features appear feasible. Wavelet use may provide a viable, advantageous option for numerically integrating model equations describing fields on all scales of time and distance, especially where inhomogeneous fields exist, and provide a computationally efficient method of focusing on high-variability regions. The potential for wavelets to conduct integrations totally in transform space contrasts with Fourier-based approaches, which essentially preclude such treatments whenever nonlinear chemical processes occur in the modeled system

  20. Processing of pulse oximeter data using discrete wavelet analysis.

    Science.gov (United States)

    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.

  1. Accelerating wavelet lifting on graphics hardware using CUDA

    NARCIS (Netherlands)

    Laan, van der W.J.; Roerdink, J.B.T.M.; Jalba, A.C.

    2011-01-01

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as

  2. Accelerating Wavelet Lifting on Graphics Hardware Using CUDA

    NARCIS (Netherlands)

    Laan, Wladimir J. van der; Jalba, Andrei C.; Roerdink, Jos B.T.M.

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. We show that this transform, by means of the lifting scheme, can be performed in a memory and computation-efficient way on modern, programmable GPUs, which can be regarded as

  3. Fast, large-scale hologram calculation in wavelet domain

    Science.gov (United States)

    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.

  4. Texture Analysis of Recurrence Plots Based on Wavelets and PSO for Laryngeal Pathologies Detection.

    Science.gov (United States)

    Souza, Taciana A; Vieira, Vinícius J D; Correia, Suzete E N; Costa, Silvana L N C; de A Costa, Washington C; Souza, Micael A

    2015-01-01

    This paper deals with the discrimination between healthy and pathological speech signals using recurrence plots and wavelet transform with texture features. Approximation and detail coefficients are obtained from the recurrence plots using Haar wavelet transform, considering one decomposition level. The considered laryngeal pathologies are: paralysis, Reinke's edema and nodules. Accuracy rates above 86% were obtained by means of the employed method.

  5. Spline and spline wavelet methods with applications to signal and image processing

    CERN Document Server

    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...

  6. Proposed Suitable Methods to Detect Transient Regime Switching to Improve Power Quality with Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Javad Safaee Kuchaksaraee

    2016-10-01

    Full Text Available The increasing consumption of electrical energy and the use of non-linear loads that create transient regime states in distribution networks is increasing day by day. This is the only reason due to which the analysis of power quality for energy sustainability in power networks has become more important. Transients are often created by energy injection through switching or lightning and make changes in voltage and nominal current. Sudden increase or decrease in voltage or current makes characteristics of the transient regime. This paper shed some lights on the capacitor bank switching, which is one of the main causes for oscillatory transient regime states in the distribution network, using wavelet transform. The identification of the switching current of capacitor bank and the internal fault current of the transformer to prevent the unnecessary outage of the differential relay, it propose a new smart method. The accurate performance of this method is shown by simulation in EMTP and MATLAB (matrix laboratory software.

  7. A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

    Directory of Open Access Journals (Sweden)

    Fanping Zhang

    2014-01-01

    Full Text Available Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds and an approximation (A3 at three resolution levels (21-22-23 using Daubechies (db3 discrete wavelet. Correlation coefficients between each D subtime series and original monthly streamflow time series are calculated. Ds components with high correlation coefficients (D3 are added to the approximation (A3 as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters, C, ε, and σ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

  8. Multiscale wavelet representations for mammographic feature analysis

    Science.gov (United States)

    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).

  9. Volatility forecasting with the wavelet transformation algorithm GARCH model: Evidence from African stock markets

    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.

  10. Texture Segmentation Based on Wavelet and Kohonen Network for Remotely Sensed Images

    NARCIS (Netherlands)

    Chen, Z.; Feng, T.J.; Feng, T.J.; Houkes, Z.

    1999-01-01

    In this paper, an approach based on wavelet decomposition and Kohonen's self-organizing map is developed for image segmentation. After performing the 2D wavelet transform of image, some features are extracted for texture segmentation, and the Kohonen neural network is used to accomplish feature

  11. Wavelet-like bases for thin-wire integral equations in electromagnetics

    Science.gov (United States)

    Francomano, E.; Tortorici, A.; Toscano, E.; Ala, G.; Viola, F.

    2005-03-01

    In this paper, wavelets are used in solving, by the method of moments, a modified version of the thin-wire electric field integral equation, in frequency domain. The time domain electromagnetic quantities, are obtained by using the inverse discrete fast Fourier transform. The retarded scalar electric and vector magnetic potentials are employed in order to obtain the integral formulation. The discretized model generated by applying the direct method of moments via point-matching procedure, results in a linear system with a dense matrix which have to be solved for each frequency of the Fourier spectrum of the time domain impressed source. Therefore, orthogonal wavelet-like basis transform is used to sparsify the moment matrix. In particular, dyadic and M-band wavelet transforms have been adopted, so generating different sparse matrix structures. This leads to an efficient solution in solving the resulting sparse matrix equation. Moreover, a wavelet preconditioner is used to accelerate the convergence rate of the iterative solver employed. These numerical features are used in analyzing the transient behavior of a lightning protection system. In particular, the transient performance of the earth termination system of a lightning protection system or of the earth electrode of an electric power substation, during its operation is focused. The numerical results, obtained by running a complex structure, are discussed and the features of the used method are underlined.

  12. Wavelets in self-consistent electronic structure calculations

    International Nuclear Information System (INIS)

    Wei, S.; Chou, M.Y.

    1996-01-01

    We report the first implementation of orthonormal wavelet bases in self-consistent electronic structure calculations within the local-density approximation. These local bases of different scales efficiently describe localized orbitals of interest. As an example, we studied two molecules, H 2 and O 2 , using pseudopotentials and supercells. Considerably fewer bases are needed compared with conventional plane-wave approaches, yet calculated binding properties are similar. Our implementation employs fast wavelet and Fourier transforms, avoiding evaluating any three-dimensional integral numerically. copyright 1996 The American Physical Society

  13. Selection of mother wavelets for the detection of the oscillation frequencies in power signals of nuclear reactors

    International Nuclear Information System (INIS)

    Amador G, R.; Castillo D, R.; Ortiz V, J.

    2007-01-01

    Diverse types of transitory events can lead to oscillations of power in nuclear reactors. In such events, the power monitors provide a signal that contains important characteristics of the transitory one, as the oscillation frequency, tendencies, changes and the instants or periods in those that important events are presented. This characteristics are detected by means of diverse analysis techniques, as Autoregressive methods, Fourier Transform, Fourier Transform in Short Time, Wavelets Transform, among others. Presently work is used the one Wavelets Continuous Transform because it allows to carry out studies of the stationary, quasi-stationary and transitory signals in the Time-scale and Time-scale-spectrum planes. Contrary to other similar works, this work describes a methodology for the selection of the scales and the Wavelet mother to be applied the one Wavelets Continuous Transform, with the objective of detecting to the dominant frequencies of the system. To prove the proposal a broadly well-known real signal of an event of power oscillations it has been used. The obtained results correspond to three families of Wavelets mothers that fulfilled the conditions of scales and central frequency of the proposal. The results show that the value of the certain frequency oscillation in this work is practically the same one reported in other studies with other techniques. (Author)

  14. 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

  15. 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.

  16. Non-stationary dynamics in the bouncing ball: A wavelet perspective

    Energy Technology Data Exchange (ETDEWEB)

    Behera, Abhinna K., E-mail: abhinna@iiserkol.ac.in; Panigrahi, Prasanta K., E-mail: pprasanta@iiserkol.ac.in [Department of Physical Sciences, Indian Institute of Science Education and Research (IISER) Kolkata, Mohanpur 741246 (India); Sekar Iyengar, A. N., E-mail: ansekar.iyengar@saha.ac.in [Plasma Physics Division, Saha Institute of Nuclear Physics (SINP), Sector 1, Block-AF, Bidhannagar, Kolkata 700064 (India)

    2014-12-01

    The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.

  17. 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

  18. A Study of Coherent Structures using Wavelet Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Kaspersen, J H

    1996-05-01

    Turbulence is important in many fields of engineering, for example in estimating drag or minimizing drag on surfaces. It is known that turbulent flows contain coherent structures, which implies that a turbulent shear flow can be decomposed into coherent structures and random motion. It is generally accepted that coherent structures are responsible for significant transport of mass, heat and momentum. This doctoral thesis presents and discusses a new algorithm to detect coherent structures based on Wavelet transformations, a transform similar to the Fourier transform but providing information on both frequency and scale. The new detection scheme does not require any predefined threshold or integration time, and its general performance is found to be very good. Wind tunnel experiments were performed to obtain data for analysis. Scalograms resulting from the Wavelet transform show clearly that coherent structures exist in turbulent flows. These structures are shown to contribute considerably to the shear stresses. The contribution from the organized motion to the normal stresses close to the wall appears to be considerably smaller. Direct Navier Stokes (DNS) channel flow seems to be more organized than Zero Pressure Gradient (ZPG) flows. The topology of ZPG flows was studied using a multiple hot wire arrangement and conditionally averaged streamlines based on detections from the Wavelet method are presented. It is shown that the coherent structures produce large amounts of both vorticity and strain at the detection point. 56 refs., 92 figs., 3 tabs.

  19. 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.

  20. 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...

  1. 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...

  2. Discovering Wavelets

    CERN Document Server

    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

  3. EFFECTIVE MULTI-RESOLUTION TRANSFORM IDENTIFICATION FOR CHARACTERIZATION AND CLASSIFICATION OF TEXTURE GROUPS

    Directory of Open Access Journals (Sweden)

    S. Arivazhagan

    2011-11-01

    Full Text Available Texture classification is important in applications of computer image analysis for characterization or classification of images based on local spatial variations of intensity or color. Texture can be defined as consisting of mutually related elements. This paper proposes an experimental approach for identification of suitable multi-resolution transform for characterization and classification of different texture groups based on statistical and co-occurrence features derived from multi-resolution transformed sub bands. The statistical and co-occurrence feature sets are extracted for various multi-resolution transforms such as Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT, Double Density Wavelet Transform (DDWT and Dual Tree Complex Wavelet Transform (DTCWT and then, the transform that maximizes the texture classification performance for the particular texture group is identified.

  4. Multiresolution forecasting for futures trading using wavelet decompositions.

    Science.gov (United States)

    Zhang, B L; Coggins, R; Jabri, M A; Dersch, D; Flower, B

    2001-01-01

    We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades.

  5. Adaptive wavelet tight frame construction for accelerating MRI reconstruction

    Directory of Open Access Journals (Sweden)

    Genjiao Zhou

    2017-09-01

    Full Text Available The sparsity regularization approach, which assumes that the image of interest is likely to have sparse representation in some transform domain, has been an active research area in image processing and medical image reconstruction. Although various sparsifying transforms have been used in medical image reconstruction such as wavelet, contourlet, and total variation (TV etc., the efficiency of these transforms typically rely on the special structure of the underlying image. A better way to address this issue is to develop an overcomplete dictionary from the input data in order to get a better sparsifying transform for the underlying image. However, the general overcomplete dictionaries do not satisfy the so-called perfect reconstruction property which ensures that the given signal can be perfectly represented by its canonical coefficients in a manner similar to orthonormal bases, resulting in time consuming in the iterative image reconstruction. This work is to develop an adaptive wavelet tight frame method for magnetic resonance image reconstruction. The proposed scheme incorporates the adaptive wavelet tight frame approach into the magnetic resonance image reconstruction by solving a l0-regularized minimization problem. Numerical results show that the proposed approach provides significant time savings as compared to the over-complete dictionary based methods with comparable performance in terms of both peak signal-to-noise ratio and subjective visual quality.

  6. A Novel Image Encryption Scheme Based on Self-Synchronous Chaotic Stream Cipher and Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Chunlei Fan

    2018-06-01

    Full Text Available In this paper, a novel image encryption scheme is proposed for the secure transmission of image data. A self-synchronous chaotic stream cipher is designed with the purpose of resisting active attack and ensures the limited error propagation of image data. Two-dimensional discrete wavelet transform and Arnold mapping are used to scramble the pixel value of the original image. A four-dimensional hyperchaotic system with four positive Lyapunov exponents serve as the chaotic sequence generator of the self-synchronous stream cipher in order to enhance the security and complexity of the image encryption system. Finally, the simulation experiment results show that this image encryption scheme is both reliable and secure.

  7. Fault location in underground cables using ANFIS nets and discrete wavelet transform

    Directory of Open Access Journals (Sweden)

    Shimaa Barakat

    2014-12-01

    Full Text Available This paper presents an accurate algorithm for locating faults in a medium voltage underground power cable using a combination of Adaptive Network-Based Fuzzy Inference System (ANFIS and discrete wavelet transform (DWT. The proposed method uses five ANFIS networks and consists of 2 stages, including fault type classification and exact fault location. In the first part, an ANFIS is used to determine the fault type, applying four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents. Other four ANFIS networks are utilized to pinpoint the faults (one for each fault type. Four inputs, i.e., the maximum detailed energy of three phase and zero sequence currents, are used to train the neuro-fuzzy inference systems in order to accurately locate the faults on the cable. The proposed method is evaluated under different fault conditions such as different fault locations, different fault inception angles and different fault resistances.

  8. Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform

    International Nuclear Information System (INIS)

    Tascikaraoglu, Akin; Sanandaji, Borhan M.; Poolla, Kameshwar; Varaiya, Pravin

    2016-01-01

    Highlights: • We propose a spatio-temporal approach for wind speed forecasting. • The method is based on a combination of Wavelet decomposition and structured-sparse recovery. • Our analyses confirm that low-dimensional structures govern the interactions between stations. • Our method particularly shows improvements for profiles with high ramps. • We examine our approach on real data and illustrate its superiority over a set of benchmark models. - Abstract: Integration of renewable energy resources into the power grid is essential in achieving the envisioned sustainable energy future. Stochasticity and intermittency characteristics of renewable energies, however, present challenges for integrating these resources into the existing grid in a large scale. Reliable renewable energy integration is facilitated by accurate wind forecasts. In this paper, we propose a novel wind speed forecasting method which first utilizes Wavelet Transform (WT) for decomposition of the wind speed data into more stationary components and then uses a spatio-temporal model on each sub-series for incorporating both temporal and spatial information. The proposed spatio-temporal forecasting approach on each sub-series is based on the assumption that there usually exists an intrinsic low-dimensional structure between time series data in a collection of meteorological stations. Our approach is inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms. Based on detailed case studies, we show that the proposed approach based on exploiting the sparsity of correlations between a large set of meteorological stations and decomposing time series for higher-accuracy forecasts considerably improve the short-term forecasts compared to the temporal and spatio-temporal benchmark methods.

  9. Embedded wavelet-based face recognition under variable position

    Science.gov (United States)

    Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

    2015-02-01

    For several years, face recognition has been a hot topic in the image processing field: this technique is applied in several domains such as CCTV, electronic devices delocking and so on. In this context, this work studies the efficiency of a wavelet-based face recognition method in terms of subject position robustness and performance on various systems. The use of wavelet transform has a limited impact on the position robustness of PCA-based face recognition. This work shows, for a well-known database (Yale face database B*), that subject position in a 3D space can vary up to 10% of the original ROI size without decreasing recognition rates. Face recognition is performed on approximation coefficients of the image wavelet transform: results are still satisfying after 3 levels of decomposition. Furthermore, face database size can be divided by a factor 64 (22K with K = 3). In the context of ultra-embedded vision systems, memory footprint is one of the key points to be addressed; that is the reason why compression techniques such as wavelet transform are interesting. Furthermore, it leads to a low-complexity face detection stage compliant with limited computation resources available on such systems. The approach described in this work is tested on three platforms from a standard x86-based computer towards nanocomputers such as RaspberryPi and SECO boards. For K = 3 and a database with 40 faces, the execution mean time for one frame is 0.64 ms on a x86-based computer, 9 ms on a SECO board and 26 ms on a RaspberryPi (B model).

  10. Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain

    Science.gov (United States)

    Nougarou, François; Massicotte, Daniel; Descarreaux, Martin

    2012-12-01

    The flexion relaxation phenomenon (FRP) can be defined as a reduction or silence of myoelectric activity of the lumbar erector spinae muscle during full trunk flexion. It is typically absent in patients with chronic low back pain (LBP). Before any broad clinical utilization of this neuromuscular response can be made, effective, standardized, and accurate methods of identifying FRP limits are needed. However, this phenomenon is clearly more difficult to detect for LBP patients than for healthy patients. The main goal of this study is to develop an automated method based on wavelet transformation that would improve time point limits detection of surface electromyography signals of the FRP in case of LBP patients. Conventional visual identification and proposed automated methods of time point limits detection of relaxation phase were compared on experimental data using criteria of accuracy and repeatability based on physiological properties. The evaluation demonstrates that the use of wavelet transform (WT) yields better results than methods without wavelet decomposition. Furthermore, methods based on wavelet per packet transform are more effective than algorithms employing discrete WT. Compared to visual detection, in addition to demonstrating an obvious saving of time, the use of wavelet per packet transform improves the accuracy and repeatability in the detection of the FRP limits. These results clearly highlight the value of the proposed technique in identifying onset and offset of the flexion relaxation response in LBP subjects.

  11. Identification of speech transients using variable frame rate analysis and wavelet packets.

    Science.gov (United States)

    Rasetshwane, Daniel M; Boston, J Robert; Li, Ching-Chung

    2006-01-01

    Speech transients are important cues for identifying and discriminating speech sounds. Yoo et al. and Tantibundhit et al. were successful in identifying speech transients and, emphasizing them, improving the intelligibility of speech in noise. However, their methods are computationally intensive and unsuitable for real-time applications. This paper presents a method to identify and emphasize speech transients that combines subband decomposition by the wavelet packet transform with variable frame rate (VFR) analysis and unvoiced consonant detection. The VFR analysis is applied to each wavelet packet to define a transitivity function that describes the extent to which the wavelet coefficients of that packet are changing. Unvoiced consonant detection is used to identify unvoiced consonant intervals and the transitivity function is amplified during these intervals. The wavelet coefficients are multiplied by the transitivity function for that packet, amplifying the coefficients localized at times when they are changing and attenuating coefficients at times when they are steady. Inverse transform of the modified wavelet packet coefficients produces a signal corresponding to speech transients similar to the transients identified by Yoo et al. and Tantibundhit et al. A preliminary implementation of the algorithm runs more efficiently.

  12. A Cross-Wavelet Transform Aided Rule Based Approach for Early Prediction of Lean Blow-out in Swirl-Stabilized Dump Combustor

    Directory of Open Access Journals (Sweden)

    Debangshu Dey

    2015-03-01

    Full Text Available Lean or ultralean combustion is one of the popular strategies to achieve very low emission levels. However, it is extremely susceptible to lean blow-out (LBO. The present work explores a Cross-wavelet transform (XWT aided rule based scheme for early prediction of lean blowout. XWT can be considered as an advancement of wavelet analysis which gives correlation between two waveforms in time-frequency space. In the present scheme a swirl-stabilized dump combustor is used as a laboratory-scale model of a generic gas turbine combustor with LPG as fuel. Various time series data of CH chemiluminescence signal are recorded for different flame conditions by varying equivalence ratio, flow rate and level of air-fuel premixing. Some features are extracted from the cross-wavelet spectrum of the recorded waveforms and a reference wave. The extracted features are observed to classify the flame condition into three major classes: near LBO, moderate and healthy. Moreover, a Rough Set based technique is also applied on the extracted features to generate a rule base so that it can be fed to a real time controller or expert system to take necessary control action to prevent LBO. Results show that the proposed methodology performs with an acceptable degree of accuracy.

  13. SU-F-J-27: Segmentation of Prostate CBCT Images with Implanted Calypso Transponders Using Double Haar Wavelet Transform

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Y [Shandong Communication and Media College, Jinan, Shandong (China); Saleh, Z; Tang, X [Memorial Sloan Kettering Cancer Center, West Harrison, NY (United States); Song, Y; Obcemea, C [Memorial Sloan-Kettering Cancer Center, Sleepy Hollow, NY (United States); Chan, M [Memorial Sloan-Kettering Cancer Center, Basking Ridge, NJ (United States); Li, X [Memorial Sloan Kettering Cancer Center, Rockville Centre, NY (United States); Happersett, L [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Shi, C [Saint Vincent Medical Center, Bridgeport, CT (United States); Qian, X [North Shore Long Island Jewish health System, North New Hyde Park, NY (United States)

    2016-06-15

    Purpose: Segmentation of prostate CBCT images is an essential step towards real-time adaptive radiotherapy. It is challenging For Calypso patients, as more artifacts are generated by the beacon transponders. We herein propose a novel wavelet-based segmentation algorithm for rectum, bladder, and prostate of CBCT images with implanted Calypso transponders. Methods: Five hypofractionated prostate patients with daily CBCT were studied. Each patient had 3 Calypso transponder beacons implanted, and the patients were setup and treated with Calypso tracking system. Two sets of CBCT images from each patient were studied. The structures (i.e. rectum, bladder, and prostate) were contoured by a trained expert, and these served as ground truth. For a given CBCT, the moving window-based Double Haar transformation is applied first to obtain the wavelet coefficients. Based on a user defined point in the object of interest, a cluster algorithm based adaptive thresholding is applied to the low frequency components of the wavelet coefficients, and a Lee filter theory based adaptive thresholding is applied to the high frequency components. For the next step, the wavelet reconstruction is applied to the thresholded wavelet coefficients. A binary/segmented image of the object of interest is therefore obtained. DICE, sensitivity, inclusiveness and ΔV were used to evaluate the segmentation result. Results: Considering all patients, the bladder has the DICE, sensitivity, inclusiveness, and ΔV ranges of [0.81–0.95], [0.76–0.99], [0.83–0.94], [0.02–0.21]. For prostate, the ranges are [0.77–0.93], [0.84–0.97], [0.68–0.92], [0.1–0.46]. For rectum, the ranges are [0.72–0.93], [0.57–0.99], [0.73–0.98], [0.03–0.42]. Conclusion: The proposed algorithm appeared effective segmenting prostate CBCT images with the present of the Calypso artifacts. However, it is not robust in two scenarios: 1) rectum with significant amount of gas; 2) prostate with very low contrast. Model

  14. Power Spectral Analysis of Short-Term Heart Rate Variability in Healthy and Arrhythmia Subjects by the Adaptive Continuous Morlet Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Ram Sewak SINGH

    2017-12-01

    Full Text Available Power spectral analysis of short-term heart rate variability (HRV can provide instant valuable information to understand the functioning of autonomic control over the cardiovascular system. In this study, an adaptive continuous Morlet wavelet transform (ACMWT method has been used to describe the time-frequency characteristics of the HRV using band power spectra and the median value of interquartile range. Adaptation of the method was based on the measurement of maximum energy concentration. The ACMWT has been validated on synthetic signals (i.e. stationary, non-stationary as slow varying and fast changing frequency with time modeled as closest to dynamic changes in HRV signals. This method has been also tested in the presence of additive white Gaussian noise (AWGN to show its robustness towards the noise. From the results of testing on synthetic signals, the ACMWT was found to be an enhanced energy concentration estimator for assessment of power spectral of short-term HRV time series compared to adaptive Stockwell transform (AST, adaptive modified Stockwell transform (AMST, standard continuous Morlet wavelet transform (CMWT and Stockwell transform (ST estimators at statistical significance level of 5%. Further, the ACMWT was applied to real HRV data from Fantasia and MIT-BIH databases, grouped as healthy young group (HYG, healthy elderly group (HEG, arrhythmia controlled medication group (ARCMG, and supraventricular tachycardia group (SVTG subjects. The global results demonstrate that spectral indices of low frequency power (LFp and high frequency power (HFp of HRV were decreased in HEG compared to HYG subjects (p<0.0001. While LFp and HFp indices were increased in ARCMG compared to HEG (p<0.00001. The LFp and HFp components of HRV obtained from SVTG were reduced compared to other group subjects (p<0.00001.

  15. The Discrete Wavelet Transform and Its Application for Noise Removal in Localized Corrosion Measurements

    Directory of Open Access Journals (Sweden)

    Rogelio Ramos

    2017-01-01

    Full Text Available The present work discusses the problem of induced external electrical noise as well as its removal from the electrical potential obtained from Scanning Vibrating Electrode Technique (SVET in the pitting corrosion process of aluminum alloy A96061 in 3.5% NaCl. An accessible and efficient solution of this problem is presented with the use of virtual instrumentation (VI, embedded systems, and the discrete wavelet transform (DWT. The DWT is a computational algorithm for digital processing that allows obtaining electrical noise with Signal to Noise Ratio (SNR superior to those obtained with Lock-In Amplifier equipment. The results show that DWT and the threshold method are efficient and powerful alternatives to carry out electrical measurements of potential signals from localized corrosion processes measured by SVET.

  16. Wavelet and Spectral Analysis of Some Selected Problems in Reactor Diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Sunde, Carl

    2004-12-01

    Both spectral and wavelet analysis were successfully used in various diagnostic problems involving non-stationary core processes in nuclear power reactors. Three different problems were treated: two-phase flow identification, detector tube impacting and core-barrel vibrations. The first two problems are of non-stationary nature, whereas the last one is not. In the first problem, neutron radiographic and visible light images of four different vertical two-phase flow regimes, bubbly, slug, chum and annular flow, were analysed and classified with a neuro-wavelet algorithm. The algorithm consists of a wavelet part, using the 2-D discrete wavelet transform and of an artificial neural network. It classifies the different flow regimes with up to 99% efficiency. Detector tubes in a Boiling Water Reactor may execute vibrations and may also impact on nearby fuel-assemblies. Signals from in-core neutron detectors in Ringhals-1 were analysed, for detection of impacting, with both a classical spectral method and wavelet-based methods. The wavelet methods include both the discrete and the continuous 1-D wavelet transform. It was found that there is agreement between the different methods as well as with visual inspections made during the outage at the plant. However, the wavelet technique has the advantage that it does not require expert judgement for the interpretation of the analysis. In the last part two analytical calculations of the neutron noise, induced by shell-mode core-barrel vibrations, were carried out. The results are in good agreement with calculations from a numerical simulator. An out-of-phase behaviour between in-core and ex-core positions was found, which is in agreement with earlier measurements from the Pressurised Water Reactor Ringhals-3. The results from these calculations are planned to be used when diagnosing the shell-mode core-barrel vibrations in an operating plant.

  17. Wavelet and Spectral Analysis of Some Selected Problems in Reactor Diagnostics

    International Nuclear Information System (INIS)

    Sunde, Carl

    2004-12-01

    Both spectral and wavelet analysis were successfully used in various diagnostic problems involving non-stationary core processes in nuclear power reactors. Three different problems were treated: two-phase flow identification, detector tube impacting and core-barrel vibrations. The first two problems are of non-stationary nature, whereas the last one is not. In the first problem, neutron radiographic and visible light images of four different vertical two-phase flow regimes, bubbly, slug, chum and annular flow, were analysed and classified with a neuro-wavelet algorithm. The algorithm consists of a wavelet part, using the 2-D discrete wavelet transform and of an artificial neural network. It classifies the different flow regimes with up to 99% efficiency. Detector tubes in a Boiling Water Reactor may execute vibrations and may also impact on nearby fuel-assemblies. Signals from in-core neutron detectors in Ringhals-1 were analysed, for detection of impacting, with both a classical spectral method and wavelet-based methods. The wavelet methods include both the discrete and the continuous 1-D wavelet transform. It was found that there is agreement between the different methods as well as with visual inspections made during the outage at the plant. However, the wavelet technique has the advantage that it does not require expert judgement for the interpretation of the analysis. In the last part two analytical calculations of the neutron noise, induced by shell-mode core-barrel vibrations, were carried out. The results are in good agreement with calculations from a numerical simulator. An out-of-phase behaviour between in-core and ex-core positions was found, which is in agreement with earlier measurements from the Pressurised Water Reactor Ringhals-3. The results from these calculations are planned to be used when diagnosing the shell-mode core-barrel vibrations in an operating plant

  18. Procesamiento de datos mediante Wavelet para la modelación térmica de transformadores de potencia; Data processing using wavelet for power transformers thermal model

    Directory of Open Access Journals (Sweden)

    Rómulo Pérez

    2014-04-01

    Full Text Available En este trabajo las mediciones recabadas por una estación experimental instalada en un Transformador de 100 MVA de la Subestación Barquisimeto de Venezuela son procesadas para eliminar factores de ruido que introducen errores en la identificación de parámetros del modelo térmico para el cálculo de la temperatura superior del aceite. Se usa una metodología para el control de calidad y eliminación del ruido en las mediciones recabadas basada en experiencias propias y reforzadas con experiencias de reconocidos autores internacionales, la cual aplica la Transformada Discreta de Wavelet DWT para obtener datos que muestran buenos indicadores de calidad en las principales variables del modelo térmico, como lo son la corriente de carga, la temperatura ambiente y la temperatura del aceite superior. Finalmente se comparan los resultados de la modelación térmica antes y después de ser procesados los datos, donde se evidencia un notable incremento en la exactitud del modelo.  In this work measurement get of experimental station connected in a power transformer of 100 MVA in a Barquisimeto Substation in Venezuela are processing to eliminate noise that introduce mistake in the parameters identification for top oil temperature model calculation. A methodology based in your experiences with experiences of international authors for the control of quality and elimination of the noise in the successfully obtained measurements is used. It’s apply the Discreet Wavelet Transform (DWT to collect data that show good indicators of quality in the main values of the thermal model, as the load current, the ambient temperature and the top oil temperature. Finally is compared thermal model results after and beforedata processing, where at increase in the exactitude of the thermal model is demonstrated.

  19. Target Identification Using Harmonic Wavelet Based ISAR Imaging

    Science.gov (United States)

    Shreyamsha Kumar, B. K.; Prabhakar, B.; Suryanarayana, K.; Thilagavathi, V.; Rajagopal, R.

    2006-12-01

    A new approach has been proposed to reduce the computations involved in the ISAR imaging, which uses harmonic wavelet-(HW) based time-frequency representation (TFR). Since the HW-based TFR falls into a category of nonparametric time-frequency (T-F) analysis tool, it is computationally efficient compared to parametric T-F analysis tools such as adaptive joint time-frequency transform (AJTFT), adaptive wavelet transform (AWT), and evolutionary AWT (EAWT). Further, the performance of the proposed method of ISAR imaging is compared with the ISAR imaging by other nonparametric T-F analysis tools such as short-time Fourier transform (STFT) and Choi-Williams distribution (CWD). In the ISAR imaging, the use of HW-based TFR provides similar/better results with significant (92%) computational advantage compared to that obtained by CWD. The ISAR images thus obtained are identified using a neural network-based classification scheme with feature set invariant to translation, rotation, and scaling.

  20. Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

    Science.gov (United States)

    Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

    2005-12-01

    Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

  1. A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

    Science.gov (United States)

    Huang, Darong; Bai, Xing-Rong

    Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

  2. Pigmented skin lesion detection using random forest and wavelet-based texture

    Science.gov (United States)

    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.

  3. Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface.

    Science.gov (United States)

    John, Agnes Aruna; Subramanian, Aruna Priyadharshni; Jaganathan, Saravana Kumar; Sethuraman, Balasubramanian

    2015-01-01

    To process the electrocardiogram (ECG) signals using MATLAB-based graphical user interface (GUI) and to classify the signals based on heart rate. The subject condition was identified using R-peak detection based on discrete wavelet transform followed by a Bayes classifier that classifies the ECG signals. The GUI was designed to display the ECG signal plot. Obtained from MIT database 18 patients had normal heart rate and 9 patients had abnormal heart rate; 14.81% of the patients suffered from tachycardia and 18.52% of the patients have bradycardia. The proposed GUI display was found useful to analyze the digitized ECG signal by a non-technical user and may help in diagnostics. Further improvement can be done by employing field programmable gate array for the real time processing of cardiac signals. Copyright © 2015 Cardiological Society of India. Published by Elsevier B.V. All rights reserved.

  4. Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry.

    Science.gov (United States)

    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.

  5. Chaos-based partial image encryption scheme based on linear fractional and lifting wavelet transforms

    Science.gov (United States)

    Belazi, Akram; Abd El-Latif, Ahmed A.; Diaconu, Adrian-Viorel; Rhouma, Rhouma; Belghith, Safya

    2017-01-01

    In this paper, a new chaos-based partial image encryption scheme based on Substitution-boxes (S-box) constructed by chaotic system and Linear Fractional Transform (LFT) is proposed. It encrypts only the requisite parts of the sensitive information in Lifting-Wavelet Transform (LWT) frequency domain based on hybrid of chaotic maps and a new S-box. In the proposed encryption scheme, the characteristics of confusion and diffusion are accomplished in three phases: block permutation, substitution, and diffusion. Then, we used dynamic keys instead of fixed keys used in other approaches, to control the encryption process and make any attack impossible. The new S-box was constructed by mixing of chaotic map and LFT to insure the high confidentiality in the inner encryption of the proposed approach. In addition, the hybrid compound of S-box and chaotic systems strengthened the whole encryption performance and enlarged the key space required to resist the brute force attacks. Extensive experiments were conducted to evaluate the security and efficiency of the proposed approach. In comparison with previous schemes, the proposed cryptosystem scheme showed high performances and great potential for prominent prevalence in cryptographic applications.

  6. A robust image watermarking in contourlet transform domain

    Science.gov (United States)

    Sharma, Rajat; Gupta, Abhishek Kumar; Singh, Deepak; Verma, Vivek Singh; Bhardwaj, Anuj

    2017-10-01

    A lot of work has been done in the field of image watermarking to overcome the problems of rightful ownership, copyright protection etc. In order to provide a robust solution of such issues, the authors propose a hybrid approach that involves contourlet, lifting wavelet, and discrete cosine transform. The first level coefficients of the original image which are obtained using contourlet transform are further decomposed using one level lifting wavelet transform. After that, these coefficients are modified using discrete cosine transform. Whereas, second level subband of contourlet transform coefficients are used to obtain block wise modification parameter based on edge detection and entropy calculations. Watermark bits are embedded by quantizing the discrete cosine transform coefficient blocks obtained using HL sub-band of first level lifting wavelet transform coefficients. The experimental results reveal that the proposed scheme has high robustness and imperceptibility.

  7. Intrusion Detection in NEAR System by Anti-denoising Traffic Data Series using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    VANCEA, F.

    2014-11-01

    Full Text Available The paper presents two methods for detecting anomalies in data series derived from network traffic. Intrusion detection systems based on network traffic analysis are able to respond to incidents never seen before by detecting anomalies in data series extracted from the traffic. Some anomalies manifest themselves as pulses of various sizes and shapes, superimposed on series corresponding to normal traffic. In order to detect those impulses we propose two methods based on discrete wavelet transformation. Their effectiveness expressed in relative thresholds on pulse amplitude for no false negatives and no false positives is then evaluated against pulse duration and Hurst characteristic of original series. Different base functions are also evaluated for efficiency in the context of the proposed methods.

  8. Abstract harmonic analysis of continuous wavelet transforms

    CERN Document Server

    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.

  9. Pre-Processing Data Using Wavelet Transform and PCA Based on ...

    Indian Academy of Sciences (India)

    5

    wavelet theory in the last century, using this tool in the hydrology has been an ... These models are based on Statistical Learning Theory, which is ...... Hutcheson G and Nick S 1999 The multivariate social scientist: Introductory statistics using.

  10. Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

    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.

  11. Epileptic Seizure Detection based on Wavelet Transform Statistics Map and EMD Method for Hilbert-Huang Spectral Analyzing in Gamma Frequency Band of EEG Signals

    Directory of Open Access Journals (Sweden)

    Morteza Behnam

    2015-08-01

    Full Text Available Seizure detection using brain signal (EEG analysis is the important clinical methods in drug therapy and the decisions before brain surgery. In this paper, after signal conditioning using suitable filtering, the Gamma frequency band has been extracted and the other brain rhythms, ambient noises and the other bio-signal are canceled. Then, the wavelet transform of brain signal and the map of wavelet transform in multi levels are computed. By dividing the color map to different epochs, the histogram of each sub-image is obtained and the statistics of it based on statistical momentums and Negentropy values are calculated. Statistical feature vector using Principle Component Analysis (PCA is reduced to one dimension. By EMD algorithm and sifting procedure for analyzing the data by Intrinsic Mode Function (IMF and computing the residues of brain signal using spectrum of Hilbert transform and Hilbert – Huang spectrum forming, one spatial feature based on the Euclidian distance for signal classification is obtained. By K-Nearest Neighbor (KNN classifier and by considering the optimal neighbor parameter, EEG signals are classified in two classes, seizure and non-seizure signal, with the rate of accuracy 76.54% and with variance of error 0.3685 in the different tests.

  12. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation

    International Nuclear Information System (INIS)

    Mohammadi, Kasra; Shamshirband, Shahaboddin; Tong, Chong Wen; Arif, Muhammad; Petković, Dalibor; Ch, Sudheer

    2015-01-01

    Highlights: • Horizontal global solar radiation (HGSR) is predicted based on a new hybrid approach. • Support Vector Machines and Wavelet Transform algorithm (SVM–WT) are combined. • Different sets of meteorological elements are used to predict HGSR. • The precision of SVM–WT is assessed thoroughly against ANN, GP and ARMA. • SVM–WT would be an appealing approach to predict HGSR and outperforms others. - Abstract: In this paper, a new hybrid approach by combining the Support Vector Machine (SVM) with Wavelet Transform (WT) algorithm is developed to predict horizontal global solar radiation. The predictions are conducted on both daily and monthly mean scales for an Iranian coastal city. The proposed SVM–WT method is compared against other existing techniques to demonstrate its efficiency and viability. Three different sets of parameters are served as inputs to establish three models. The results indicate that the model using relative sunshine duration, difference between air temperatures, relative humidity, average temperature and extraterrestrial solar radiation as inputs shows higher performance than other models. The statistical analysis demonstrates that SVM–WT approach enjoys very good performance and outperforms other approaches. For the best SVM–WT model, the obtained statistical indicators of mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination for daily estimation are 6.9996%, 0.8405 MJ/m 2 , 1.4245 MJ/m 2 , 7.9467% and 0.9086, respectively. Also, for monthly mean estimation the values are 3.2601%, 0.5104 MJ/m 2 , 0.6618 MJ/m 2 , 3.6935% and 0.9742, respectively. Based upon relative percentage error, for the best SVM–WT model, 88.70% of daily predictions fall within the acceptable range of −10% to +10%

  13. Designing an Algorithm for Cancerous Tissue Segmentation Using Adaptive K-means Cluttering and Discrete Wavelet Transform.

    Science.gov (United States)

    Rezaee, Kh; Haddadnia, J

    2013-09-01

    Breast cancer is currently one of the leading causes of death among women worldwide. The diagnosis and separation of cancerous tumors in mammographic images require accuracy, experience and time, and it has always posed itself as a major challenge to the radiologists and physicians. This paper proposes a new algorithm which draws on discrete wavelet transform and adaptive K-means techniques to transmute the medical images implement the tumor estimation and detect breast cancer tumors in mammograms in early stages. It also allows the rapid processing of the input data. In the first step, after designing a filter, the discrete wavelet transform is applied to the input images and the approximate coefficients of scaling components are constructed. Then, the different parts of image are classified in continuous spectrum. In the next step, by using adaptive K-means algorithm for initializing and smart choice of clusters' number, the appropriate threshold is selected. Finally, the suspicious cancerous mass is separated by implementing the image processing techniques. We Received 120 mammographic images in LJPEG format, which had been scanned in Gray-Scale with 50 microns size, 3% noise and 20% INU from clinical data taken from two medical databases (mini-MIAS and DDSM). The proposed algorithm detected tumors at an acceptable level with an average accuracy of 92.32% and sensitivity of 90.24%. Also, the Kappa coefficient was approximately 0.85, which proved the suitable reliability of the system performance. The exact positioning of the cancerous tumors allows the radiologist to determine the stage of disease progression and suggest an appropriate treatment in accordance with the tumor growth. The low PPV and high NPV of the system is a warranty of the system and both clinical specialists and patients can trust its output.

  14. Wavelet Entropy-Based Traction Inverter Open Switch Fault Diagnosis in High-Speed Railways

    Directory of Open Access Journals (Sweden)

    Keting Hu

    2016-03-01

    Full Text Available In this paper, a diagnosis plan is proposed to settle the detection and isolation problem of open switch faults in high-speed railway traction system traction inverters. Five entropy forms are discussed and compared with the traditional fault detection methods, namely, discrete wavelet transform and discrete wavelet packet transform. The traditional fault detection methods cannot efficiently detect the open switch faults in traction inverters because of the low resolution or the sudden change of the current. The performances of Wavelet Packet Energy Shannon Entropy (WPESE, Wavelet Packet Energy Tsallis Entropy (WPETE with different non-extensive parameters, Wavelet Packet Energy Shannon Entropy with a specific sub-band (WPESE3,6, Empirical Mode Decomposition Shannon Entropy (EMDESE, and Empirical Mode Decomposition Tsallis Entropy (EMDETE with non-extensive parameters in detecting the open switch fault are evaluated by the evaluation parameter. Comparison experiments are carried out to select the best entropy form for the traction inverter open switch fault detection. In addition, the DC component is adopted to isolate the failure Isolated Gate Bipolar Transistor (IGBT. The simulation experiments show that the proposed plan can diagnose single and simultaneous open switch faults correctly and timely.

  15. Analysis of two dimensional signals via curvelet transform

    Science.gov (United States)

    Lech, W.; Wójcik, W.; Kotyra, A.; Popiel, P.; Duk, M.

    2007-04-01

    This paper describes an application of curvelet transform analysis problem of interferometric images. Comparing to two-dimensional wavelet transform, curvelet transform has higher time-frequency resolution. This article includes numerical experiments, which were executed on random interferometric image. In the result of nonlinear approximations, curvelet transform obtains matrix with smaller number of coefficients than is guaranteed by wavelet transform. Additionally, denoising simulations show that curvelet could be a very good tool to remove noise from images.

  16. An arrhythmia classification algorithm using a dedicated wavelet adapted to different subjects.

    Science.gov (United States)

    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.

  17. 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.

  18. Accelerating wavelet-based video coding on graphics hardware using CUDA

    NARCIS (Netherlands)

    Laan, van der W.J.; Roerdink, J.B.T.M.; Jalba, A.C.; Zinterhof, P.; Loncaric, S.; Uhl, A.; Carini, A.

    2009-01-01

    The DiscreteWavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory and computation efficient way on modern, programmable GPUs, which can be regarded as massively

  19. Accelerating Wavelet-Based Video Coding on Graphics Hardware using CUDA

    NARCIS (Netherlands)

    Laan, Wladimir J. van der; Roerdink, Jos B.T.M.; Jalba, Andrei C.; Zinterhof, P; Loncaric, S; Uhl, A; Carini, A

    2009-01-01

    The Discrete Wavelet Transform (DWT) has a wide range of applications from signal processing to video and image compression. This transform, by means of the lifting scheme, can be performed in a memory mid computation efficient way on modern, programmable GPUs, which can be regarded as massively

  20. a pyramid algorithm for the haar discrete wavelet packet transform

    African Journals Online (AJOL)

    PROF EKWUEME

    computer-aided signal processing of non-stationary signals, this paper develops a pyramid algorithm for the discrete wavelet packet ... Edith T. Luhanga, School of Computational and Communication Sciences and Engineering, Nelson Mandela African. Institute of ..... Mathematics, Washington University. 134. EDITH T.

  1. Multispectral image pansharpening based on the contourlet transform

    Energy Technology Data Exchange (ETDEWEB)

    Amro, Israa; Mateos, Javier, E-mail: iamro@correo.ugr.e, E-mail: jmd@decsai.ugr.e [Departamento de Ciencias de la Computacion e I.A., Universidad de Granada, 18071 Granada (Spain)

    2010-02-01

    Pansharpening is a technique that fuses the information of a low resolution multispectral image (MS) and a high resolution panchromatic image (PAN), usually remote sensing images, to provide a high resolution multispectral image. In the literature, this task has been addressed from different points of view being one of the most popular the wavelets based algorithms. Recently, the contourlet transform has been proposed. This transform combines the advantages of the wavelets transform with a more efficient directional information representation. In this paper we propose a new pansharpening method based on contourlets, compare with its wavelet counterpart and assess its performance numerically and visually.

  2. Harmonic analysis of traction power supply system based on wavelet decomposition

    Science.gov (United States)

    Dun, Xiaohong

    2018-05-01

    With the rapid development of high-speed railway and heavy-haul transport, AC drive electric locomotive and EMU large-scale operation in the country on the ground, the electrified railway has become the main harmonic source of China's power grid. In response to this phenomenon, the need for timely monitoring of power quality problems of electrified railway, assessment and governance. Wavelet transform is developed on the basis of Fourier analysis, the basic idea comes from the harmonic analysis, with a rigorous theoretical model, which has inherited and developed the local thought of Garbor transformation, and has overcome the disadvantages such as window fixation and lack of discrete orthogonally, so as to become a more recently studied spectral analysis tool. The wavelet analysis takes the gradual and precise time domain step in the high frequency part so as to focus on any details of the signal being analyzed, thereby comprehensively analyzing the harmonics of the traction power supply system meanwhile use the pyramid algorithm to increase the speed of wavelet decomposition. The matlab simulation shows that the use of wavelet decomposition of the traction power supply system for harmonic spectrum analysis is effective.

  3. Wigner functions from the two-dimensional wavelet group.

    Science.gov (United States)

    Ali, S T; Krasowska, A E; Murenzi, R

    2000-12-01

    Following a general procedure developed previously [Ann. Henri Poincaré 1, 685 (2000)], here we construct Wigner functions on a phase space related to the similitude group in two dimensions. Since the group space in this case is topologically homeomorphic to the phase space in question, the Wigner functions so constructed may also be considered as being functions on the group space itself. Previously the similitude group was used to construct wavelets for two-dimensional image analysis; we discuss here the connection between the wavelet transform and the Wigner function.

  4. Sparse data structure design for wavelet-based methods

    Directory of Open Access Journals (Sweden)

    Latu Guillaume

    2011-12-01

    Full Text Available This course gives an introduction to the design of efficient datatypes for adaptive wavelet-based applications. It presents some code fragments and benchmark technics useful to learn about the design of sparse data structures and adaptive algorithms. Material and practical examples are given, and they provide good introduction for anyone involved in the development of adaptive applications. An answer will be given to the question: how to implement and efficiently use the discrete wavelet transform in computer applications? A focus will be made on time-evolution problems, and use of wavelet-based scheme for adaptively solving partial differential equations (PDE. One crucial issue is that the benefits of the adaptive method in term of algorithmic cost reduction can not be wasted by overheads associated to sparse data management.

  5. Properties of wavelet discretization of Black-Scholes equation

    Science.gov (United States)

    Finěk, Václav

    2017-07-01

    Using wavelet methods, the continuous problem is transformed into a well-conditioned discrete problem. And once a non-symmetric problem is given, squaring yields a symmetric positive definite formulation. However squaring usually makes the condition number of discrete problems substantially worse. This note is concerned with a wavelet based numerical solution of the Black-Scholes equation for pricing European options. We show here that in wavelet coordinates a symmetric part of the discretized equation dominates over an unsymmetric part in the standard economic environment with low interest rates. It provides some justification for using a fractional step method with implicit treatment of the symmetric part of the weak form of the Black-Scholes operator and with explicit treatment of its unsymmetric part. Then a well-conditioned discrete problem is obtained.

  6. 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.

  7. Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

    OpenAIRE

    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...

  8. 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)

  9. Traffic characterization and modeling of wavelet-based VBR encoded video

    Energy Technology Data Exchange (ETDEWEB)

    Yu Kuo; Jabbari, B. [George Mason Univ., Fairfax, VA (United States); Zafar, S. [Argonne National Lab., IL (United States). Mathematics and Computer Science Div.

    1997-07-01

    Wavelet-based video codecs provide a hierarchical structure for the encoded data, which can cater to a wide variety of applications such as multimedia systems. The characteristics of such an encoder and its output, however, have not been well examined. In this paper, the authors investigate the output characteristics of a wavelet-based video codec and develop a composite model to capture the traffic behavior of its output video data. Wavelet decomposition transforms the input video in a hierarchical structure with a number of subimages at different resolutions and scales. the top-level wavelet in this structure contains most of the signal energy. They first describe the characteristics of traffic generated by each subimage and the effect of dropping various subimages at the encoder on the signal-to-noise ratio at the receiver. They then develop an N-state Markov model to describe the traffic behavior of the top wavelet. The behavior of the remaining wavelets are then obtained through estimation, based on the correlations between these subimages at the same level of resolution and those wavelets located at an immediate higher level. In this paper, a three-state Markov model is developed. The resulting traffic behavior described by various statistical properties, such as moments and correlations, etc., is then utilized to validate their model.

  10. Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach

    Science.gov (United States)

    Aloui, Chaker; Jammazi, Rania

    2015-10-01

    In this article, we propose a wavelet-based approach to accommodate the stylized facts and complex structure of financial data, caused by frequent and abrupt changes of markets and noises. Specifically, we show how the combination of both continuous and discrete wavelet transforms with traditional financial models helps improve portfolio's market risk assessment. In the empirical stage, three wavelet-based models (wavelet-EGARCH with dynamic conditional correlations, wavelet-copula, and wavelet-extreme value) are considered and applied to crude oil price and US dollar exchange rate data. Our findings show that the wavelet-based approach provides an effective and powerful tool for detecting extreme moments and improving the accuracy of VaR and Expected Shortfall estimates of oil-exchange rate portfolios after noise is removed from the original data.

  11. Noise Reduction planar bone imaging nuclear medicine with the use of wavelet transform: an assessment of its quality

    International Nuclear Information System (INIS)

    Casas Cardoso, Maria del Carmen; Perez Diaz, Marlen; Casas Cardoso, Gladis; Lorenzo Ginori, Juan; Paz Viera, Juan Enrique; Roque Diaz, Reinaldo; Cardenas Barreras, Julian

    2009-01-01

    Diagnostic imaging of Nuclear Medicine (MN), is highly used in Oncology, as it constitutes a noninvasive technique that allows early detection of tumors and assessment of therapeutic response of patients under treatment. However, particularly planar scintigraphy images, can be prone to problems of detectability of small lesions, because they are contaminated with noise, a phenomenon which is accentuated by the inability to increase the dose of the radiopharmaceutical or time acquisition of images of the patient over 'certain levels'. The aim of this work is to improve the detectability of tumors of bone. We describe an algorithm for random noise reduction using the wavelet transform (TW). The quality of the resulting images are evaluated through quantitative metrics such as Signal to Noise Ratio (SNR), the Mean Square Error (NMSEA) and Structural Similarity Index (SSIM). It also includes a subjective assessment of image quality by expert criteria, using a variant of the methodology FROC (Free-Response ROC). It was found that some of the filters designed in the wavelet domain, significantly improve the quality of planar bone imaging in terms of increased signal to noise ratio without implying notable structural distortions, which facilitates clinical diagnosis. (author)

  12. Medical image compression based on vector quantization with variable block sizes in wavelet domain.

    Science.gov (United States)

    Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

    2012-01-01

    An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

  13. A New Alignment Method Based on The Wavelet Multi-Scale Cross-Correlation for Noisy High Resolution ECG Records

    National Research Council Canada - National Science Library

    Laciar, E

    2001-01-01

    ... between the wavelet transforms of the template and the detected beat, respectively. The wavelet and temporal methods were tested for several simulated records corrupted with white noise and electromyographic (EMG...

  14. Wavelet Filtering to Reduce Conservatism in Aeroservoelastic Robust Stability Margins

    Science.gov (United States)

    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.

  15. 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 ...

  16. Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.

    Science.gov (United States)

    Gao, Zheyu; Lin, Jing; Wang, Xiufeng; Xu, Xiaoqiang

    2017-05-24

    Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

  17. Application of the wavelet image analysis technique to monitor cell concentration in bioprocesses

    Directory of Open Access Journals (Sweden)

    G. J. R. Garófano

    2005-12-01

    Full Text Available The growth of cells of great practical interest, such as, the filamentous cells of bacterium Streptomyces clavuligerus, the yeast Saccharomyces cerevisiae and the insect Spodoptera frugiperda (Sf9 cell, cultivated in shaking flasks with complex media at appropriate temperatures and pHs, was quantified by the new wavelet transform technique. This image analysis tool was implemented using Matlab 5.2 software to process digital images acquired of samples taken of these three types of cells throughoot their cultivation. The values of the average wavelet coefficients (AWCs of simplified images were compared with experimental measurements of cell concentration and with computer-based densitometric measurements. AWCs were shown to be directly proportional to measurements of cell concentration and to densitometric measurements, making evident the great potential of the wavelet transform technique to quantitatively estimate the growth of several types of cells.

  18. Using wavelet denoising and mathematical morphology in the segmentation technique applied to blood cells images

    OpenAIRE

    Boix García, Macarena; Cantó Colomina, Begoña

    2013-01-01

    Accurate image segmentation is used in medical diagnosis since this technique is a noninvasive pre-processing step for biomedical treatment. In this work we present an efficient segmentation method for medical image analysis. In particular, with this method blood cells can be segmented. For that, we combine the wavelet transform with morphological operations. Moreover, the wavelet thresholding technique is used to eliminate the noise and prepare the image for suitable segmentation. In wavelet...

  19. Reactor condition monitoring and singularity detection via wavelet and use of entropy in Monte Carlo calculation

    International Nuclear Information System (INIS)

    Kim, Ok Joo

    2007-02-01

    Wavelet theory was applied to detect the singularity in reactor power signal. Compared to Fourier transform, wavelet transform has localization properties in space and frequency. Therefore, by wavelet transform after de-noising, singular points can be found easily. To demonstrate this, we generated reactor power signals using a HANARO (a Korean multi-purpose research reactor) dynamics model consisting of 39 nonlinear differential equations and Gaussian noise. We applied wavelet transform decomposition and de-noising procedures to these signals. It was effective to detect the singular events such as sudden reactivity change and abrupt intrinsic property changes. Thus this method could be profitably utilized in a real-time system for automatic event recognition (e.g., reactor condition monitoring). In addition, using the wavelet de-noising concept, variance reduction of Monte Carlo result was tried. To get correct solution in Monte Carlo calculation, small uncertainty is required and it is quite time-consuming on a computer. Instead of long-time calculation in the Monte Carlo code (MCNP), wavelet de-noising can be performed to get small uncertainties. We applied this idea to MCNP results of k eff and fission source. Variance was reduced somewhat while the average value is kept constant. In MCNP criticality calculation, initial guess for the fission distribution is used and it could give contamination to solution. To avoid this situation, sufficient number of initial generations should be discarded, and they are called inactive cycles. Convergence check can give guildeline to determine when we should start the active cycles. Various entropy functions are tried to check the convergence of fission distribution. Some entropy functions reflect the convergence behavior of fission distribution well. Entropy could be a powerful method to determine inactive/active cycles in MCNP calculation

  20. 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)

  1. PERFORMANCE ANALYSIS OF SET PARTITIONING IN HIERARCHICAL TREES (SPIHT ALGORITHM FOR A FAMILY OF WAVELETS USED IN COLOR IMAGE COMPRESSION

    Directory of Open Access Journals (Sweden)

    A. Sreenivasa Murthy

    2014-11-01

    Full Text Available With the spurt in the amount of data (Image, video, audio, speech, & text available on the net, there is a huge demand for memory & bandwidth savings. One has to achieve this, by maintaining the quality & fidelity of the data acceptable to the end user. Wavelet transform is an important and practical tool for data compression. Set partitioning in hierarchal trees (SPIHT is a widely used compression algorithm for wavelet transformed images. Among all wavelet transform and zero-tree quantization based image compression algorithms SPIHT has become the benchmark state-of-the-art algorithm because it is simple to implement & yields good results. In this paper we present a comparative study of various wavelet families for image compression with SPIHT algorithm. We have conducted experiments with Daubechies, Coiflet, Symlet, Bi-orthogonal, Reverse Bi-orthogonal and Demeyer wavelet types. The resulting image quality is measured objectively, using peak signal-to-noise ratio (PSNR, and subjectively, using perceived image quality (human visual perception, HVP for short. The resulting reduction in the image size is quantified by compression ratio (CR.

  2. Wavelet transform analysis of electromyography kung fu strikes data.

    Science.gov (United States)

    Neto, Osmar Pinto; Marzullo, Ana Carolina de Miranda

    2009-11-01

    In martial arts and contact sports strikes are performed at near maximum speeds. For that reason, electromyography (EMG) analysis of such movements is non-trivial. This paper has three main goals: firstly, to investigate the differences in the EMG activity of muscles during strikes performed with and without impacts; secondly, to assess the advantages of using Sum of Significant Power (SSP) values instead of root mean square (rms) values when analyzing EMG data; and lastly to introduce a new method of calculating median frequency values using wavelet transforms (WMDF). EMG data of the deltoid anterior (DA), triceps brachii (TB) and brachioradialis (BR) muscles were collected from eight Kung Fu practitioners during strikes performed with and without impacts. SSP results indicated significant higher muscle activity (p = 0.023) for the strikes with impact. WMDF results, on the other hand, indicated significant lower values (p = 0. 007) for the strikes with impact. SSP results presented higher sensitivity than rms to quantify important signal differences and, at the same time, presented lower inter-subject coefficient of variations. The result of increase in SSP values and decrease in WMDF may suggest better synchronization of motor units for the strikes with impact performed by the experienced Kung Fu practitioners. Key PointsThe results show higher muscle activity and lower electromyography median frequencies for strikes with impact compared to strikes without.SSP results presented higher sensitivity and lower inter-subject coefficient of variations than rms results.Kung Fu palm strikes with impact may present better motor units' synchronization than strikes without.

  3. Constructing pairs of dual bandlimited frame wavelets in L^2(R^n)

    DEFF Research Database (Denmark)

    Lemvig, Jakob

    2012-01-01

    combination of dilations of ψ with explicitly given coefficients. The result allows a simple construction procedure for pairs of dual wavelet frames whose generators have compact support in the Fourier domain and desired time localization. The construction relies on a technical condition on ψ, and we exhibit......Given a real, expansive dilation matrix we prove that any bandlimited function ψ∈L2(Rn), for which the dilations of its Fourier transform form a partition of unity, generates a wavelet frame for certain translation lattices. Moreover, there exists a dual wavelet frame generated by a finite linear...

  4. A Quantitative Analysis of an EEG Epileptic Record Based on MultiresolutionWavelet Coefficients

    Directory of Open Access Journals (Sweden)

    Mariel Rosenblatt

    2014-11-01

    Full Text Available The characterization of the dynamics associated with electroencephalogram (EEG signal combining an orthogonal discrete wavelet transform analysis with quantifiers originated from information theory is reviewed. In addition, an extension of this methodology based on multiresolution quantities, called wavelet leaders, is presented. In particular, the temporal evolution of Shannon entropy and the statistical complexity evaluated with different sets of multiresolution wavelet coefficients are considered. Both methodologies are applied to the quantitative EEG time series analysis of a tonic-clonic epileptic seizure, and comparative results are presented. In particular, even when both methods describe the dynamical changes of the EEG time series, the one based on wavelet leaders presents a better time resolution.

  5. The wavelet/scalar quantization compression standard for digital fingerprint images

    Energy Technology Data Exchange (ETDEWEB)

    Bradley, J.N.; Brislawn, C.M.

    1994-04-01

    A new digital image compression standard has been adopted by the US Federal Bureau of Investigation for use on digitized gray-scale fingerprint images. The algorithm is based on adaptive uniform scalar quantization of a discrete wavelet transform image decomposition and is referred to as the wavelet/scalar quantization standard. The standard produces archival quality images at compression ratios of around 20:1 and will allow the FBI to replace their current database of paper fingerprint cards with digital imagery.

  6. A hybrid wavelet transform based short-term wind speed forecasting approach.

    Science.gov (United States)

    Wang, Jujie

    2014-01-01

    It is important to improve the accuracy of wind speed forecasting for wind parks management and wind power utilization. In this paper, a novel hybrid approach known as WTT-TNN is proposed for wind speed forecasting. In the first step of the approach, a wavelet transform technique (WTT) is used to decompose wind speed into an approximate scale and several detailed scales. In the second step, a two-hidden-layer neural network (TNN) is used to predict both approximated scale and detailed scales, respectively. In order to find the optimal network architecture, the partial autocorrelation function is adopted to determine the number of neurons in the input layer, and an experimental simulation is made to determine the number of neurons within each hidden layer in the modeling process of TNN. Afterwards, the final prediction value can be obtained by the sum of these prediction results. In this study, a WTT is employed to extract these different patterns of the wind speed and make it easier for forecasting. To evaluate the performance of the proposed approach, it is applied to forecast Hexi Corridor of China's wind speed. Simulation results in four different cases show that the proposed method increases wind speed forecasting accuracy.

  7. Wavelet-Based Processing for Fiber Optic Sensing Systems

    Science.gov (United States)

    Hamory, Philip J. (Inventor); Parker, Allen R., Jr. (Inventor)

    2016-01-01

    The present invention is an improved method of processing conglomerate data. The method employs a Triband Wavelet Transform that decomposes and decimates the conglomerate signal to obtain a final result. The invention may be employed to improve performance of Optical Frequency Domain Reflectometry systems.

  8. FEATURE EXTRACTION BASED WAVELET TRANSFORM IN BREAST CANCER DIAGNOSIS USING FUZZY AND NON-FUZZY CLASSIFICATION

    Directory of Open Access Journals (Sweden)

    Pelin GORGEL

    2013-01-01

    Full Text Available This study helps to provide a second eye to the expert radiologists for the classification of manually extracted breast masses taken from 60 digital mammıgrams. These mammograms have been acquired from Istanbul University Faculty of Medicine Hospital and have 78 masses. The diagnosis is implemented with pre-processing by using feature extraction based Fast Wavelet Transform (FWT. Afterwards Adaptive Neuro-Fuzzy Inference System (ANFIS based fuzzy subtractive clustering and Support Vector Machines (SVM methods are used for the classification. It is a comparative study which uses these methods respectively. According to the results of the study, ANFIS based subtractive clustering produces ??% while SVM produces ??% accuracy in malignant-benign classification. The results demonstrate that the developed system could help the radiologists for a true diagnosis and decrease the number of the missing cancerous regions or unnecessary biopsies.

  9. Diagnostics of detector tube impacting with wavelet techniques

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A. [KFKI-AEKI Applied Reactor Physics, Budapest (Hungary); Pazsit, I. [Chalmers Univ. of Tech., Goeteborg (Sweden). Dept. of Reactor Physics

    1997-12-08

    A neutron noise based method is proposed for the detection of impacting of detector tubes in BWRs. The basic idea relies on the assumption that non-stationary transients (e.g. fuel box vibrations) may be induced at impacting. Such short-lived transients are difficult to detect by spectral analysis methods. However, their presence in the detector signal can be detected by wavelet analysis. A simple wavelet technique, the so-called Haar transform, is suggested for the detection of impacting. Tests of the proposed method have been performed with success on both simulated data with controlled impacting as well as with real measurement data. The simulation model as well as the results of the wavelet analysis are reported in this paper. The source code written in MATLAB are available at a public ftp site. The necessary information to reproduce the simulation results is also reported. (author).

  10. Diagnostics of detector tube impacting with wavelet techniques

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A.; Pazsit, I

    1998-04-01

    A neutron noise based method is proposed for the detection of impacting of detector tubes in BWRs. The basic idea relies on the assumption that non-stationary transients (e.g. fuel box vibrations) may be induced at impacting. Such short-lived transients are difficult to detect by spectral analysis methods. However, their presence in the detector signal can be detected by wavelet analysis. A simple wavelet technique, the so-called Haar transform, is suggested for the detection of impacting. Tests of the proposed method have been performed with success on both simulated data with controlled impacting as well as with real measurement data. The simulation model as well as the results of the wavelet analysis are reported in this paper. The source codes written in MATLAB[reg] are available at a public ftp site. The necessary information to reproduce the simulation results is also reported.

  11. Wavelet regression model in forecasting crude oil price

    Science.gov (United States)

    Hamid, Mohd Helmie; Shabri, Ani

    2017-05-01

    This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

  12. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

    Science.gov (United States)

    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.

  13. An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes

    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.

  14. Investigating the effect of traditional Persian music on ECG signals in young women using wavelet transform and neural networks.

    Science.gov (United States)

    Abedi, Behzad; Abbasi, Ataollah; Goshvarpour, Atefeh

    2017-05-01

    In the past few decades, several studies have reported the physiological effects of listening to music. The physiological effects of different music types on different people are different. In the present study, we aimed to examine the effects of listening to traditional Persian music on electrocardiogram (ECG) signals in young women. Twenty-two healthy females participated in this study. ECG signals were recorded under two conditions: rest and music. For each ECG signal, 20 morphological and wavelet-based features were selected. Artificial neural network (ANN) and probabilistic neural network (PNN) classifiers were used for the classification of ECG signals during and before listening to music. Collected data were separated into two data sets: train and test. Classification accuracies of 88% and 97% were achieved in train data sets using ANN and PNN, respectively. In addition, the test data set was employed for evaluating the classifiers, and classification rates of 84% and 93% were obtained using ANN and PNN, respectively. The present study investigated the effect of music on ECG signals based on wavelet transform and morphological features. The results obtained here can provide a good understanding on the effects of music on ECG signals to researchers.

  15. Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features.

    Science.gov (United States)

    Bhagavatula, Chandrasekhar; Venugopalan, Shreyas; Blue, Rebecca; Friedman, Robert; Griofa, Marc O; Savvides, Marios; Kumar, B V K Vijaya

    2012-01-01

    In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification.

  16. Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Huiyan Jiang

    2012-01-01

    Full Text Available An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

  17. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

  18. Optimal sampling in damage detection of flexural beams by continuous wavelet transform

    International Nuclear Information System (INIS)

    Basu, B; Broderick, B M; Montanari, L; Spagnoli, A

    2015-01-01

    Modern measurement techniques are improving in capability to capture spatial displacement fields occurring in deformed structures with high precision and in a quasi-continuous manner. This in turn has made the use of vibration-based damage identification methods more effective and reliable for real applications. However, practical measurement and data processing issues still present barriers to the application of these methods in identifying several types of structural damage. This paper deals with spatial Continuous Wavelet Transform (CWT) damage identification methods in beam structures with the aim of addressing the following key questions: (i) can the cost of damage detection be reduced by down-sampling? (ii) what is the minimum number of sampling intervals required for optimal damage detection ? The first three free vibration modes of a cantilever and a simple supported beam with an edge open crack are numerically simulated. A thorough parametric study is carried out by taking into account the key parameters governing the problem, including level of noise, crack depth and location, mechanical and geometrical parameters of the beam. The results are employed to assess the optimal number of sampling intervals for effective damage detection. (paper)

  19. Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

    Science.gov (United States)

    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.

  20. Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

    Science.gov (United States)

    Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

    2017-11-01

    Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.