WorldWideScience

Sample records for based multi wavelet

  1. Multi-Level Wavelet Shannon Entropy-Based Method for Single-Sensor Fault Location

    Directory of Open Access Journals (Sweden)

    Qiaoning Yang

    2015-10-01

    Full Text Available In actual application, sensors are prone to failure because of harsh environments, battery drain, and sensor aging. Sensor fault location is an important step for follow-up sensor fault detection. In this paper, two new multi-level wavelet Shannon entropies (multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are defined. They take full advantage of sensor fault frequency distribution and energy distribution across multi-subband in wavelet domain. Based on the multi-level wavelet Shannon entropy, a method is proposed for single sensor fault location. The method firstly uses a criterion of maximum energy-to-Shannon entropy ratio to select the appropriate wavelet base for signal analysis. Then multi-level wavelet time Shannon entropy and multi-level wavelet time-energy Shannon entropy are used to locate the fault. The method is validated using practical chemical gas concentration data from a gas sensor array. Compared with wavelet time Shannon entropy and wavelet energy Shannon entropy, the experimental results demonstrate that the proposed method can achieve accurate location of a single sensor fault and has good anti-noise ability. The proposed method is feasible and effective for single-sensor fault location.

  2. Multi-frequency fringe projection profilometry based on wavelet transform.

    Science.gov (United States)

    Jiang, Chao; Jia, Shuhai; Dong, Jun; Lian, Qin; Li, Dichen

    2016-05-30

    Based on wavelet transforms (WTs), an alternative multi-frequency fringe projection profilometry is described. Fringe patterns with multiple frequencies are projected onto an object and the reflected patterns are recorded digitally. Phase information for every pattern is calculated by identifying the ridge that appears in WT results. Distinct from the phase unwrapping process, a peak searching algorithm is applied to obtain object height from the phases of the different frequency for a single point on the object. Thus, objects with large discontinuities can be profiled. In comparing methods, the height profiles obtained from the WTs have lower noise and higher measurement accuracy. Although measuring times are similar, the proposed method offers greater reliability. PMID:27410063

  3. Balance of multi-wavelets

    Institute of Scientific and Technical Information of China (English)

    MAO Yibo

    2003-01-01

    The discrete scalar data need prefiltering when transformed by discrete multi-wavelet, but prefiltering will make some properties of multi-wavelets lost. Balanced multi-wavelets can avoid prefiltering. The sufficient and necessary condition of p-order balance for multi-wavelets in time domain, the interrelation between balance order and approximation order and the sampling property of balanced multi-wavelets are investigated. The algorithms of 1-0rder and 2-0rder balancing for multi-wavelets are obtained. The two algorithms both preserve the orthogonal relation between multi-scaling function and multi-wavelets. More importantly, balancing operation doesn't increase the length of filters, which suggests that a relatively short balanced multiwavelet can be constructed from an existing unbalanced multi-wavelet as short as possible.

  4. Multi-spectral image fusion method based on two channels non-separable wavelets

    Institute of Scientific and Technical Information of China (English)

    LIU Bin; PENG JiaXiong

    2008-01-01

    A construction method of two channels non-separable wavelets filter bank which dilation matrix is [1, 1; 1, -1] and its application in the fusion of multi-spectral image are presented. Many 4x4 filter banks are designed. The multi-spectral image fusion algorithm based on this kind of wavelet is proposed. Using this filter bank, multi-resolution wavelet decomposition of the intensity of multi-spectral image and panchromatic image is performed, and the two low-frequency components of the intensity and the panchromatic image are merged by using a tradeoff parameter. The experiment results show that this method is good in the preservation of spectral quality and high spatial resolution information. Its performance in preserving spectral quality and high spatial information is better than the fusion method based on DWFT and IHS. When the parameter t is closed to 1, the fused image can obtain rich spectral information from the original MS image. The amount of computation reduced to only half of the fusion method based on four channels wavelet transform.

  5. Multi-Modality Medical Image Fusion Based on Wavelet Analysis and Quality Evaluation

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Multi-modality medical image fusion has more and more important applications in medical image analysisand understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fusemedical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusionresults when applying different selection rules and obtain optimum combination of fusion parameters.

  6. Multi-level denoising and enhancement method based on wavelet transform for mine monitoring

    Institute of Scientific and Technical Information of China (English)

    Yanqin Zhao

    2013-01-01

    Based on low illumination and a large number of mixed noises contained in coal mine,denoising with one method usually cannot achieve good results,So a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented.Firstly,we used directional median filter to effectively reduce impulse noise in the spatial domain,which is the main cause of noise in mine.Secondly,we used a Wiener filtration method to mainly reduce the Gaussian noise,and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain.This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits,and effectively reduce impulse noise and Gaussian noise in a coal mine,while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.

  7. Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform

    Science.gov (United States)

    Wu, Zhi-guo; Wang, Ming-jia; Han, Guang-liang

    2011-08-01

    Being an efficient method of information fusion, image fusion has been used in many fields such as machine vision, medical diagnosis, military applications and remote sensing. In this paper, Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing, including segmentation, target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First, the two original images are decomposed by wavelet transform. Then, based on the PCNN, a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength, so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So, the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment, the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range, which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore, by this algorithm, the threshold adjusting constant is estimated by appointed iteration number. Furthermore, In order to sufficient reflect order of the firing time, the threshold adjusting constant αΘ is estimated by appointed iteration number. So after the iteration achieved, each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules, the experiments upon Multi-focus image are done. Moreover

  8. The design and implementation of signal decomposition system of CL multi-wavelet transform based on DSP builder

    Science.gov (United States)

    Huang, Yan; Wang, Zhihui

    2015-12-01

    With the development of FPGA, DSP Builder is widely applied to design system-level algorithms. The algorithm of CL multi-wavelet is more advanced and effective than scalar wavelets in processing signal decomposition. Thus, a system of CL multi-wavelet based on DSP Builder is designed for the first time in this paper. The system mainly contains three parts: a pre-filtering subsystem, a one-level decomposition subsystem and a two-level decomposition subsystem. It can be converted into hardware language VHDL by the Signal Complier block that can be used in Quartus II. After analyzing the energy indicator, it shows that this system outperforms Daubenchies wavelet in signal decomposition. Furthermore, it has proved to be suitable for the implementation of signal fusion based on SoPC hardware, and it will become a solid foundation in this new field.

  9. A NOVEL ALGORITHM OF MULTI-SENSOR IMAGE FUSION BASED ON WAVELET PACKET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    In order to enhance the image information from multi-sensor and to improve the abilities of theinformation analysis and the feature extraction, this letter proposed a new fusion approach in pixel level bymeans of the Wavelet Packet Transform (WPT). The WPT is able to decompose an image into low frequencyband and high frequency band in higher scale. It offers a more precise method for image analysis than Wave-let Transform (WT). Firstly, the proposed approach employs HIS (Hue, Intensity, Saturation) transform toobtain the intensity component of CBERS (China-Brazil Earth Resource Satellite) multi-spectral image. ThenWPT transform is employed to decompose the intensity component and SPOT (Systeme Pour I'Observationde la Therre ) image into low frequency band and high frequency band in three levels. Next, two high fre-quency coefficients and low frequency coefficients of the images are combined by linear weighting strategies.Finally, the fused image is obtained with inverse WPT and inverse HIS. The results show the new approachcan fuse details of input image successfully, and thereby can obtain a more satisfactory result than that of HM(Histogram Matched)-based fusion algorithm and WT-based fusion approach.

  10. Wavelet-Based Multi-Scale Entropy Analysis of Complex Rainfall Time Series

    OpenAIRE

    Chien-Ming Chou

    2011-01-01

    This paper presents a novel framework to determine the number of resolution levels in the application of a wavelet transformation to a rainfall time series. The rainfall time series are decomposed using the à trous wavelet transform. Then, multi-scale entropy (MSE) analysis that helps to elucidate some hidden characteristics of the original rainfall time series is applied to the decomposed rainfall time series. The analysis shows that the Mann-Kendall (MK) rank correlation test of MSE curves ...

  11. Wavelet Based Image Denoising Technique

    Directory of Open Access Journals (Sweden)

    Sachin D Ruikar

    2011-03-01

    Full Text Available This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poisson’s, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.

  12. Improving the Performance of Machine Learning Based Multi Attribute Face Recognition Algorithm Using Wavelet Based Image Decomposition Technique

    Directory of Open Access Journals (Sweden)

    S. Sakthivel

    2011-01-01

    Full Text Available Problem statement: Recognizing a face based attributes is an easy task for a human to perform; it is closely automated and requires little mental effort. A computer, on the other hand, has no innate ability to recognize a face or a facial feature and must be programmed with an algorithm to do so. Generally, to recognize a face, different kinds of the facial features were used separately or in a combined manner. In the previous work, we have developed a machine learning based multi attribute face recognition algorithm and evaluated it different set of weights to each input attribute and performance wise it is low compared to proposed wavelet decomposition technique. Approach: In this study, wavelet decomposition technique has been applied as a preprocessing technique to enhance the input face images in order to reduce the loss of classification performance due to changes in facial appearance. The Experiment was specifically designed to investigate the gain in robustness against illumination and facial expression changes. Results: In this study, a wavelet based image decomposition technique has been proposed to enhance the performance by 8.54 percent of the previously designed system. Conclusion: The proposed model has been tested on face images with difference in expression and illumination condition with a dataset obtained from face image databases from Olivetti Research Laboratory.

  13. Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

    OpenAIRE

    Li-Ye Zhao; Lei Wang; Ru-Qiang Yan

    2015-01-01

    This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD) with multi-scale permutation entropy (MPE). The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band sign...

  14. SEVERAL ELEMENTS OF THE MULTI-RESOLUTION WAVELET ANALYSIS. Part 1: BASICS OF THE WAVELETS AND THE MULTI-RESOLUTION WAVELET ANALYSIS

    OpenAIRE

    Akimov Pavel Alekseevich; Mozgaleva Marina Leonidovna

    2012-01-01

    Part 1 of this paper represents an introduction into the multi-resolution wavelet analysis. The wavelet-based analysis is an exciting new problem-solving tool used by mathematicians, scientists and engineers. In the paper, the authors try to present the fundamental elements of the multi-resolution wavelet analysis in a way that is accessible to an engineer, a scientist and an applied mathematician both as a theoretical approach and as a potential practical method of solving problems (particul...

  15. Multi-source feature extraction and target recognition in wireless sensor networks based on adaptive distributed wavelet compression algorithms

    Science.gov (United States)

    Hortos, William S.

    2008-04-01

    Proposed distributed wavelet-based algorithms are a means to compress sensor data received at the nodes forming a wireless sensor network (WSN) by exchanging information between neighboring sensor nodes. Local collaboration among nodes compacts the measurements, yielding a reduced fused set with equivalent information at far fewer nodes. Nodes may be equipped with multiple sensor types, each capable of sensing distinct phenomena: thermal, humidity, chemical, voltage, or image signals with low or no frequency content as well as audio, seismic or video signals within defined frequency ranges. Compression of the multi-source data through wavelet-based methods, distributed at active nodes, reduces downstream processing and storage requirements along the paths to sink nodes; it also enables noise suppression and more energy-efficient query routing within the WSN. Targets are first detected by the multiple sensors; then wavelet compression and data fusion are applied to the target returns, followed by feature extraction from the reduced data; feature data are input to target recognition/classification routines; targets are tracked during their sojourns through the area monitored by the WSN. Algorithms to perform these tasks are implemented in a distributed manner, based on a partition of the WSN into clusters of nodes. In this work, a scheme of collaborative processing is applied for hierarchical data aggregation and decorrelation, based on the sensor data itself and any redundant information, enabled by a distributed, in-cluster wavelet transform with lifting that allows multiple levels of resolution. The wavelet-based compression algorithm significantly decreases RF bandwidth and other resource use in target processing tasks. Following wavelet compression, features are extracted. The objective of feature extraction is to maximize the probabilities of correct target classification based on multi-source sensor measurements, while minimizing the resource expenditures at

  16. A study of orthogonal, balanced and symmetric multi-wavelets on the interval

    Institute of Scientific and Technical Information of China (English)

    GAO Xieping; ZHOU Siwang

    2005-01-01

    The construction and properties of interval multi-wavelets based on symmetric/anti-symmetric orthogonal multi-wavelets on L2(R) with arbitrary supports and multiplicity 2 are introduced. The main contributions include that (1) we study the construction of general orthogonal interval multi-wavelets which preserve the polynomial- reproduction order, and obtain the parametric expressions of interval multi-wavelets; (2) we obtain the decomposition and reconstruction formulas of interval multi-wavelets; (3) we define the "balancing" concept of interval multi-wavelets for the first time and study the construction of orthogonal balancing multi-wavelets, which have been ignored in the past; (4) we study the necessary and sufficient conditions about the symmetry of interval multi-wavelets.

  17. Wavelet-based fluid motion estimation

    OpenAIRE

    Dérian, Pierre; Héas, Patrick; Herzet, Cédric; Mémin, Étienne

    2011-01-01

    International audience Based on a wavelet expansion of the velocity field, we present a novel optical flow algorithm dedicated to the estimation of continuous motion fields such as fluid flows. This scale-space representation, associated to a simple gradient-based optimization algorithm, naturally sets up a well-defined multi-resolution analysis framework for the optical flow estimation problem, thus avoiding the common drawbacks of standard multi-resolution schemes. Moreover, wavelet prop...

  18. SEVERAL ELEMENTS OF THE MULTI-RESOLUTION WAVELET ANALYSIS. Part 1: BASICS OF THE WAVELETS AND THE MULTI-RESOLUTION WAVELET ANALYSIS

    Directory of Open Access Journals (Sweden)

    Akimov Pavel Alekseevich

    2012-10-01

    Full Text Available Part 1 of this paper represents an introduction into the multi-resolution wavelet analysis. The wavelet-based analysis is an exciting new problem-solving tool used by mathematicians, scientists and engineers. In the paper, the authors try to present the fundamental elements of the multi-resolution wavelet analysis in a way that is accessible to an engineer, a scientist and an applied mathematician both as a theoretical approach and as a potential practical method of solving problems (particularly, boundary problems of structural mechanics and mathematical physics. The main goal of the contemporary wavelet research is to generate a set of basic functions (or general expansion functions and transformations that will provide an informative, efficient and useful description of a function or a signal. Another central idea is that of multi-resolution whereby decomposition of a signal represents the resolution of the detail. The multi-resolution decomposition seems to separate components of a signal in a way that is superior to most other methods of analysis, processing or compression. Due to the ability of the discrete wavelet transformation technique to decompose a signal at different independent scaling levels and to do it in a very flexible way, wavelets can be named "the microscopes of mathematics". Indeed, the use of the wavelet analysis and wavelet transformations requires a new point of view and a new method of interpreting representations.

  19. Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy

    Directory of Open Access Journals (Sweden)

    Li-Ye Zhao

    2015-09-01

    Full Text Available This paper presents a rolling bearing fault diagnosis approach by integrating wavelet packet decomposition (WPD with multi-scale permutation entropy (MPE. The approach uses MPE values of the sub-frequency band signals to identify faults appearing in rolling bearings. Specifically, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of sub-frequency band signals by means of the WPD method. Then, each sub-frequency band signal is divided into a series of subsequences, and MPEs of all subsequences in corresponding sub-frequency band signal are calculated. After that, the average MPE value of all subsequences about each sub-frequency band is calculated, and is considered as the fault feature of the corresponding sub-frequency band. Subsequently, MPE values of all sub-frequency bands are considered as input feature vectors, and the hidden Markov model (HMM is used to identify the fault pattern of the rolling bearing. Experimental study on a data set from the Case Western Reserve University bearing data center has shown that the presented approach can accurately identify faults in rolling bearings.

  20. A new wavelet-based thin plate element using B-spline wavelet on the interval

    Science.gov (United States)

    Jiawei, Xiang; Xuefeng, Chen; Zhengjia, He; Yinghong, Zhang

    2008-01-01

    By interacting and synchronizing wavelet theory in mathematics and variational principle in finite element method, a class of wavelet-based plate element is constructed. In the construction of wavelet-based plate element, the element displacement field represented by the coefficients of wavelet expansions in wavelet space is transformed into the physical degree of freedoms in finite element space via the corresponding two-dimensional C1 type transformation matrix. Then, based on the associated generalized function of potential energy of thin plate bending and vibration problems, the scaling functions of B-spline wavelet on the interval (BSWI) at different scale are employed directly to form the multi-scale finite element approximation basis so as to construct BSWI plate element via variational principle. BSWI plate element combines the accuracy of B-spline functions approximation and various wavelet-based elements for structural analysis. Some static and dynamic numerical examples are studied to demonstrate the performances of the present element.

  1. Signature Recognition using Multi Scale Fourier Descriptor And Wavelet Transform

    CERN Document Server

    Ismail, Ismail A; danaf, Talaat S El; Samak, Ahmed H

    2010-01-01

    This paper present a novel off-line signature recognition method based on multi scale Fourier Descriptor and wavelet transform . The main steps of constructing a signature recognition system are discussed and experiments on real data sets show that the average error rate can reach 1%. Finally we compare 8 distance measures between feature vectors with respect to the recognition performance. Key words: signature recognition; Fourier Descriptor; Wavelet transform; personal verification

  2. Method for Car in Dangerous Action Detection by Means of Wavelet Multi Resolution Analysis Based on Appropriate Support Length of Base Function

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-04-01

    Full Text Available Multi-Resolution Analysis: MRA based on the mother wavelet function with which support length differs from the image of the automobile rear under run is performed, and the run characteristic of a car is searched for. Speed, deflection, etc. are analyzed and the method of detecting vehicles with high accident danger is proposed. The experimental results show that vehicles in a dangerous action can be detected by the proposed method.

  3. Multi scale risk measurement in electricity market:a wavelet based value at risk approach

    Institute of Scientific and Technical Information of China (English)

    Guu; Sy-Ming; Lai; Kin; Keung

    2008-01-01

    Value at risk (VaR) is adopted to measure the risk level in the electricity market. To estimate VaR at higher accuracy and reliability, the wavelet variance decomposed approach for value at risk estimates (WVDVaR) is proposed. Empirical studies conduct in five Australian electricity markets, which evaluate the performances of both the proposed approach and the traditional ARMA-GARCH approach using the Kupiec backtesting procedure. Experimental results suggest that the proposed approach measures electricity ...

  4. Wavelet-Based Volume Visualization

    NARCIS (Netherlands)

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

    1999-01-01

    We consider multiresolution visualization of large volume data sets based on wavelets. Starting from a wavelet decomposition of the data, a low resolution image is computed; this approximation can be successively refined. The practical need for such a multiresolution approach is motivated. The mathe

  5. OFDM Scheme Based on Wavelet Packet Transform-OrientedGraded Multi-Service

    Institute of Scientific and Technical Information of China (English)

    赵慧; 侯春萍

    2003-01-01

    In this paper, a concept of image grading transmission is put forward to enhance data rate and to improve the usage of subcarriers in orthogonal frequency division multiplexing (OFDM). The idea originates from the wavelet packets representative of an image in which information is graded in terms of different priorities. The graded image facilitates more efficient use of adaptive subcarriers and bits allocation. The results of simulation in typical mobile environment prove that the output signal noise ratio (SNR) of the graded image excels that of the ungraded image by 1-2 dB under the same channel condition.

  6. Wavelet-Based Grid Generation

    Science.gov (United States)

    Jameson, Leland

    1996-01-01

    Wavelets can provide a basis set in which the basis functions are constructed by dilating and translating a fixed function known as the mother wavelet. The mother wavelet can be seen as a high pass filter in the frequency domain. The process of dilating and expanding this high-pass filter can be seen as altering the frequency range that is 'passed' or detected. The process of translation moves this high-pass filter throughout the domain, thereby providing a mechanism to detect the frequencies or scales of information at every location. This is exactly the type of information that is needed for effective grid generation. This paper provides motivation to use wavelets for grid generation in addition to providing the final product: source code for wavelet-based grid generation.

  7. 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.

  8. Numerical Algorithms Based on Biorthogonal Wavelets

    Science.gov (United States)

    Ponenti, Pj.; Liandrat, J.

    1996-01-01

    Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.

  9. Leather inspection based on wavelets

    OpenAIRE

    Sobral, João Luís Ferreira

    2005-01-01

    This paper presents a new methodology to detect leather defects, based on the wavelet transform. The methodology uses a bank of optimised filters, where each filter is tuned to one defect type. Filter shape and wavelet sub-band are selected based the maximisation of the ratio between features values on defect regions and on normal regions. The proposed methodology can detect defects even when small features variations are present, which are not detect by generic texture classification techniq...

  10. Multi-scale Kalman filters algorithm for GPS common-view observation data based on correlation structure of discrete wavelet coefficients

    Institute of Scientific and Technical Information of China (English)

    OU Xiaojuan; ZHOU Wei

    2007-01-01

    Global positioning system (GPS)common-view observation data were processed by using the multi-scale Kalman algorithm based on a correlative structure of the discrete wavelet coefficients.Suppose that the GPS common-view observation data has the 1/f fractal characteristic,the algorithm of wavelet transform was used to estimate the Hurst parameter H of GPS clock difference data.When 0<H<1,the 1/f fractal characteristic of the GPS clock difference data iS a Gaussian zero-mean and non-stationary stochastic process.Thus,the discrete wavelet coefficients can be discussed in the process of estimating multi-scale Kalman coefficients.Furthermore,the discrete clock difierence can be estimated.The single-channel and multi-channel common-view observation data were processed respectively.Comparisons were made between the results obtained and the Circular T data.Simulation results show that the algorithm discussed in this paper is both feasible and effective.

  11. From cardinal spline wavelet bases to highly coherent dictionaries

    International Nuclear Information System (INIS)

    Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation. (fast track communication)

  12. From cardinal spline wavelet bases to highly coherent dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    Andrle, Miroslav; Rebollo-Neira, Laura [Aston University, Birmingham B4 7ET (United Kingdom)

    2008-05-02

    Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation. (fast track communication)

  13. Discriminant analyses of stock prices by using multifractality of time series generated via multi-agent systems and interpolation based on wavelet transforms

    Science.gov (United States)

    Tokinaga, Shozo; Ikeda, Yoshikazu

    In investments, it is not easy to identify traders'behavior from stock prices, and agent systems may help us. This paper deals with discriminant analyses of stock prices using multifractality of time series generated via multi-agent systems and interpolation based on Wavelet Transforms. We assume five types of agents where a part of agents prefer forecast equations or production rules. Then, it is shown that the time series of artificial stock price reveals as a multifractal time series whose features are defined by the Hausedorff dimension D(h). As a result, we see the relationship between the reliability (reproducibility) of multifractality and D(h) under sufficient number of time series data. However, generally we need sufficient samples to estimate D(h), then we use interpolations of multifractal times series based on the Wavelet Transform.

  14. Novel Adaptive Beamforming Algorithm Based on Wavelet Packet Transform

    Institute of Scientific and Technical Information of China (English)

    Zhang Xiaofei; Xu Dazhuan

    2005-01-01

    An analysis of the received signal of array antennas shows that the received signal has multi-resolution characteristics, and hence the wavelet packet theory can be used to detect the signal. By emplying wavelet packet theory to adaptive beamforming, a wavelet packet transform-based adaptive beamforming algorithm (WP-ABF) is proposed . This WP-ABF algorithm uses wavelet packet transform as the preprocessing, and the wavelet packet transformed signal uses least mean square algorithm to implement the adaptive beamforming. White noise can be wiped off under wavelet packet transform according to the different characteristics of signal and white under the wavelet packet transform. Theoretical analysis and simulations demonstrate that the proposed WP-ABF algorithm converges faster than the conventional adaptive beamforming algorithm and the wavelet transform-based beamforming algorithm. Simulation results also reveal that the convergence of the algorithm relates closely to the wavelet base and series; that is, the algorithm convergence gets better with the increasing of series, and for the same series of wavelet base the convergence gets better with the increasing of regularity.

  15. AN ALGORITHM FOR CONSTRUCTING ORTHOGONAL ARMLET MULTI-WAVELETS WITH MULTIPLICITY r AND DILATION FACTOR a

    Institute of Scientific and Technical Information of China (English)

    Zhou Xiaohui; Wang Gang; Wang Baoqin

    2011-01-01

    The purpose of this paper is to construct an orthogonal Armlet multi-wavelets with multiplicity r and dilation factor a.Firstly,the definition of Armlets with dilation factor a is proposed in this paper.Based on the Two-scale Similar Transform (TST),the notion of the Para-unitary A-scale Similar Transform (PAST) is introduced,and we also give the transform on the all two-scale matrix symbols of the multi-wavelets with dilation a.Then we show that the PAST and the transform on the matrix symbols of the multi-wavelets keep the orthogonality of the multi-wavelets system.We discuss the condition that a- 1 multi-wavelets corresponding to the multi-scaling functions are all Armlets.After performing the PAST and the transform on the matrix symbols of the multi-wavelets,the multi-scaling function can be balanced and the corresponding multi-wavelets can be Armlets at the same time.The construction of Armlets with high order is also discussed.At last,by a given example,we can conclude that the algorithm is feasible and efficient.

  16. Wavelet-based Evapotranspiration Forecasts

    Science.gov (United States)

    Bachour, R.; Maslova, I.; Ticlavilca, A. M.; McKee, M.; Walker, W.

    2012-12-01

    Providing a reliable short-term forecast of evapotranspiration (ET) could be a valuable element for improving the efficiency of irrigation water delivery systems. In the last decade, wavelet transform has become a useful technique for analyzing the frequency domain of hydrological time series. This study shows how wavelet transform can be used to access statistical properties of evapotranspiration. The objective of the research reported here is to use wavelet-based techniques to forecast ET up to 16 days ahead, which corresponds to the LANDSAT 7 overpass cycle. The properties of the ET time series, both physical and statistical, are examined in the time and frequency domains. We use the information about the energy decomposition in the wavelet domain to extract meaningful components that are used as inputs for ET forecasting models. Seasonal autoregressive integrated moving average (SARIMA) and multivariate relevance vector machine (MVRVM) models are coupled with the wavelet-based multiresolution analysis (MRA) results and used to generate short-term ET forecasts. Accuracy of the models is estimated and model robustness is evaluated using the bootstrap approach.

  17. Comparison of Citrus Fruit Surface Defect Classification using Discrete Wavelet Transform, Stationary Wavelet Transform and Wavelet Packet Transform Based Features

    Directory of Open Access Journals (Sweden)

    K. Vijayarekha

    2012-12-01

    Full Text Available The aim of this study is to classify the citrus fruit images based on the external defect using the features extracted in the spectral domain (transform based and to compare the performance of each of the feature set. Automatic classification of agricultural produce by machine vision technology plays a very important role as it improves the quality of grading. Multi resolution analysis using wavelets yields better results for pattern recognition and object classification. This study details about an image processing method applied for classifying three external surface defects of citrus fruit using wavelet transforms based features and an artificial neural network. The Discrete Wavelet Transform (DWT, Stationary Wavelet Transform (SWT and Wavelet Packet Transform (WPT features viz. mean and standard deviation of the details and approximations were extracted from citrus fruit images and used for classifying the defects. The DWT and SWT features were extracted from 40x40 sub-windows of the fruit image. The WPT features were extracted from the full fruit image of size 640x480. The classification results pertaining to the three wavelet transforms are reported and discussed.

  18. MULTI-SPECTRAL AND HYPERSPECTRAL IMAGE FUSION USING 3-D WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Zhang Yifan; He Mingyi

    2007-01-01

    Image fusion is performed between one band of multi-spectral image and two bands of hyperspectral image to produce fused image with the same spatial resolution as source multi-spectral image and the same spectral resolution as source hyperspectral image. According to the characteristics and 3-Dimensional (3-D) feature analysis of multi-spectral and hyperspectral image data volume, the new fusion approach using 3-D wavelet based method is proposed. This approach is composed of four major procedures: Spatial and spectral resampling, 3-D wavelet transform, wavelet coefficient integration and 3-D inverse wavelet transform. Especially, a novel method, Ratio Image Based Spectral Resampling (RIBSR) method, is proposed to accomplish data resampling in spectral domain by utilizing the property of ratio image. And a new fusion rule, Average and Substitution (A&S) rule, is employed as the fusion rule to accomplish wavelet coefficient integration. Experimental results illustrate that the fusion approach using 3-D wavelet transform can utilize both spatial and spectral characteristics of source images more adequately and produce fused image with higher quality and fewer artifacts than fusion approach using 2-D wavelet transform. It is also revealed that RIBSR method is capable of interpolating the missing data more effectively and correctly, and A&S rule can integrate coefficients of source images in 3-D wavelet domain to preserve both spatial and spectral features of source images more properly.

  19. The Brera Multi-scale Wavelet (BMW) ROSAT HRI source catalog; 1, the algorithm

    CERN Document Server

    Lazzati, D; Rosati, P; Panzera, M R; Tagliaferri, G; Lazzati, Davide; Campana, Sergio; Rosati, Piero; Panzera, Maria Rosa; Tagliaferri, Gianpiero

    1999-01-01

    We present a new detection algorithm based on the wavelet transform for the analysis of high energy astronomical images. The wavelet transform, due to its multi-scale structure, is suited for the optimal detection of point-like as well as extended sources, regardless of any loss of resolution with the off-axis angle. Sources are detected as significant enhancements in the wavelet space, after the subtraction of the non-flat components of the background. Detection thresholds are computed through Monte Carlo simulations in order to establish the expected number of spurious sources per field. The source characterization is performed through a multi-source fitting in the wavelet space. The procedure is designed to correctly deal with very crowded fields, allowing for the simultaneous characterization of nearby sources. To obtain a fast and reliable estimate of the source parameters and related errors, we apply a novel decimation technique which, taking into account the correlation properties of the wavelet transf...

  20. Wavelet Analysis for Classification of Multi-source PD Patterns

    OpenAIRE

    Lalitha, EM; Satish, L.

    2000-01-01

    Multi-resolution signal decomposition (MSD) technique of wavelet transforms has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable farm. This feature was exploited to identify individual partial discharge (PD) sources present in multi-source PD Patterns, usually encountered during practical PD measurements, Employing the Daubechies wavelet, features were extracted from the third level decomposed and reconstructed horizo...

  1. Vibrator Data Denoising Based on Fractional Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Zheng Jing

    2015-06-01

    Full Text Available In this paper, a novel data denoising method is proposed for seismic exploration with a vibrator which produces a chirp-like signal. The method is based on fractional wavelet transform (FRWT, which is similar to the fractional Fourier transform (FRFT. It can represent signals in the fractional domain, and has the advantages of multi-resolution analysis as the wavelet transform (WT. The fractional wavelet transform can process the reflective chirp signal as pulse seismic signal and decompose it into multi-resolution domain to denoise. Compared with other methods, FRWT can offer wavelet transform for signal analysis in the timefractional- frequency plane which is suitable for processing vibratory seismic data. It can not only achieve better denoising performance, but also improve the quality and continuity of the reflection syncphase axis.

  2. Architecture design of the multi-functional wavelet-based ECG microprocessor for realtime detection of abnormal cardiac events.

    Science.gov (United States)

    Cheng, Li-Fang; Chen, Tung-Chien; Chen, Liang-Gee

    2012-01-01

    Most of the abnormal cardiac events such as myocardial ischemia, acute myocardial infarction (AMI) and fatal arrhythmia can be diagnosed through continuous electrocardiogram (ECG) analysis. According to recent clinical research, early detection and alarming of such cardiac events can reduce the time delay to the hospital, and the clinical outcomes of these individuals can be greatly improved. Therefore, it would be helpful if there is a long-term ECG monitoring system with the ability to identify abnormal cardiac events and provide realtime warning for the users. The combination of the wireless body area sensor network (BASN) and the on-sensor ECG processor is a possible solution for this application. In this paper, we aim to design and implement a digital signal processor that is suitable for continuous ECG monitoring and alarming based on the continuous wavelet transform (CWT) through the proposed architectures--using both programmable RISC processor and application specific integrated circuits (ASIC) for performance optimization. According to the implementation results, the power consumption of the proposed processor integrated with an ASIC for CWT computation is only 79.4 mW. Compared with the single-RISC processor, about 91.6% of the power reduction is achieved.

  3. Wavelet-based prediction of oil prices

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, Shahriar [Econometric Group, Department of Economics, University of Southern Denmark, DK-5230 Odense M (Denmark); Weinreich, Ilona [Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhr Campus, D-53424 Remagen (Germany)]. E-mail: weinreich@rheinahrcampus.de; Reinarz, Dominik [Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhr Campus, D-53424 Remagen (Germany)

    2005-07-01

    This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced.

  4. Multi-sensor image fusion using discrete wavelet frame transform

    Institute of Scientific and Technical Information of China (English)

    Zhenhua Li(李振华); Zhongliang Jing(敬忠良); Shaoyuan Sun(孙韶媛)

    2004-01-01

    An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT).The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transform based or discrete wavelet transform (DWT) based algorithms in multi-sensor image fusion.

  5. Wavelet Image Encryption Algorithm Based on AES

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Traditional encryption techniques have some limits for multimedia information, especially image and video, which are considered only to be common data. In this paper, we propose a wavelet-based image encryption algorithm based on the Advanced Encryption Standard, which encrypts only those low frequency coefficients of image wavelet decomposition. The experimental results are satisfactory.

  6. Multifidus Muscle Volume Estimation Based on Three Dimensional Wavelet Multi Resolution Analysis: MRA with Buttocks Computer-Tomography: CT Images

    OpenAIRE

    Kohei Arai

    2013-01-01

    Multi-Resolution Analysis:. MRA based edge detection algorithm is proposed for estimation of volume of multifidus muscle in the Computer Tomography: CT scanned image The volume of multifidus muscle would be a good measure for metabolic syndrome rather than internal fat from a point of view from processing complexity. The proposed measure shows 0.178 of R square which corresponds to mutual correlation between internal fat and the volume of multifidus muscle. It is also fund that R square betwe...

  7. Cycle-slip Detection of GPS Carrier Phase with Methodology of SA4 Multi-wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    HUO Guoping; MIAO Lingjuan

    2012-01-01

    That cycle-slips remain undetected will significantly degrade the accuracy of the navigation solution when using carrier phase measurements in global positioning system (GPS).In this paper,an algorithm based on length-4 symmetric/anti-symmetrc (SA4) orthogonal multi-wavelet is presented to detect and identify cycle-slips in the context of the feature of the GPS zero-differential carrier phase measurements.Associated with the local singularity detection principle,cycle-slips can be detected and located precisely through the modulus maxima of the coefficients achieved by the multi-wavelet transform.Firstly,studies are focused on the feasibility of the algorithm employing the orthogonal multi-wavelet system such as Geronimo-Hardin-Massopust (GHM),Chui-Lian (CL) and SA4.Moreover,the mathematical characterization of singularities with Lipschitz exponents is explained,the modulus maxima from wavelet to multi-wavelet domain is extended and a localization formula is provided from the modulus maxima of the coefficients to the original observation.Finally,field experiments with real receiver are presented to demonstrate the effectiveness of the proposed algorithm.Because SA4 possesses the specific nature of good multi-filter properties (GMPs),it is superior to scalar wavelet and other orthogonal multi-wavelet candidates distinctly,and for the half-cycle slip,it also remains better detection,location ability and the equal complexity of wavelet transform.

  8. Wavelet methods in multi-conjugate adaptive optics

    CERN Document Server

    Helin, Tapio

    2013-01-01

    The next generation ground-based telescopes rely heavily on adaptive optics for overcoming the limitation of atmospheric turbulence. In the future adaptive optics modalities, like multi-conjugate adaptive optics (MCAO), atmospheric tomography is the major mathematical and computational challenge. In this severely ill-posed problem a fast and stable reconstruction algorithm is needed that can take into account many real-life phenomena of telescope imaging. We introduce a novel reconstruction method for the atmospheric tomography problem and demonstrate its performance and flexibility in the context of MCAO. Our method is based on using locality properties of compactly supported wavelets, both in the spatial and frequency domain. The reconstruction in the atmospheric tomography problem is obtained by solving the Bayesian MAP estimator with a conjugate gradient based algorithm. An accelerated algorithm with preconditioning is also introduced. Numerical performance is demonstrated on the official end-to-end simul...

  9. Wavelet methods in multi-conjugate adaptive optics

    Science.gov (United States)

    Helin, T.; Yudytskiy, M.

    2013-08-01

    The next generation ground-based telescopes rely heavily on adaptive optics for overcoming the limitation of atmospheric turbulence. In the future adaptive optics modalities, like multi-conjugate adaptive optics (MCAO), atmospheric tomography is the major mathematical and computational challenge. In this severely ill-posed problem, a fast and stable reconstruction algorithm is needed that can take into account many real-life phenomena of telescope imaging. We introduce a novel reconstruction method for the atmospheric tomography problem and demonstrate its performance and flexibility in the context of MCAO. Our method is based on using locality properties of compactly supported wavelets, both in the spatial and frequency domains. The reconstruction in the atmospheric tomography problem is obtained by solving the Bayesian MAP estimator with a conjugate-gradient-based algorithm. An accelerated algorithm with preconditioning is also introduced. Numerical performance is demonstrated on the official end-to-end simulation tool OCTOPUS of European Southern Observatory.

  10. Multifidus Muscle Volume Estimation Based on Three Dimensional Wavelet Multi Resolution Analysis: MRA with Buttocks Computer-Tomography: CT Images

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-12-01

    Full Text Available Multi-Resolution Analysis:. MRA based edge detection algorithm is proposed for estimation of volume of multifidus muscle in the Computer Tomography: CT scanned image The volume of multifidus muscle would be a good measure for metabolic syndrome rather than internal fat from a point of view from processing complexity. The proposed measure shows 0.178 of R square which corresponds to mutual correlation between internal fat and the volume of multifidus muscle. It is also fund that R square between internal fat and the other possible measures shows smaller than that of multifidus muscle.

  11. Wavelet-based multispectral face recognition

    Institute of Scientific and Technical Information of China (English)

    LIU Dian-ting; ZHOU Xiao-dan; WANG Cheng-wen

    2008-01-01

    This paper proposes a novel wavelet-based face recognition method using thermal infrared (1R) and visible-light face images. The method applies the combination of Gabor and the Fisherfaces method to the reconstructed IR and visible images derived from wavelet frequency subbands. Our objective is to search for the subbands that are insensitive to the variation in expression and in illumination. The classification performance is improved by combining the multispectal information coming from the subbands that attain individually low equal error rate. Experimental results on Notre Dame face database show that the proposed wavelet-based algorithm outperforms previous multispectral images fusion method as well as monospectral method.

  12. Hydrologic regionalization using wavelet-based multiscale entropy method

    Science.gov (United States)

    Agarwal, A.; Maheswaran, R.; Sehgal, V.; Khosa, R.; Sivakumar, B.; Bernhofer, C.

    2016-07-01

    Catchment regionalization is an important step in estimating hydrologic parameters of ungaged basins. This paper proposes a multiscale entropy method using wavelet transform and k-means based hybrid approach for clustering of hydrologic catchments. Multi-resolution wavelet transform of a time series reveals structure, which is often obscured in streamflow records, by permitting gross and fine features of a signal to be separated. Wavelet-based Multiscale Entropy (WME) is a measure of randomness of the given time series at different timescales. In this study, streamflow records observed during 1951-2002 at 530 selected catchments throughout the United States are used to test the proposed regionalization framework. Further, based on the pattern of entropy across multiple scales, each cluster is given an entropy signature that provides an approximation of the entropy pattern of the streamflow data in each cluster. The tests for homogeneity reveals that the proposed approach works very well in regionalization.

  13. A wavelet-based method for the forced vibration analysis of piecewise linear single- and multi-DOF systems with application to cracked beam dynamics

    Science.gov (United States)

    Joglekar, D. M.; Mitra, M.

    2015-12-01

    The present investigation outlines a method based on the wavelet transform to analyze the vibration response of discrete piecewise linear oscillators, representative of beams with breathing cracks. The displacement and force variables in the governing differential equation are approximated using Daubechies compactly supported wavelets. An iterative scheme is developed to arrive at the optimum transform coefficients, which are back-transformed to obtain the time-domain response. A time-integration scheme, solving a linear complementarity problem at every time step, is devised to validate the proposed wavelet-based method. Applicability of the proposed solution technique is demonstrated by considering several test cases involving a cracked cantilever beam modeled as a bilinear SDOF system subjected to a harmonic excitation. In particular, the presence of higher-order harmonics, originating from the piecewise linear behavior, is confirmed in all the test cases. Parametric study involving the variations in the crack depth, and crack location is performed to bring out their effect on the relative strengths of higher-order harmonics. Versatility of the method is demonstrated by considering the cases such as mixed-frequency excitation and an MDOF oscillator with multiple bilinear springs. In addition to purporting the wavelet-based method as a viable alternative to analyze the response of piecewise linear oscillators, the proposed method can be easily extended to solve inverse problems unlike the other direct time integration schemes.

  14. Performance Analysis of Multi Spectral Band Image Compression using Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    S. S. Ramakrishnan

    2012-01-01

    Full Text Available Problem statement: Efficient and effective utilization of transmission bandwidth and storage capacity have been a core area of research for remote sensing images. Hence image compression is required for multi-band satellite imagery. In addition, image quality is also an important factor after compression and reconstruction. Approach: In this investigation, the discrete wavelet transform is used to compress the Landsat5 agriculture and forestry image using various wavelets and the spectral signature graph is drawn. Results: The compressed image performance is analyzed using Compression Ratio (CR, Peak Signal to Noise Ratio (PSNR. The compressed image using dmey wavelet is selected based on its Digital Number Minimum (DNmin and Digital Number Maximum (DNmax. Then it is classified using maximum likelihood classification and the accuracy is determined using error matrix, kappa statistics and over all accuracy. Conclusion: Hence the proposed compression technique is well suited to compress the agriculture and forestry multi-band image.

  15. Wavelet basis construction method based on separation blast vibration signal

    Institute of Scientific and Technical Information of China (English)

    凌同华; 张胜; 陈倩倩; 李洁

    2015-01-01

    As wavelet basis in wavelet analysis is neither arbitrary nor unique, the same signal dealing with different wavelet bases will generate different results. Therefore, how to construct a wavelet basis suitable for the characteristics of the analyzed signal and solve its algorithm and realization is a fundamental problem which perplexed many researchers. To solve these problems, in accordance with the basic features of the measured millisecond blast vibration signal, a new wavelet basis construction method based on the separation blast vibration signal is proposed, and the feasibility of this method is verified by comparing the practical effect of the newly constructed wavelet with other known wavelets in signal processing.

  16. Complex Wavelet Based Modulation Analysis

    DEFF Research Database (Denmark)

    Luneau, Jean-Marc; Lebrun, Jérôme; Jensen, Søren Holdt

    2008-01-01

     because only the magnitudes are taken into account and the phase data is often neglected. We remedy this problem with the use of a complex wavelet transform as a more appropriate envelope and phase processing tool. Complex wavelets carry both magnitude and phase explicitly with great sparsity and preserve well...... polynomial trends. Moreover an analytic Hilbert-like transform is possible with complex wavelets implemented as an orthogonal filter bank. By working in an alternative transform domain coined as “Modulation Subbands”, this transform shows very promising denoising capabilities and suggests new approaches for joint...

  17. Multiuser detector based on wavelet networks

    Institute of Scientific and Technical Information of China (English)

    王伶; 焦李成; 陶海红; 刘芳

    2004-01-01

    Multiple access interference (MAI) and near-far problem are two major obstacles in DS-CDMA systems.Combining wavelet neural networks and two matched filters, the novel multiuser detector, which is based on multiple variable function estimation wavelet networks over single path asynchronous channel and space-time channel respectively is presented. Excellent localization characteristics of wavelet functions in both time and frequency domains allowed hierarchical multiple resolution learning of input-output data mapping. The mathematic frame of the neural networks and error back ward propagation algorithm are introduced. The complexity of the multiuser detector only depends on that of wavelet networks. With numerical simulations and performance analysis, it indicates that the multiuser detector has excellent performance in eliminating MAI and near-far resistance.

  18. Wavelet-based acoustic recognition of aircraft

    Energy Technology Data Exchange (ETDEWEB)

    Dress, W.B.; Kercel, S.W.

    1994-09-01

    We describe a wavelet-based technique for identifying aircraft from acoustic emissions during take-off and landing. Tests show that the sensor can be a single, inexpensive hearing-aid microphone placed close to the ground the paper describes data collection, analysis by various technique, methods of event classification, and extraction of certain physical parameters from wavelet subspace projections. The primary goal of this paper is to show that wavelet analysis can be used as a divide-and-conquer first step in signal processing, providing both simplification and noise filtering. The idea is to project the original signal onto the orthogonal wavelet subspaces, both details and approximations. Subsequent analysis, such as system identification, nonlinear systems analysis, and feature extraction, is then carried out on the various signal subspaces.

  19. Electric Equipment Diagnosis based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Stavitsky Sergey A.

    2016-01-01

    Full Text Available Due to electric equipment development and complication it is necessary to have a precise and intense diagnosis. Nowadays there are two basic ways of diagnosis: analog signal processing and digital signal processing. The latter is more preferable. The basic ways of digital signal processing (Fourier transform and Fast Fourier transform include one of the modern methods based on wavelet transform. This research is dedicated to analyzing characteristic features and advantages of wavelet transform. This article shows the ways of using wavelet analysis and the process of test signal converting. In order to carry out this analysis, computer software Mathcad was used and 2D wavelet spectrum for a complex function was created.

  20. Wavelet based approach for facial expression recognition

    Directory of Open Access Journals (Sweden)

    Zaenal Abidin

    2015-03-01

    Full Text Available Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4 wavelet and Coiflet (1 wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database.

  1. NOVEL ADAPTIVE MULTIUSER DETECTIONALGORITHM BASED ON WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    ZHANGXiao-fei; XUDa-zhuan; YANGBei

    2004-01-01

    The wavelet transform-based adaptive multiuser detection algorithm is presented. The novel adaptive multiuser detection algorithm uses the wavelet transform for the preprocessing, and wavelet-transformed signal uses LMS algorithm to implement the adaptive multiuser detection. The algorithm makes use of wavelet transform to divide the wavelet space, which shows that the wavelet transform has a better decorrelation ability and leads to better convergence. White noise can be wiped off under the wavelet transform according to different characteristics of signal and white noise under the wavelet transform. Theoretical analyses and simulations demonstrate that the algorithm converges faster than the conventional adaptive multiuser detection algorithm, and has the better performance. Simulation results reveal that the algorithm convergence relates to the wavelet base, and show that the algorithm convergence gets better with the increasing of regularity for the same series of the wavelet base. Finally the algorithm shows that it can be easily implemented.

  2. Data Clustering Analysis Based on Wavelet Feature Extraction

    Institute of Scientific and Technical Information of China (English)

    QIANYuntao; TANGYuanyan

    2003-01-01

    A novel wavelet-based data clustering method is presented in this paper, which includes wavelet feature extraction and cluster growing algorithm. Wavelet transform can provide rich and diversified information for representing the global and local inherent structures of dataset. therefore, it is a very powerful tool for clustering feature extraction. As an unsupervised classification, the target of clustering analysis is dependent on the specific clustering criteria. Several criteria that should be con-sidered for general-purpose clustering algorithm are pro-posed. And the cluster growing algorithm is also con-structed to connect clustering criteria with wavelet fea-tures. Compared with other popular clustering methods,our clustering approach provides multi-resolution cluster-ing results,needs few prior parameters, correctly deals with irregularly shaped clusters, and is insensitive to noises and outliers. As this wavelet-based clustering method isaimed at solving two-dimensional data clustering prob-lem, for high-dimensional datasets, self-organizing mapand U-matrlx method are applied to transform them intotwo-dimensional Euclidean space, so that high-dimensional data clustering analysis,Results on some sim-ulated data and standard test data are reported to illus-trate the power of our method.

  3. Wavelet Packet based Multicarrier Modulation for Cognitive UWB Systems

    Directory of Open Access Journals (Sweden)

    Haleh Hosseini, Norsheila Fisal, & Sharifah K. Syed-Yusof

    2010-06-01

    Full Text Available Orthogonal frequency division multiplexing (OFDM is a multi-carrier modulation(MCM scheme where the sub carriers are orthogonal waves. The mainadvantages of OFDM are robustness against multi-path fading, frequencyselective fading, narrowband interference, and efficient use of spectrum.Recently it is proved that MCM system optimization can be achieved by applyingwavelet bases instead of conventional fourier bases. Wavelet packet based MCM(WPMCM systems have overall the same capabilities as OFDM systems withsome improved features. In this research the literature and analytic schemes ofWPMCM system is addressed, a wavelet packet based cognitive ultra wideband(UWB transceiver is proposed, and performance analysis of WPMCM in differentwireless multipath channels is investigated. Simulation results show a significantenhancement in terms of spectral efficiency, side-lobes suppression and BERcomparing to conventional OFDM.

  4. Multi-resolution Analysis of Multi-spectral Palmprints using Hybrid Wavelets for Identification

    Directory of Open Access Journals (Sweden)

    Dr. H.B. Kekre

    2013-04-01

    Full Text Available Palmprint is a relatively new physiological biometric used in identification systems due to its stable and unique characteristics. The vivid texture information of palmprint present at different resolutions offers abundant prospects in personal recognition. This paper describes a new method to authenticate individuals based on palmprint identification. In order to analyze the texture information at various resolutions, we introduce a new hybrid wavelet, which is generated using two or more component transforms incorporating both their properties. A unique property of this wavelet is its flexibility to vary the number of components at each level of resolution and hence can be made suitable for various applications. Multi-spectral palmprints have been identified using energy compaction of the hybrid wavelet transform coefficients. The scores generated for each set of palmprint images under red, green and blue illuminations are combined using score-level fusion using AND and OR operators. Comparatively low values of equal error rate and high security index have been obtained for all fusion techniques. The experimental results demonstrate the effectiveness and accuracy of the proposed method.

  5. Multi-Carrier Phase Coded Radar Signal Based on Wavelet Packet%一种基于小波包的多载波相位编码雷达信号

    Institute of Scientific and Technical Information of China (English)

    尹冰之; 李勇

    2012-01-01

    In order to enhance Multi — carrier Phase Coded ( MCPC) radar signal anti- interference ability and spectrum efficiency, based on conventional Fourier MCPC signal, an adaptive method using Wavelet Packet Transform was advanced. Wavelet Packet's orthogonality and band-limited ability obtain more flexible radar signal, improve signal' s time - frequency property and enhance anti - interference ability. The simulation indicates that the MCPC signal based on Wavelet Packet has better spectrum efficiency and ambiguity function to satisfy the design of wideband radar signal.%为了提高多载波相位编码(Multi-carrier Phase Coded,MCPC)雷达信号的抗干扰性与频谱利用率,在传统的基于傅里叶变换的MCPC的基础上,提出了一种优化方法.基于小波包变换来产生MCPC信号,利用小波包基函数的正交性和带限能力来获取更为灵活的雷达信号,从而改善信号的时频特性,提高抗干扰能力.仿真表明,小波包变换提高了 MCPC信号的频谱利用率并改善了模糊函数,适合宽带雷达信号的设计.

  6. Signal enhancement of a novel multi-address coding lidar backscatters based on a combined technique of demodulation and wavelet de-noising

    Science.gov (United States)

    Xu, Fan; Wang, Yuanqing

    2015-11-01

    Multi-address coding (MAC) lidar is a novel lidar system recently developed by our laboratory. By applying a new combined technique of multi-address encoding, multiplexing and decoding, range resolution is effectively improved. In data processing, a signal enhancement method involving laser signal demodulation and wavelet de-noising in the downlink is proposed to improve the signal to noise ratio (SNR) of raw signal and the capability of remote application. In this paper, the working mechanism of MAC lidar is introduced and the implementation of encoding and decoding is also illustrated. We focus on the signal enhancement method and provide the mathematical model and analysis of an algorithm on the basis of the combined method of demodulation and wavelet de-noising. The experimental results and analysis demonstrate that the signal enhancement approach improves the SNR of raw data. Overall, compared with conventional lidar system, MAC lidar achieves a higher resolution and better de-noising performance in long-range detection.

  7. Wavelet Based QRS Complex Detection of ECG Signal

    OpenAIRE

    Mukhopadhyay, Sayantan; Biswas, Shouvik; Roy, Anamitra Bardhan; Dey, Nilanjan

    2012-01-01

    The Electrocardiogram (ECG) is a sensitive diagnostic tool that is used to detect various cardiovascular diseases by measuring and recording the electrical activity of the heart in exquisite detail. A wide range of heart condition is determined by thorough examination of the features of the ECG report. Automatic extraction of time plane features is important for identification of vital cardiac diseases. This paper presents a multi-resolution wavelet transform based system for detection 'P', '...

  8. Wavelet based detection of manatee vocalizations

    Science.gov (United States)

    Gur, Berke M.; Niezrecki, Christopher

    2005-04-01

    The West Indian manatee (Trichechus manatus latirostris) has become endangered partly because of watercraft collisions in Florida's coastal waterways. Several boater warning systems, based upon manatee vocalizations, have been proposed to reduce the number of collisions. Three detection methods based on the Fourier transform (threshold, harmonic content and autocorrelation methods) were previously suggested and tested. In the last decade, the wavelet transform has emerged as an alternative to the Fourier transform and has been successfully applied in various fields of science and engineering including the acoustic detection of dolphin vocalizations. As of yet, no prior research has been conducted in analyzing manatee vocalizations using the wavelet transform. Within this study, the wavelet transform is used as an alternative to the Fourier transform in detecting manatee vocalizations. The wavelet coefficients are analyzed and tested against a specified criterion to determine the existence of a manatee call. The performance of the method presented is tested on the same data previously used in the prior studies, and the results are compared. Preliminary results indicate that using the wavelet transform as a signal processing technique to detect manatee vocalizations shows great promise.

  9. Embedded Object Detection with Radar Echo Data by Means of Wavelet Analysis of MRA: Multi-Resolution Analysis

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2011-09-01

    Full Text Available A method for embedded object detection with radar echo data by means of wavelet analysis of MRA: Multi-Resolution Analysis, in particular, three dimensional wavelet transformations is proposed. In order to improve embedded object detecting capability, not only one dimensional radar echo data but also three dimensional data are used. Through a comparison between one dimensional edge detection with Sobel operator and three dimensional wavelet transformation based edge detection, it is found that the proposed method is superior to the Sobel operator based method.

  10. 利用小波多尺度积实现裂纹缺陷的边缘检测%Edge detection of crack defect based on wavelet multi-scale multiplication.

    Institute of Scientific and Technical Information of China (English)

    贾超; 王耀坤; 邢晶晶

    2011-01-01

    A new image edge detection algorithm which is based on wavelet transform is concerned with doing multi-scale wavelet decomposition to the image with multi-scale of edge information and the modulus maximum value of wavelet transform, and then making a multiplication of consecutive scale wavelet coefficient to enhance edge and achieving the final image edge with double threshold to remove noise components. The result of test indicates that this algorithm solves problems of edge noise and the bad edge, and ensures the accuracy of edge continuation and positioning. The algorithm with double threshold is superior to a single threshold and can be used effectively in framing member detection.%提出了一种基于小波变换的图像边缘检测方法,即利用边缘信息的多尺度特性和小波变换模极大值对图像进行多尺度分解,将相邻尺度的小渡系数相乘增强边缘,再通过双阈值去噪的方法,得到最终的图像边缘.实验结果表明该方法很好地解决了噪声和坏边的问题,边缘连续的同时又保证了边缘定位的准确性,采用双阈值的算法明显优于采用单阈值,可以有效用于结构件的检测.

  11. Wavelet Based Image Authentication and Recovery

    Institute of Scientific and Technical Information of China (English)

    Rafiullah Chamlawi; Asifullah Khan; Adnan Idris

    2007-01-01

    In this paper, we propose a secure semi-fragile watermarking technique based on integer wavelet transform with a choice of two watermarks to be embedded. A self-recovering algorithm is employed, that hides the image digest into some wavelet subbands for detecting possible illicit object manipulation undergone in the image. The semi-fragility makes the scheme tolerant against JPEG lossy compression with the quality factor as low as 70%, and locates the tampered area accurately. In addition, the system ensures more security because the embedded watermarks are protected with private keys. The computational complexity is reduced by using parameterized integer wavelet transform. Experimental results show that the proposed scheme guarantees safety of a watermark, recovery of image and localization of tampered area.

  12. 基于小波分析的地貌多尺度表达与自动综合%Multi-scale Representation and Automatic Generalization of Relief Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    吴凡; 祝国瑞

    2001-01-01

    . The fundamental characteristics of multi-scale spatial datacan be detected and extracted,and represented by a set of wavelet coefficients, then handled andreconstructed, then the optimal representation of the spatial data sets can be got. This paper studies the multi-scale representation and automatic generalization of relief and the quantitative methodand criterion of investigating the extent of generalization based on the above idea. The paper formulates briefly the basic principle of multiresolution analysis (MRA) on wavelet transform atfirst, and describes a model for multi-scale handling of spatial data based on MRA of wavelet. Weknow that subspace at a higher resolution includes completely all information at a lower resolutionfrom the model, so multiple data sets such as Vi, V2,…, VJ may be derived from a basic set ofspatial data V0 at multiple scale by using MRA of wavelet, and the reverse procedure can be implemented completely by reconstructing. The decomposition and reconstruction are very stable.Accordingly, the model not only meets the need of automatic generalization but also is scale-dependent completely. Handling of automatic generalization is reverse based on the model.Two sections,approximation Ajf and detail Dejf, can be produced automatically by MRA of wavelet. The approximation describes the gentle and trend component of the characteristics of data, and the detaildescribes the fast and local one. They represent low and high frequency of data respectively. Whendata sets at scale j are derived from scale j + 1 , the loss of the approximation is Wj because Vj + 1 = Vj Wj and Vj Vj+ 1, described by Deejf. Therefore,{Dej f} represents the detail generalized at stepped down scale. DEM is an abstract model about relief in GIS. The key problem of multi-scale representation of relief is how to derive the DEM at multiple scales. We propose a schemefor a multi-scale representation and generalization of scale-dependent relief based on the abovemodel

  13. Wavelet-Based Denoising Attack on Image Watermarking

    Institute of Scientific and Technical Information of China (English)

    XUAN Jian-hui; WANG Li-na; ZHANG Huan-guo

    2005-01-01

    In this paper, we propose wavelet-based denoising attack methods on image watermarking in discrete cosine transform (DCT) or discrete Fourier transform (DFT) domain or discrete wavelet transform (DWT) domain. Wiener filtering based on wavelet transform is performed in approximation subband to remove DCT or DFT domain watermark,and adaptive wavelet soft thresholding is employed to remove the watermark resided in detail subbands of DWT domain.

  14. OPTIMAL LEVEL OF DECOMPOSITION OF STATIONARY WAVELET TRANSFORM FOR REGION LEVEL FUSION OF MULTI-FOCUSED IMAGES

    Directory of Open Access Journals (Sweden)

    K. Kannan

    2010-11-01

    Full Text Available In machine vision, due to the limited depth-of-focus of optical lenses in CCD devices, it is not possible to have a single image that contains all the information of objects in the image. To achieve this, image fusion is required which is usually refers to the process of combining two or more different images, each containing different features into a new single image retaining important features from each and every image with extended information content. The approaches to image fusion can be classified into two namely Spatial Fusion and Transform fusion. The most commonly used transform for image fusion at multi scale is Discrete Wavelet Transform since it minimizes structural distortions. But, wavelet transform suffers from lack of shift invariance and this disadvantage is overcome by Stationary Wavelet Transform. This paper describes the optimum level of decomposition of Stationary Wavelet Transform for region based fusion of multi focused images in terms of various performance measures.

  15. Wavelet Based Image Fusion for Detection of Brain Tumor

    Directory of Open Access Journals (Sweden)

    CYN Dwith

    2013-01-01

    Full Text Available Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR images and Computed Tomography (CT images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.

  16. Wavelet Variance Analysis of EEG Based on Window Function

    Institute of Scientific and Technical Information of China (English)

    ZHENG Yuan-zhuang; YOU Rong-yi

    2014-01-01

    A new wavelet variance analysis method based on window function is proposed to investigate the dynamical features of electroencephalogram (EEG).The ex-prienmental results show that the wavelet energy of epileptic EEGs are more discrete than normal EEGs, and the variation of wavelet variance is different between epileptic and normal EEGs with the increase of time-window width. Furthermore, it is found that the wavelet subband entropy (WSE) of the epileptic EEGs are lower than the normal EEGs.

  17. Singularity Detection of Signals Based on their Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    This paper introduces a multiresolution decomposition of signals based on their wavelet transform. The different behaviors of the wavelet transform between the signal and the noise are compared. An algorithm of singularity detection and processing in signals is proposed by the modulus maximum of the wavelet transform.

  18. Wavelet-based multifractal analysis of laser biopsy imagery

    CERN Document Server

    Jagtap, Jaidip; Panigrahi, Prasanta K; Pradhan, Asima

    2011-01-01

    In this work, we report a wavelet based multi-fractal study of images of dysplastic and neoplastic HE- stained human cervical tissues captured in the transmission mode when illuminated by a laser light (He-Ne 632.8nm laser). It is well known that the morphological changes occurring during the progression of diseases like cancer manifest in their optical properties which can be probed for differentiating the various stages of cancer. Here, we use the multi-resolution properties of the wavelet transform to analyze the optical changes. For this, we have used a novel laser imagery technique which provides us with a composite image of the absorption by the different cellular organelles. As the disease progresses, due to the growth of new cells, the ratio of the organelle to cellular volume changes manifesting in the laser imagery of such tissues. In order to develop a metric that can quantify the changes in such systems, we make use of the wavelet-based fluctuation analysis. The changing self- similarity during di...

  19. WAVELET-BASED FINE GRANULARITY SCALABLE VIDEO CODING

    Institute of Scientific and Technical Information of China (English)

    Zhang Jiangshan; Zhu Guangxi

    2003-01-01

    This letter proposes an efficient wavelet-based Fine Granularity Scalable (FGS)coding scheme, where the base layer is encoded with a newly designed wavelet-based coder, and the enhancement layer is encoded with Progressive Fine Granularity Scalable (PFGS) coding.This algorithm involves multi-frame motion compensation, rate-distortion optimizing strategy with Lagrangian cost function and context-based adaptive arithmetic coding. In order to improve efficiency of the enhancement layer coding, an improved motion estimation scheme that uses both information from the base layer and the enhancement layer is also proposed in this letter. The wavelet-based coder significantly improves the coding efficiency of the base layer compared with MPEG-4 ASP (Advanced Simple Profile) and H.26L TML9. The PFGS coding is a significant improvement over MPEG-4 FGS coding at the enhancement layer. Experiments show that single layer coding efficiency gain of the proposed scheme is about 2.0-3.0dB and 0.3-1.0dB higher than that of MPEG-4 ASP and H.26L TML9, respectively. The overall coding efficiency gain of the proposed scheme is about 4.0-5.0dB higher than that of MPEG-4 FGS.

  20. Bayesian-based Wavelet Shrinkage for SAR Image Despeckling Using Cycle Spinning

    Institute of Scientific and Technical Information of China (English)

    ZHANG De-xiang; GAO Qing-wei; CHEN Jun-ning

    2006-01-01

    A novel and efficient speckle noise reduction algorithm based on Bayesian wavelet shrinkage using cycle spinning is proposed. First, the sub-band decompositions of non-logarithmically transformed SAR images are shown. Then, a Bayesian wavelet shrinkage factor is applied to the decomposed data to estimate noise-free wavelet coefficients. The method is based on the Mixture Gaussian Distributed (MGD) modeling of sub-band coefficients. Finally, multi-resolution wavelet coefficients are reconstructed by wavelet-threshold using cycle spinning. Experimental results show that the proposed despeckling algorithm is possible to achieve an excellent balance between suppresses speckle effectively and preserves as many image details and sharpness as possible. The new method indicated its higher performance than the other speckle noise reduction techniques and minimizing the effect of pseudo-Gibbs phenomena.

  1. WAVELET-BASED OFDM-CDMA HIGH SPEED POWER LINE COMMUNICATION SYSTEMS

    Institute of Scientific and Technical Information of China (English)

    Zhou Lerong; Guo Jinghong; Wei Gang

    2004-01-01

    This letter derives the Equivalent M-band Discrete Wavelet(EMDW) transmission mode of Orthogonal Frequency Division Multiplexing(OFDM) transmission systems, and presents a new Quadrature M-band Discrete Wavelet(QMDW) based OFDM-CDMA(Code Division Multiple Access) communication systems for high speed Power Line Communication (PLC) channels.This system gives much better robustness to Inter-Channel Interference (ICI), Multi-User Interference (MUI) and noise interference, which is verified by simulation.

  2. Gestures recognition based on wavelet and LLE

    International Nuclear Information System (INIS)

    Wavelet analysis is a time–frequency, non-stationary method while the largest Lyapunov exponent (LLE) is used to judge the non-linear characteristic of systems. Because surface electromyography signal (SEMGS) is a complex signal that is characterized by non-stationary and non-linear properties. This paper combines wavelet coefficient and LLE together as the new feature of SEMGS. The proposed method not only reflects the non-stationary and non-linear characteristics of SEMGS, but also is suitable for its classification. Then, the BP (back propagation) neural network is employed to implement the identification of six gestures (fist clench, fist extension, wrist extension, wrist flexion, radial deviation, ulnar deviation). The experimental results indicate that based on the proposed method, the identification of these six gestures can reach an average rate of 97.71 %.

  3. Texture Classification based on Gabor Wavelet

    Directory of Open Access Journals (Sweden)

    Amandeep Kaur

    2012-07-01

    Full Text Available This paper presents the comparison of Texture classification algorithms based on Gabor Wavelets. The focus of this paper is on feature extraction scheme for texture classification. The texture feature for an image can be classified using texture descriptors. In this paper we have used Homogeneous texture descriptor that uses Gabor Wavelets concept. For texture classification, we have used online texture database that is Brodatz’s database and three advanced well known classifiers: Support Vector Machine, K-nearest neighbor method and decision tree induction method. The results shows that classification using Support vector machines gives better results as compare to the other classifiers. It can accurately discriminate between a testing image data and training data.

  4. Compression and Multi-Resolution Rendering of Sparse Voxels Based on Wavelet%基于小波的稀疏体素数据压缩与多分辨实时绘制

    Institute of Scientific and Technical Information of China (English)

    薛俊杰; 赵罡; 肖文磊

    2016-01-01

    为减少多分辨稀疏体素的存储空间并提高其绘制效率,提出一种基于小波的稀疏体素数据压缩与实时绘制算法。在稀疏体素生成阶段,基于小波的多分辨和稀疏体素的稀疏特性,利用多级三维 Haar 小波变换将高分辨率的稀疏体素转换为低分辨稀疏体素和多级细节信息,并采用紧凑的编码方式对小波系数进行编码,实现对多层级稀疏体素的数据压缩;在交互绘制阶段,结合稀疏体素八叉树光线投射算法,以低分辨体素节点为交互过程中的着色计算图元,交互过程终止后通过三维 Harr 小波逆变换逐级添加细节信息还原得到高分辨体素,进而实现多分辨绘制;最后充分利用多核 CPU 并行加速多分辨光线投射算法。对不同复杂度的面片模型进行压缩与绘制,实例计算表明,该算法高效且易于实现。%To reduce the storage size and improve the rendering efficiency of multi-resolution sparse voxels, a wavelet based compression and rendering algorithm is proposed. At the building stage of sparse voxels, according to the multi-resolution characteristic of wavelet and the sparsity of voxel structure, high-resolution sparse voxels were transformed into low-resolution sparse voxels and multi-level detail information by employing 3D Haar wavelet transform, and the wavelet coefficients were encoded with a compact encoding method. At the interactive rendering phase, in order to implement multi-resolution rendering, the low-resolution voxels were selected as shading primitives during the interaction. After the interaction process, the details were added to the coarse-grained voxels level by level through the inverse transform of 3D Haar wavelet to restore high-resolution voxels. Lastly, the rendering algorithm was accelerated in parallel by utilizing multi-core CPU. The experimental results show that the proposed algorithm provides an efficient and achievable way to

  5. Application of CL multi-wavelet transform and DCT in Information Hiding Algorithm

    Directory of Open Access Journals (Sweden)

    Tao ZHANG

    2011-02-01

    Full Text Available Taking advantage of a feature that allows theenergy of an image would gather and spread on four components (LL2, LH2, HL2 and HH2 in the sub image after first-order CL multi-wavelet transform, and Using the advantage of Discrete Cosine Transform in application of information hiding, propose an Information Hiding scheme based on CL multi-wavelet transform and Discrete Cosine Transform (abbreviated as CL-DCT. LL2 is embedded module of robust parameters (optimized code of Chebyshev scrambling and Hash value of embedding information. Embed hiding Information in LH2 and HL2 with RAID1 and fragile sign in HH2. Select a different range of DCT coefficients in LH2, HL2 and HH2. The embedding sequence of each bit plane is traversal according to Knight-tour rout. Experimental results indicate that the proposed scheme can increase invisibility and robustness separately by 5.24% and 28.33% averagely. In particular, the scheme has better ability against cutting attacks. The scheme has certain ability against steganalysis such as Higher Order Statistics based on wavelet coefficients. Moreover, the scheme has excellent sensitivity of image processing.

  6. MR IMAGE COMPRESSION BASED ON SELECTION OF MOTHER WAVELET AND LIFTING BASED WAVELET

    Directory of Open Access Journals (Sweden)

    Sheikh Md. Rabiul Islam

    2014-04-01

    Full Text Available Magnetic Resonance (MR image is a medical image technique required enormous data to be stored and transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the performance of the compression scheme. In this paper we extended the commonly used algorithms to image compression and compared its performance. For an image compression technique, we have linked different wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7 wavelet transform with Set Partition in Hierarchical Trees (SPIHT algorithm. A novel image quality index with highlighting shape of histogram of the image targeted is introduced to assess image compression quality. The index will be used in place of existing traditional Universal Image Quality Index (UIQI “in one go”. It offers extra information about the distortion between an original image and a compressed image in comparisons with UIQI. The proposed index is designed based on modelling image compression as combinations of four major factors: loss of correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate and applicable in various image processing applications. One of our contributions is to demonstrate the choice of mother wavelet is very important for achieving superior wavelet compression performances based on proposed image quality indexes. Experimental results show that the proposed image quality index plays a significantly role in the quality evaluation of image compression on the open sources “BrainWeb: Simulated Brain Database (SBD ”.

  7. MR Image Compression Based on Selection of Mother Wavelet and Lifting Based Wavelet

    Directory of Open Access Journals (Sweden)

    Sheikh Md. Rabiul Islam

    2014-04-01

    Full Text Available Magnetic Resonance (MR image is a medical image technique required enormous data to be stored and transmitted for high quality diagnostic application. Various algorithms have been proposed to improve the performance of the compression scheme. In this paper we extended the commonly used algorithms to image compression and compared its performance. For an image compression technique, we have linked different wavelet techniques using traditional mother wavelets and lifting based Cohen-Daubechies-Feauveau wavelets with the low-pass filters of the length 9 and 7 (CDF 9/7 wavelet transform with Set Partition in Hierarchical Trees (SPIHT algorithm. A novel image quality index with highlighting shape of histogram of the image targeted is introduced to assess image compression quality. The index will be used in place of existing traditional Universal Image Quality Index (UIQI “in one go”. It offers extra information about the distortion between an original image and a compressed image in comparisons with UIQI. The proposed index is designed based on modelling image compression as combinations of four major factors: loss of correlation, luminance distortion, contrast distortion and shape distortion. This index is easy to calculate and applicable in various image processing applications. One of our contributions is to demonstrate the choice of mother wavelet is very important for achieving superior wavelet compression performances based on proposed image quality indexes. Experimental results show that the proposed image quality index plays a significantly role in the quality evaluation of image compression on the open sources “BrainWeb: Simulated Brain Database (SBD ”.

  8. A New Text Location Approach Based Wavelet

    Institute of Scientific and Technical Information of China (English)

    Weihua Li; Zhen Fang; Shuozhong Wang

    2002-01-01

    With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regions in the input image will facilitate the retrieving task, and the optical character recognizer can then be applied to only those regions of the image which contain text. In this paper a new text location method based wavelet is described, which can be used to locate textual regions from complex image and video frame. Experimental results show that the textual regions in image can be located effectively and quickly.

  9. The Noval Properties and Construction of Multi-scale Matrix-valued Bivariate Wavelet wraps

    Science.gov (United States)

    Zhang, Hai-mo

    In this paper, we introduce matrix-valued multi-resolution structure and matrix-valued bivariate wavelet wraps. A constructive method of semi-orthogonal matrix-valued bivari-ate wavelet wraps is presented. Their properties have been characterized by using time-frequency analysis method, unitary extension principle and operator theory. The direct decom-position relation is obtained.

  10. Crack Detection of Simply Supported Beam Based on Multi-resolution Analysis of Wavelet%基于小波多分辨率分析的简支梁损伤识别方法研究

    Institute of Scientific and Technical Information of China (English)

    孙海宁

    2012-01-01

    基于小波多分辨率分析的方法,根据移动载荷作用下简支梁动态响应特性,利用梁上一点振动信号对裂纹梁进行损伤识别。移动载荷经过梁上裂纹处时,必然导致梁响应时间历程曲线的奇异性,这种奇异性不能直接被观察到,但是可以通过小波多分辨率分析进行识别。利用有限元Ansys软件的瞬态分析方法进行裂纹梁移动载荷作用下的损伤识别数值模拟,通过梁上某一点挠度、速度和加速度的振动信号,利用小波多分辨率分析有效地识别出单个及多个裂纹,同时扩展到多个同向和相向移动载荷作用下裂纹梁的损伤识别。%The article shows the potential of the crack detection method based on multi-resolution analysis of wavelet, which is depending on the response at a single point on a beam-like structure subject to concentrated moving loads. When moving along the structure , the moving loads cause small distortions in the dynamic response of the beam-like structure at the crack locations .General speaking, these small distortions are difficult to detect visually. An ANSYS model of a cracked beam is established. The moving load is transient analyzed by shifting the point of the concentrated force. The response at mid-span of the beam-like structure is calculated and wavelet multi-resolution transformed. The crack on the beam-like structure can be found by position of peak of wavelet multi-resolution analysis, also the velocity and acceleration of the moving load. Furthermore, by the principle of superposition, the solution obtained for a single moving load will be expanded to deal with a series of identical , equi-distant moving loads and opposite direction moving loads, by which the key parameter dominating the dynamic response of the beam-like structure can be identified.

  11. Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV

    Science.gov (United States)

    Mortezanejad, R.; Gholami, A.

    2016-06-01

    One of the most crucial challenges in seismic data processing is the reduction of noise in the data or improving the signal-to-noise ratio (SNR). Wavelet- and curvelet-based denoising algorithms have become popular to address random noise attenuation for seismic sections. Wavelet basis, thresholding function, and threshold value are three key factors of such algorithms, having a profound effect on the quality of the denoised section. Therefore, given a signal, it is necessary to optimize the denoising operator over these factors to achieve the best performance. In this paper a general denoising algorithm is developed as a multi-variant (variable) filter which performs in multi-scale transform domains (e.g. wavelet and curvelet). In the wavelet domain this general filter is a function of the type of wavelet, characterized by its smoothness, thresholding rule, and threshold value, while in the curvelet domain it is only a function of thresholding rule and threshold value. Also, two methods, Stein’s unbiased risk estimate (SURE) and generalized cross validation (GCV), evaluated using a Monte Carlo technique, are utilized to optimize the algorithm in both wavelet and curvelet domains for a given seismic signal. The best wavelet function is selected from a family of fractional B-spline wavelets. The optimum thresholding rule is selected from general thresholding functions which contain the most well known thresholding functions, and the threshold value is chosen from a set of possible values. The results obtained from numerical tests show high performance of the proposed method in both wavelet and curvelet domains in comparison to conventional methods when denoising seismic data.

  12. Texture Image Classification Based on Gabor Wavelet

    Institute of Scientific and Technical Information of China (English)

    DENG Wei-bing; LI Hai-fei; SHI Ya-li; YANG Xiao-hui

    2014-01-01

    For a texture image, by recognizining the class of every pixel of the image, it can be partitioned into disjoint regions of uniform texture. This paper proposed a texture image classification algorithm based on Gabor wavelet. In this algorithm, characteristic of every image is obtained through every pixel and its neighborhood of this image. And this algorithm can achieve the information transform between different sizes of neighborhood. Experiments on standard Brodatz texture image dataset show that our proposed algorithm can achieve good classification rates.

  13. Wavelet-Based Quantum Field Theory

    Directory of Open Access Journals (Sweden)

    Mikhail V. Altaisky

    2007-11-01

    Full Text Available The Euclidean quantum field theory for the fields $phi_{Delta x}(x$, which depend on both the position $x$ and the resolution $Delta x$, constructed in SIGMA 2 (2006, 046, on the base of the continuous wavelet transform, is considered. The Feynman diagrams in such a theory become finite under the assumption there should be no scales in internal lines smaller than the minimal of scales of external lines. This regularisation agrees with the existing calculations of radiative corrections to the electron magnetic moment. The transition from the newly constructed theory to a standard Euclidean field theory is achieved by integration over the scale arguments.

  14. Wavelet-based compression of medical images: filter-bank selection and evaluation.

    Science.gov (United States)

    Saffor, A; bin Ramli, A R; Ng, K H

    2003-06-01

    Wavelet-based image coding algorithms (lossy and lossless) use a fixed perfect reconstruction filter-bank built into the algorithm for coding and decoding of images. However, no systematic study has been performed to evaluate the coding performance of wavelet filters on medical images. We evaluated the best types of filters suitable for medical images in providing low bit rate and low computational complexity. In this study a variety of wavelet filters are used to compress and decompress computed tomography (CT) brain and abdomen images. We applied two-dimensional wavelet decomposition, quantization and reconstruction using several families of filter banks to a set of CT images. Discreet Wavelet Transform (DWT), which provides efficient framework of multi-resolution frequency was used. Compression was accomplished by applying threshold values to the wavelet coefficients. The statistical indices such as mean square error (MSE), maximum absolute error (MAE) and peak signal-to-noise ratio (PSNR) were used to quantify the effect of wavelet compression of selected images. The code was written using the wavelet and image processing toolbox of the MATLAB (version 6.1). This results show that no specific wavelet filter performs uniformly better than others except for the case of Daubechies and bi-orthogonal filters which are the best among all. MAE values achieved by these filters were 5 x 10(-14) to 12 x 10(-14) for both CT brain and abdomen images at different decomposition levels. This indicated that using these filters a very small error (approximately 7 x 10(-14)) can be achieved between original and the filtered image. The PSNR values obtained were higher for the brain than the abdomen images. For both the lossy and lossless compression, the 'most appropriate' wavelet filter should be chosen adaptively depending on the statistical properties of the image being coded to achieve higher compression ratio. PMID:12956184

  15. Region-Based Fractional Wavelet Transform Using Post Processing Artifact Reduction

    Directory of Open Access Journals (Sweden)

    Jassim M. Abdul-Jabbar

    2010-06-01

    Full Text Available Wavelet-based algorithms are increasingly used in the source coding of remote sensing, satellite and other geospatial imagery. At the same time, wavelet-based coding applications are also increased in robust communication and network transmission of images. Although wireless multimedia sensors are widely used to deliver multimedia content due to the availability of inexpensive CMOS cameras, their computational and memory resources are still typically very limited. It is known that allowing a low-cost camera sensor node with limited RAM size to perform a multi-level wavelet transform, will in return limit the size of the acquired image. Recently, fractional wavelet filter technique became an interesting solution to reduce communication energy and wireless bandwidth, for resource-constrained devices (e.g. digital cameras. The reduction in the required memory in these fractional wavelet transforms is achieved at the expense of the image quality. In this paper, an adaptive fractional artifacts reduction approach is proposed for efficient filtering operations according to the desired compromise between the effectiveness of artifact reduction and algorithm simplicity using some local image features to reduce boundaries artifacts caused by fractional wavelet. Applying such technique on different types of images with different sizes using CDF 9/7 wavelet filters results in a good performance.

  16. Wavelet-based verification of the quantitative precipitation forecast

    Science.gov (United States)

    Yano, Jun-Ichi; Jakubiak, Bogumil

    2016-06-01

    This paper explores the use of wavelets for spatial verification of quantitative precipitation forecasts (QPF), and especially the capacity of wavelets to provide both localization and scale information. Two 24-h forecast experiments using the two versions of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) on 22 August 2010 over Poland are used to illustrate the method. Strong spatial localizations and associated intermittency of the precipitation field make verification of QPF difficult using standard statistical methods. The wavelet becomes an attractive alternative, because it is specifically designed to extract spatially localized features. The wavelet modes are characterized by the two indices for the scale and the localization. Thus, these indices can simply be employed for characterizing the performance of QPF in scale and localization without any further elaboration or tunable parameters. Furthermore, spatially-localized features can be extracted in wavelet space in a relatively straightforward manner with only a weak dependence on a threshold. Such a feature may be considered an advantage of the wavelet-based method over more conventional "object" oriented verification methods, as the latter tend to represent strong threshold sensitivities. The present paper also points out limits of the so-called "scale separation" methods based on wavelets. Our study demonstrates how these wavelet-based QPF verifications can be performed straightforwardly. Possibilities for further developments of the wavelet-based methods, especially towards a goal of identifying a weak physical process contributing to forecast error, are also pointed out.

  17. Wavelet and ANN Based Relaying for Power Transformer Protection

    OpenAIRE

    Sudha, S.; A. E. Jeyakumar

    2007-01-01

    This paper presents an efficient wavelet and neural network (WNN) based algorithm for distinguishing magnetizing inrush currents from internal fault currents in three phase power transformers. The wavelet transform is applied first to decompose the current signals of the power transformer into a series of detailed wavelet components. The values of the detailed coefficients obtained can accurately discriminate between an internal fault and magnetizing inrush currents in power transformers. The...

  18. MOVING TARGETS PATTERN RECOGNITION BASED ON THE WAVELET NEURAL NETWORK

    Institute of Scientific and Technical Information of China (English)

    Ge Guangying; Chen Lili; Xu Jianjian

    2005-01-01

    Based on pattern recognition theory and neural network technology, moving objects automatic detection and classification method integrating advanced wavelet analysis are discussed in detail. An algorithm of moving targets pattern recognition on the combination of inter-frame difference and wavelet neural network is presented. The experimental results indicate that the designed BP wavelet network using this algorithm can recognize and classify moving targets rapidly and effectively.

  19. 单幅图像多尺度小波深度提取算法%Depth Extraction Algorithm for Single Image Based on Multi-Scale Wavelet

    Institute of Scientific and Technical Information of China (English)

    陈一民; 姚杰

    2014-01-01

    Aiming at solving the problem of reducing the depth extraction error of smooth foreground in defocus image ,this work propose an algorithm to generate the depth map with a single 2D image based on multi‐scale wavelet ,which can do depth correction by pixel classification techniques and is suitable for both defocus and wide angle images .Firstly ,a wavelet analysis method is used to extract depth maps from a single image at multiple scales . Secondly , an adaptive pixel classification method is proposed to do depth correction pixel by pixel according to the variation between scale and depth . Thirdly ,the depth map is optimized regionally using region growing integrate with edge segmentation techniques .In order to accelerate the depth calculation ,a fast zerocount method and a multi‐scale segment method are presented , w hich can meet the requirements of real‐time video processing . Experiments demonstrate that the depth maps generated by our algorithm are not only visually correct but also regionally consistent in both foreground and background .%针对浅景深图像中平滑前景区域深度提取误差大的问题,基于像素点分类思想对深度值进行修正,提出一种基于多尺度小波线索的、可同时面向单幅浅景深图像和广角图像的深度图提取算法。首先使用小波分析法在多个尺度下提取图像深度信息;然后提出自适应分类法并根据尺度与深度变化规律对像素点做深度修正,得到深度图;最后结合区域生长与边缘分割算法对深度图进行区域优化。为了加快深度计算,还提出了快速zerocount法以及多尺度加速法来满足标清视频实时处理要求。实验结果证明,采用文中算法获得的深度图相对深度正确,前景和背景区域深度一致性好。

  20. Based on the Wavelet Function of Power Network Fault Location

    Directory of Open Access Journals (Sweden)

    Fan YU

    2013-04-01

    Full Text Available In order to improve the measurement accuracy, in the traditional measuring method based on, by avoiding wave speed influence on fault location of transmission line method, and compares it with the combination of wavelet transform. This article selects dBN wavelet and three B spline wavelet contrast, compared them with new methods, through the Xi'an City Power Supply Bureau of the actual fault data validation. The results show that, with3 B spline wavelet and the new method combined with the location results are closer to the actual distance, its accuracy is higher than that of db3wavelet transform and a new method derived from the results, the error is far less than the db3 wavelet function, location is satisfactory.

  1. Infrared Image Small Target Detection Based on Bi-orthogonal Wavelet and Morphology

    Institute of Scientific and Technical Information of China (English)

    CHI Jian-nan; ZHANG Zhao-hui; WANG Dong-shu; HAO Yan-shuang

    2007-01-01

    An image multi-scale edge detection method based on anti-symmetrical bi-orthogonal wavelet is given in theory. Convolution operation property and function as a differential operator are analyzed,which anti-symmetrical bi-orthogonal wavelet transform have. An algorithm for wavelet reconstruction in which multi-scale edge can be detected is put forward. Based on it, a detection method for small target in infrared image with sea or sky background based on the anti-symmetrical bi-orthogonal wavelet and morphology is proposed. The small target detection is considered as a process in which structural background is removed, correlative background is suppressed, and noise is restrained. In this approach, the multi-scale edge is extracted by means of the anti-symmetrical bi-orthogonal wavelet decomposition. Then, module maximum chains formed by complicated background of clouds, sea wave and sea-sky-line are removed, and the image background becomes smoother. Finally, the morphology based edge detection method is used to get small target and restrain undulate background and noise. Experiment results show that the approach can suppress clutter background and detect the small target effectively.

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

    Institute of Scientific and Technical Information of China (English)

    金剑秋; 戴敏雅; 鲍虎军; 彭群生

    2004-01-01

    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.

  3. Abnormal traffic flow data detection based on wavelet analysis

    Directory of Open Access Journals (Sweden)

    Xiao Qian

    2016-01-01

    Full Text Available In view of the traffic flow data of non-stationary, the abnormal data detection is difficult.proposed basing on the wavelet analysis and least squares method of abnormal traffic flow data detection in this paper.First using wavelet analysis to make the traffic flow data of high frequency and low frequency component and separation, and then, combined with least square method to find abnormal points in the reconstructed signal data.Wavelet analysis and least square method, the simulation results show that using wavelet analysis of abnormal traffic flow data detection, effectively reduce the detection results of misjudgment rate and false negative rate.

  4. A Comparative Study of Human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP)

    OpenAIRE

    Seal, Ayan; Ganguly, Suranjan; Bhattacharjee, Debotosh; Nasipuri, Mita; Basu, Dipak Kumar

    2013-01-01

    Thermal infra-red (IR) images focus on changes of temperature distribution on facial muscles and blood vessels. These temperature changes can be regarded as texture features of images. A comparative study of face recognition methods working in thermal spectrum is carried out in this paper. In these study two local-matching methods based on Haar wavelet transform and Local Binary Pattern (LBP) are analyzed. Wavelet transform is a good tool to analyze multi-scale, multi-direction changes of tex...

  5. Morphological Multiscale Stationary Wavelet Transform based Texture Segmentation

    Directory of Open Access Journals (Sweden)

    Mosiganti Joseph Prakash

    2014-07-01

    Full Text Available Image segmentation is an important step in several computer vision applications. The segmentation of images into homogeneous and meaningful regions is a fundamental technique for image analysis. Textures occupy a vital role in a wide range of computer vision research fields; from microscopic images to images sent down to earth by satellites, from the analysis of multi-spectral scan images to outdoor scenes, all consist of texture. Although several methods have been proposed, less work has been done in developing suitable techniques for segmentation of texture images. After a careful and in-depth survey on wavelet transforms, the present study found that efficient numerical solutions in the signal processing applications can be found using Stationary Wavelet Transform (SWT. SWT is redundant, linear and shift invariant, that’s why it gives a better approximation than the DWT. In this paper a novel texture segmentation method based on “SWT and Textural Properties” is proposed. Multi scale SWT with Textural Properties and morphological treatment is used in the present study to detect fine edges from texture images for a fine segmentation.

  6. Research of image enhancement of dental cast based on wavelet transformation

    Science.gov (United States)

    Zhao, Jing; Li, Zhongke; Liu, Xingmiao

    2010-10-01

    This paper describes a 3D laser scanner for dental cast that realize non-contact deepness measuring. The scanner and the control PC make up of a 3D scan system, accomplish the real time digital of dental cast. Owing to the complexity shape of the dental cast and the random nature of scanned points, the detected feature curves are generally not smooth or not accurate enough for subsequent application. The purpose of this p is to present an algorithm for enhancing the useful points and eliminating the noises. So an image enhancement algorithm based on wavelet transform and fuzzy set theory is presented. Firstly, the multi-scale wavelet transform is adopted to decompose the input image, which extracts the characteristic of multi-scale of the image. Secondly, wavelet threshold is used for image de-noising, and then the traditional fuzzy set theory is improved and applied to enhance the low frequency wavelet coefficients and the high frequency wavelet coefficients of different directions of each scale. Finally, the inverse wavelet transform is applied to synthesis image. A group of experimental results demonstrate that the proposed algorithm is effective for the dental cast image de-noising and enhancement, the edge of the enhanced image is distinct which is good for the subsequent image processing.

  7. Facial Feature Extraction Based on Wavelet Transform

    Science.gov (United States)

    Hung, Nguyen Viet

    Facial feature extraction is one of the most important processes in face recognition, expression recognition and face detection. The aims of facial feature extraction are eye location, shape of eyes, eye brow, mouth, head boundary, face boundary, chin and so on. The purpose of this paper is to develop an automatic facial feature extraction system, which is able to identify the eye location, the detailed shape of eyes and mouth, chin and inner boundary from facial images. This system not only extracts the location information of the eyes, but also estimates four important points in each eye, which helps us to rebuild the eye shape. To model mouth shape, mouth extraction gives us both mouth location and two corners of mouth, top and bottom lips. From inner boundary we obtain and chin, we have face boundary. Based on wavelet features, we can reduce the noise from the input image and detect edge information. In order to extract eyes, mouth, inner boundary, we combine wavelet features and facial character to design these algorithms for finding midpoint, eye's coordinates, four important eye's points, mouth's coordinates, four important mouth's points, chin coordinate and then inner boundary. The developed system is tested on Yale Faces and Pedagogy student's faces.

  8. Extraction of Line Features from Multifidus Muscle of CT Scanned Images with Morphologic Filter Together with Wavelet Multi Resolution Analysis

    Directory of Open Access Journals (Sweden)

    Yoichiro Kitajima

    2011-09-01

    Full Text Available A method for line feature extraction from multifidus muscle of Computer Tomography (CT scanned image with morphologic filter together with wavelet based Multi Resolution Analysis (MRA is proposed. The contour of the multifidus muscle can be extracted from hip CT image. The area of multifidus muscle is then estimated and is used for an index of belly fat because there is a high correlation between belly fat and multifidus muscle. When the area of the multifidus muscle was calculated from the CT image, the MRA with Daubechies base functions and with the parameter of MRA of level is three would appropriate. After the wavelet transformation is applied to the original hip CT image three times and LLL (3D low frequency components is filled “0” then inverse wavelet transformation is applied for reconstruction. The proposed method is validated with four patients.

  9. Multiple descriptions based wavelet image coding

    Institute of Scientific and Technical Information of China (English)

    陈海林; 杨宇航

    2004-01-01

    We present a simple and efficient scheme that combines multiple descriptions coding with wavelet transform under JPEG2000 image coding architecture. To reduce packet losses, controlled amounts of redundancy are added to the wavelet transform coefficients to produce multiple descriptions of wavelet coefficients during the compression process to produce multiple descriptions bit-stream of a compressed image. Even if areceiver gets only parts of descriptions (other descriptions being lost), it can still reconstruct image with acceptable quality. Specifically, the scheme uses not only high-performance wavelet transform to improve compression efficiency, but also multiple descriptions technique to enhance the robustness of the compressed image that is transmitted through unreliable network channels.

  10. sEMG Pattern Recognition Based on Multi Feature Fusion of Wavelet Transform%基于小波变换的多特征融合sEMG模式识别

    Institute of Scientific and Technical Information of China (English)

    于亚萍; 孙立宁; 张峰峰; 张建法

    2016-01-01

    In view of the poor characterization of single feature value,multi feature fusion based on different wavelet basis was adopted to extract the surface EMG signal according to multi resolution analysis of wavelet transform. The experiment was conducted on ten testers and collected signals for four basic lower limb movements in daily life. First of all,discrete wavelet transform was used to decompose the surface EMG signals in multi-scale with DB, Dmey and Bior wavelet basis respectively. After that,it was founded that the characterization effects of different muscle vary by different extraction way. In order to combine the characteristics of different features ,features were fused to analyze and compare. At last,the feature values were input to the Elman neural network and BP neural net⁃work for pattern recognition and comparison analysis. Experimental results showed that the recognition rate ob⁃tained by fusing the eigenvalues is higher than single feature with the accuracy up to 98.7%,and the BP neural net⁃work is better than the Elman neural network.%针对单一特征值表征能力差的情况,根据小波变换的多分辨分析思想,采用基于多种母小波的多特征融合的特征提取方法对表面肌电信号进行特征提取。本实验对十名测试人员进行肌电信号的采集,对日常生活中的四个基本下肢动作进行测试。首先,分别基于DB、Dmey和Bior三种不同的母小波,采用离散小波变换通过不同的分析方法对表面肌电信号进行多尺度分解。然后,通过分析发现,不同肌肉在不同特征提取方式下表征效果存在差异,为了结合不同特征方式的特点对基于不同小波基的特征值进行融合分析并比较。最后,将特征值分别输入到Elman神经网络和BP神经网络进行模式识别并比较分析。实验结果表明:通过对不同特征值进行识别比较,融合处理的特征值可以达到98.7%的识别率,并且,BP

  11. Design and Implementation of Fast- Lifting Based Wavelet Transform for Image Compression

    Directory of Open Access Journals (Sweden)

    Lokesh B.S

    2014-06-01

    Full Text Available The digital data can be compressed and retrieved using Discrete Wavelet Transform (DWT and Inverse Discrete wavelet Transform (IDWT. The medical images need to be compressed and retrieved without loosing of information. The Discrete Wavelet Transform (DWT is based on time-scale representation which provides efficient multi-resolution. This paper mainly describes the lifting based scheme gives lossless mode of information. The lifting based DWT and IDWT are having lower computational complexity and reduced memory requirement. Conventional convolution based DWT and IDWT are area and power hungry. These drawbacks can be overcome by using the lifting based scheme. This system adopts lifting based scheme DWT and IDWT which gives the lossless information of the data, reduces the complexity and optimized in area and power. In this research the DWT and IDWT are simulated and the design of hardware model is carried out using RTL level coding.

  12. 基于主方向构造二分树复数小波的新方法%A New Construction Method for the Dual Tree Complex Wavelet Based on Direction Sensitivity

    Institute of Scientific and Technical Information of China (English)

    王红霞; 陈波; 成礼智

    2006-01-01

    The conception of "main direction" of multi-dimensional wavelet is established in this paper, and the capabilities of several classical complex wavelets for representing directional singularities are investigated based on their main directions. It is proved to be impossible to represent directional singularities optimally by a multi-resolution analysis (MRA) of L2(R2). Based on the above results, a new algorithm to construct Q-shift dual tree complex wavelet is proposed. By optimizing the main direction of parameterized wavelet filters, the difficulty in choosing stop-band frequency is overcome and the performances of the designed wavelet are improved too. Furthermore, results of image enhancement by various multi-scale methods are given, which show that the new designed Q-shift complex wavelet do offer significant improvement over the conventionally used wavelets. Direction sensitivity is an important index to the performance of 2D wavelets.

  13. 基于证据理论的小波域多特征医学图像融合%Medical image fusion with multi-feature based on evidential theory in wavelet domain

    Institute of Scientific and Technical Information of China (English)

    姚丽莎; 赵海峰; 罗斌; 朱珍元

    2012-01-01

    To address the uncertainty of weights selection in multi-source medical image fusion process, the basic probability assignment function of the evidence was used to express decision result's uncertainty based on Dempster-Shafer (DS) evidential theory. Three features of the detected image, which are regional variance, regional energy, and regional information entropy, were used and normalized, then the basic probability assignment could be got according to the features. Image fusion rules with multi-feature based on DS evidence theory were used for high frequency components in wavelet domain. Adaptive fusion rules of Energy of Laplacian { EOL) were used for low frequency component in wavelet domain according to EOL. The experimental results show that the proposed algorithm is superior to other fusion algorithms. It combines the advantages of multi-feature, reduces the uncertainty during the image fusion process and retains the details of the image in large extent.%针对多源医学图像融合过程中融合权值选择的不确定性,根据DS证据理论,采用证据理论中的基本概率分配函数来描述判决结果的不确定性.利用图像的区域方差、区域能量、区域信息熵三个特征,然后对特征进行归一化,将各个特征值作为基本概率分配的依据,在小波域内对高频分量采用基于DS证据理论的多特征融合规则进行图像融合.利用拉普拉斯能量,在小波域内对低频分量采用拉普拉斯能量自适应融合规则.实验结果表示:所提算法综合了多个特征的优势,降低了融合过程中的不确定性,较大程度地保留了图像信息.

  14. Study on Singularity of Chaotic Signal Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    YOU Rong-yi

    2006-01-01

    Based on the variations of wavelet transform modulus maxima at multi-scales,the singularity of chaotic signals are studied,and the singularity of these signals are measured by the Lipschitz exponent.In the meantime,a nonlinear method is proposed based on the higher order statistics,on the other aspect,which characterizes the higher order singular spectrum (HOSS) of chaotic signals.All computations are done with Lorenz attractor,Rossler attractor and EEG (electroencephalogram) time series and the comparisions among these results are made.The experimental results show that the Lipschitz exponents and the higher order singular spectra of these signals are significantly different from each other,which indicates these methods are effective for studing the singularity of chaotic signals.

  15. 基于交叉小波变换的水文多尺度相关分析%Multi-scale correlation analysis of hydrological time series based on cross wavelet transform

    Institute of Scientific and Technical Information of China (English)

    邵骏

    2013-01-01

    针对传统水文相关分析的局限性,将交叉小波变换应用于水文时间序列的相关分析.采用交叉小波功率谱和凝聚谱分析伊犁河雅马渡水文站实测年径流量与4个影响因子之间的联合统计特征及其在时频域中的相关关系,揭示其在不同时间和频率尺度上的相关程度和细部特征.研究结果表明,与传统的相关系数只能从总体上考察两个时间序列的相关关系相比,交叉小波变换能够从时域和频率两方面同时考察两者的相关振荡随频率和时间后延的变化细节、局部特征和位相差异,在水文相关分析方面具有较好的应用效果.%As traditional hydrologic correlation analysis can only exhibit the basic relationship of two time series, a new multi-scale correlation analysis method based on cross wavelet transform is presented. This new approach was used to study the associated statistical characteristics and time frequency correlations of the annual runoff at the Ya Madu station with annual rainfall, monthly mean zonal circulation index, monthly mean radial circulation index and solar radio flux. Cross wavelet transform and wavelet coherence for examining the relationships of two series in both the time and frequency domains are discussed in this paper. The results show that this method possesses an ability to distinguish coupling signals and an excellence to describe the distribution of coupling signals in time-frequency space and it provides a new analytic method to hydrological time series correlation analysis.

  16. Electrocardiogram de-noising based on forward wavelet transform translation invariant application in bionic wavelet domain

    Indian Academy of Sciences (India)

    Mourad Talbi

    2014-08-01

    In this paper, we propose a new technique of Electrocardiogram (ECG) signal de-noising based on thresholding of the coefficients obtained from the application of the Forward Wavelet Transform Translation Invariant (FWT_TI) to each Bionic Wavelet coefficient. The De-noise De-noised ECG is obtained from the application of the inverse of BWT (BWT−1) to the de-noise de-noised bionic wavelet coefficients. For evaluating this new proposed de-noising technique, we have compared it to a thresholding technique in the FWT_TI domain. Preliminary tests of the application of the two de-noising techniques were constructed on a number of ECG signals taken from MIT-BIH database. The obtained results from Signal to Noise Ratio (SNR) and Mean Square Error (MSE) computations showed that our proposed de-noising technique outperforms the second technique. We have also compared the proposed technique to the thresholding technique in the bionic wavelet domain and this comparison was performed by SNR improvement computing. The obtained results from this evaluation showed that the proposed technique also outperforms the de-noising technique based on bionic wavelet coefficients thresholding.

  17. Numerical solution of multi-dimensional compressible reactive flow using a parallel wavelet adaptive multi-resolution method

    Science.gov (United States)

    Grenga, Temistocle

    The aim of this research is to further develop a dynamically adaptive algorithm based on wavelets that is able to solve efficiently multi-dimensional compressible reactive flow problems. This work demonstrates the great potential for the method to perform direct numerical simulation (DNS) of combustion with detailed chemistry and multi-component diffusion. In particular, it addresses the performance obtained using a massive parallel implementation and demonstrates important savings in memory storage and computational time over conventional methods. In addition, fully-resolved simulations of challenging three dimensional problems involving mixing and combustion processes are performed. These problems are particularly challenging due to their strong multiscale characteristics. For these solutions, it is necessary to combine the advanced numerical techniques applied to modern computational resources.

  18. Multiple descriptions based wavelet image coding

    Institute of Scientific and Technical Information of China (English)

    CHEN Hai-lin(陈海林); YANG Yu-hang(杨宇航)

    2004-01-01

    We present a simple and efficient scheme that combines multiple descriptions coding with wavelet transform under JPEG2000 image coding architecture. To reduce packet losses, controlled amounts of redundancy are added to the wavelet transform coefficients to produce multiple descriptions of wavelet coefficients during the compression process to produce multiple descriptions bit-stream of a compressed image. Even if a receiver gets only parts of descriptions (other descriptions being lost), it can still reconstruct image with acceptable quality. Specifically, the scheme uses not only high-performance wavelet transform to improve compression efficiency, but also multiple descriptions technique to enhance the robustness of the compressed image that is transmitted through unreliable network channels.

  19. Wavelet Transform based Medical Image Fusion With different fusion methods

    OpenAIRE

    Anjali Patil; M N Tibdewal

    2015-01-01

    This paper proposes wavelet transform based image fusion algorithm, after studying the principles and characteristics of the discrete wavelet transform. Medical image fusion used to derive useful information from multimodality medical images. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide more information to the doctor and clinical treatment planning system. This paper based on t...

  20. Seismic data compression based on integer wavelet transform

    Institute of Scientific and Technical Information of China (English)

    王喜珍; 滕云田; 高孟潭; 姜慧

    2004-01-01

    Due to the particularity of the seismic data, they must be treated by lossless compression algorithm in some cases.In the paper, based on the integer wavelet transform, the lossless compression algorithm is studied. Comparingwith the traditional algorithm, it can better improve the compression rate. CDF (2, n) biorthogonal wavelet familycan lead to better compression ratio than other CDF family, SWE and CRF, which is owe to its capability in can-celing data redundancies and focusing data characteristics. CDF (2, n) family is suitable as the wavelet function ofthe lossless compression seismic data.

  1. MIXED SCHEME FOR IMAGE EDGE DETECTION BASED ON WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Xie Hongmei; Yu Bianzhang; Zhao Jian

    2004-01-01

    A mixed scheme based on Wavelet Transformation (WT) is proposed for image edge detection. The scheme combines the wavelet transform and traditional Sobel and LoG (Laplacian of Gaussian) operator edge-detection algorithms. The precise theory analysis is given to show that the wavelet transformation has an advantage for signal processing. Simulation results show that the new scheme is better than only using the Sobel or LoG methods. Complexity analysis is also given and the conclusion is acceptable, therefore the proposed scheme is effective for edge detection.

  2. Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network

    Institute of Scientific and Technical Information of China (English)

    WANG Zhenfei; ZHAI Guangqun; WANG Nengchao

    2006-01-01

    An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system (HVS) characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.

  3. Wavelet Transform based Medical Image Fusion With different fusion methods

    Directory of Open Access Journals (Sweden)

    Anjali Patil

    2015-03-01

    Full Text Available This paper proposes wavelet transform based image fusion algorithm, after studying the principles and characteristics of the discrete wavelet transform. Medical image fusion used to derive useful information from multimodality medical images. The idea is to improve the image content by fusing images like computer tomography (CT and magnetic resonance imaging (MRI images, so as to provide more information to the doctor and clinical treatment planning system. This paper based on the wavelet transformation to fused the medical images. The wavelet based fusion algorithms used on medical images CT and MRI, This involve the fusion with MIN , MAX, MEAN method. Also the result is obtained. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field

  4. Wavelet transformation based watermarking technique for human electrocardiogram (ECG).

    Science.gov (United States)

    Engin, Mehmet; Cidam, Oğuz; Engin, Erkan Zeki

    2005-12-01

    Nowadays, watermarking has become a technology of choice for a broad range of multimedia copyright protection applications. Watermarks have also been used to embed prespecified data in biomedical signals. Thus, the watermarked biomedical signals being transmitted through communication are resistant to some attacks. This paper investigates discrete wavelet transform based watermarking technique for signal integrity verification in an Electrocardiogram (ECG) coming from four ECG classes for monitoring application of cardiovascular diseases. The proposed technique is evaluated under different noisy conditions for different wavelet functions. Daubechies (db2) wavelet function based technique performs better than those of Biorthogonal (bior5.5) wavelet function. For the beat-to-beat applications, all performance results belonging to four ECG classes are highly moderate. PMID:16235811

  5. Wavelet based methods for improved wind profiler signal processing

    Directory of Open Access Journals (Sweden)

    V. Lehmann

    Full Text Available In this paper, we apply wavelet thresholding for removing automatically ground and intermittent clutter (airplane echoes from wind profiler radar data. Using the concept of discrete multi-resolution analysis and non-parametric estimation theory, we develop wavelet domain thresholding rules, which allow us to identify the coefficients relevant for clutter and to suppress them in order to obtain filtered reconstructions.

    Key words. Meteorology and atmospheric dynamics (instruments and techniques – Radio science (remote sensing; signal processing

  6. 基于多尺度二维小波变换的静脉图像融合%Vein Image Fusion Based on Two-dimensional Wavelet Multi-scale Transform

    Institute of Scientific and Technical Information of China (English)

    欧锋; 黄丹飞

    2015-01-01

    Venous blood vessels visible image detail is rich but vascular hazy outline;Venous blood vessels infrared image contour obviously but lack of details;Aiming at the shortcomings of the single vein image, this paper proposes a vein image fusion method based on multi-scale wavelet transform,the fusion image retain the source image for more infor-mation,richer details,clearer outline,better visual effect,provide very good auxiliary effect for clinical venipuncture.%静脉可见光图像血管细节较丰富,但血管轮廓模糊;静脉红外图像血管轮廓明显,但细节欠缺。针对单一静脉图像存在的不足,提出了一种基于多尺度二维小波变换的静脉图像融合方法,通过实验证实融合后的静脉图像保留了源图像更多的信息,静脉血管细节丰富、轮廓清晰、视觉效果良好,为临床静脉穿刺提供辅助作用,具有很好的临床应用价值。

  7. 基于多尺度小波分解FDR的激光预警系统信息数据校正%Based on Multi-scale Wavelet Decomposition FDR Information and Data Correction for Laser Warning System

    Institute of Scientific and Technical Information of China (English)

    鲁旭涛; 李静; 杨泽辉

    2014-01-01

    为了使激光预警系统可以实时、精确地捕获来袭激光的特征信息,降低虚警与漏警的发生,提高系统的信噪比成为研究的重点。为了准确判断来袭激光的光谱信息,针对不合作激光信号而言,提高信噪比的方法就集中的体现在采用信息技术对各种噪声、干扰的抑制和消除等方法上。设计了基于多尺度小波分解及错误假设检验算法的信噪比优化模型。根据小波降噪原理,对来袭激光信号做多尺度分解,再采用错误假设检验算法完成了小波降噪系数的阈值选取。实验结果显示,采用该种技术降噪后系统信噪比提高到64.22 dB,其相应的波长分辨率为2 nm,相比没有滤波和仅用传统滤波算法的实验数据,有了明显的改观,系统抗干扰能力显著增强。%In order to make the laser warning system with real-time,accurately capture the characteristic information of the incoming laser,minimizes false alarm and missed alarm occurred, improving the signal-to-noise ratio of the system become the focus of the study. In order to accurately determine the spectral information of the incoming laser,to uncooperative laser signal,improved the signal-to-noise ratio is reflected in the use of information technology for all kinds of noise,interference suppression and elimination methods. Designed optimization model based on multi-scale wavelet decomposition and signal-to-noise ratio of the error hypothesis testing algorithm. According to the principle of wavelet noise reduction, incoming laser signal with multi-scale decomposition, FDR(False Discovery Rate) algorithm to complete the wavelet noise reduction coefficient threshold. Experimental results show that, used by the technical noise reduction system signal-to-noise ratio improved to 64.22 dB. The corresponding wavelength resolution of 2 nm,filtering and only traditional filtering algorithm compared to experimental data,has been

  8. [ECoG classification based on wavelet variance].

    Science.gov (United States)

    Yan, Shiyu; Liu, Chong; Wang, Hong; Zhao, Haibin

    2013-06-01

    For a typical electrocorticogram (ECoG)-based brain-computer interface (BCI) system in which the subject's task is to imagine movements of either the left small finger or the tongue, we proposed a feature extraction algorithm using wavelet variance. Firstly the definition and significance of wavelet variance were brought out and taken as feature based on the discussion of wavelet transform. Six channels with most distinctive features were selected from 64 channels for analysis. Consequently the EEG data were decomposed using db4 wavelet. The wavelet coeffi-cient variances containing Mu rhythm and Beta rhythm were taken out as features based on ERD/ERS phenomenon. The features were classified linearly with an algorithm of cross validation. The results of off-line analysis showed that high classification accuracies of 90. 24% and 93. 77% for training and test data set were achieved, the wavelet vari-ance had characteristics of simplicity and effectiveness and it was suitable for feature extraction in BCI research. K PMID:23865300

  9. Generalized Tree-Based Wavelet Transform

    CERN Document Server

    Ram, Idan; Cohen, Israel

    2010-01-01

    In this paper we propose a new wavelet transform applicable to functions defined on graphs, high dimensional data and networks. The proposed method generalizes the Haar-like transform proposed in \\cite{gavish2010mwot}, and it is similarly defined via a hierarchical tree, which is assumed to capture the geometry and structure of the input data. It is applied to the data using a multiscale filtering and decimation scheme, which can employ different wavelet filters. We propose a tree construction method which results in efficient representation of the input function in the transform domain. We show that the proposed transform is more efficient than both the 1D and 2D separable wavelet transforms in representing images. We also explore the application of the proposed transform to image denoising, and show that combined with a subimage averaging scheme, it achieves denoising results which are similar to the ones obtained with the K-SVD algorithm.

  10. Research of Adaptive Resolution Spectrum Sensing Method Based on Discrete Wavelet Packet Transform

    Directory of Open Access Journals (Sweden)

    Wei Naiqi

    2013-09-01

    Full Text Available Spectrum sensing is the precondition of the realization of cognitive radio. In order to achieve efficient multi-resolution spectrum sensing, and find the available spectrum hole quickly, it proposes a variable resolution adaptive frequency spectrum energy sensing method based on discrete wavelet packet transform (DWPT. The method applied hierarchical decomposition and threshold denoising characteristic of wavelet packet transform, and solved the problem of subband sort disorder in wavelet packet decomposition process; it can eliminate the influence of uncertainty noise on detection performance, effectively. It also can reduce the computational complexity according to demand of selection resolution and perception band. The simulation results and its analysis show that the proposed method has advantages of high precision, simple arithmetic and fine flexibility, etc. The method is adapted to fast sensing in the cognitive radio environment.  

  11. New Blocking Artifacts Reduction Method Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    SHI Min; YI Qing-ming

    2007-01-01

    It is well known that a block discrete cosine transform compressed image exhibits visually annoying blocking artifacts at low-bit-rate. A new post-processing deblocking algorithm in wavelet domain is proposed. The algorithm exploits blocking-artifact features shown in wavelet domain. The energy of blocking artifacts is concentrated into some lines to form annoying visual effects after wavelet transform. The aim of reducing blocking artifacts is to capture excessive energy on the block boundary effectively and reduce it below the visual scope. Adaptive operators for different subbands are computed based on the wavelet coefficients. The operators are made adaptive to different images and characteristics of blocking artifacts. Experimental results show that the proposed method can significantly improve the visual quality and also increase the peak signal-noise-ratio(PSNR) in the output image.

  12. On exploiting wavelet bases in statistical region-based segmentation

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    Statistical region-based segmentation methods such as the Active Appearance Models establish dense correspondences by modelling variation of shape and pixel intensities in low-resolution 2D images. Unfortunately, for high-resolution 2D and 3D images, this approach is rendered infeasible due...... 9-7 wavelet on cardiac MRIs and human faces show that the segmentation accuracy is minimally degraded at compression ratios of 1:10 and 1:20, respectively....

  13. 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...

  14. 基于小波变换的多模态医学图像的融合及性能评价%Multi-morphological Fusion of Medical Image and Performance Evaluation based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    高清河; 刚晶; 王和禹; 刘海英

    2014-01-01

    We choosed multi-focus images and standard MRI/CT gray images for source images and applied the following strate-gies to decomposition and fusion.On the one hand,decomposition of low frequency and high frequency wavelet coefficients was adopted with single scale and multi-scale way.On the other hand,image fusion region was selected by independent pixel and neighborhood operation.Through above different operation of decomposition and fusion,the fusion image of different fusion rule was obtained.Based on the fusion image of different fusion rule,the influences of various fusion rules on the fusion results were compared and analyzed, which was the purpose of this paper.By means of analyzing the experimental results and evaluation results of multi-focus fusion ima-ges and clinical MRI/CT fusion images.It show that neighborhood filtering operation can greatly improve the result of image fusion and make the detail of the image information more rich;multi-scale decompose can increase the brightness of the fusion image with respect to single scale decompose.%为了研究小波变换分解的尺度和融合策略对图像融合效果的影响。我们选择已配准后的多聚焦医学图像以及MRI/C T灰度图像,在提取图像的低频和高频小波系数时,分别进行单尺度和多尺度分解,融合时采取了基于独立像素点和基于邻域窗口的多种融合策略,深入对比分析各种融合规则对医学图像融合性能的影响。实验结果和性能评价表明:使用局部滤波的操作可以明显改善图像融合的效果,使图像的细节信息更加丰富,而多尺度融合能明显提高融合图像的亮度。

  15. Implementation of Texture Based Image Retrieval Using M-band Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    Liao Ya-li; Yang Yan; Cao Yang

    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.

  16. 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.

  17. Multi-point wind pressure signal preprocessing approach based on SYM wavelet and its application%基于SYM小波的多点风压信号预处理方法及应用

    Institute of Scientific and Technical Information of China (English)

    段旻; 谢壮宁; 李小康

    2013-01-01

    在对几类小波基降噪效果分析的基础上,确定一种小波降噪方案,将其应用到典型大跨与高层结构风洞试验同步测压信号的预处理,分析仪器噪声对极值风压、风致位移及加速度响应计算结果的影响.结果表明:symN小波为风压信号预处理的最优小波基,通过调整其消失矩阶数可分别获得不同信噪比信号的最佳降噪效果.对一体育场屋盖,仪器噪声对最大峰值负压估计的影响较小,但会不同程度地高估大跨度屋盖结构位移响应,仪器噪声会使位移背景和共振分量分别被高估7.69%和70.6%,并使总响应被高估16.96%.对某439m超高层建筑结构,较长重现期风速作用下结构风致响应的计算受仪器噪声影响相对较小;但对较短重现期风速而言,噪声对建筑顶部峰值加速度响应估算的影响不容忽视,采用有效减噪措施可提高风致峰值加速度的估算精度.%Wavelet-based de-noising method was proposed for per-processing wind pressure signals of wind tunnel models of large span roof structures and tall buildings with the synchronous multi-pressure measurement technique on the basis of effectiveness verification and comparison of de-noising effects of several types of wavelets. The effects of instrumental noise on calculation of extreme wind pressure, wind induced deflection and acceleration were analyzed. The results showed that symN wavelet is the best one for preprocessing wind pressure signals, and the best de-noising effect can be obtained by using different vanishing moments; for a large stadium roof structure, an instrumental noise affects estimation of extreme wind pressure slightly, but affects calculation of wind induced response seriously; the instrumental noise can lead to background and resonant components be over-estimated by 7. 69% and 70. 6% , respectively, and the total deflection be over-estimated by 16.96% ; for a 439 m high tall building, the effect of the

  18. Adaptive Image Transmission Scheme over Wavelet-Based OFDM System

    Institute of Scientific and Technical Information of China (English)

    GAOXinying; YUANDongfeng; ZHANGHaixia

    2005-01-01

    In this paper an adaptive image transmission scheme is proposed over Wavelet-based OFDM (WOFDM) system with Unequal error protection (UEP) by the design of non-uniform signal constellation in MLC. Two different data division schemes: byte-based and bitbased, are analyzed and compared. Different bits are protected unequally according to their different contribution to the image quality in bit-based data division scheme, which causes UEP combined with this scheme more powerful than that with byte-based scheme. Simulation results demonstrate that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality. Furthermore, by considering the tradeoff of complexity and BER performance, Haar wavelet with the shortest compactly supported filter length is the most suitable one among orthogonal Daubechies wavelet series in our proposed system.

  19. Automatic Image Registration Algorithm Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    LIU Qiong; NI Guo-qiang

    2006-01-01

    An automatic image registration approach based on wavelet transform is proposed. This proposed method utilizes multiscale wavelet transform to extract feature points. A coarse-to-fine feature matching method is utilized in the feature matching phase. A two-way matching method based on cross-correlation to get candidate point pairs and a fine matching based on support strength combine to form the matching algorithm. At last, based on an affine transformation model, the parameters are iteratively refined by using the least-squares estimation approach. Experimental results have verified that the proposed algorithm can realize automatic registration of various kinds of images rapidly and effectively.

  20. A Video Coding Algorithm Based on Intraframe and Interframe Joint Prediction of Wavelet Coefficients

    Institute of Scientific and Technical Information of China (English)

    1998-01-01

    A novel video coding algorithm based on intraframe and interframe prediction of wavelet coefficients is described in this paper. A BZT (Block Zero Tree) coding scheme represents the position of zero coefficients block efficiently. For nonzero coefficients block, temporal redundancy is removed using MultiResolution Motion Estimation (MRME). The proposed algorithm improves the coding performance significantly over the baseline MRME technique. In addition, the computational complexity is further reduced.

  1. Wavelet-Based Speech Enhancement Using Time-Frequency Adaptation

    Directory of Open Access Journals (Sweden)

    Kun-Ching Wang

    2009-01-01

    Full Text Available Wavelet denoising is commonly used for speech enhancement because of the simplicity of its implementation. However, the conventional methods generate the presence of musical residual noise while thresholding the background noise. The unvoiced components of speech are often eliminated from this method. In this paper, a novel algorithm of wavelet coefficient threshold (WCT based on time-frequency adaptation is proposed. In addition, an unvoiced speech enhancement algorithm is also integrated into the system to improve the intelligibility of speech. The wavelet coefficient threshold (WCT of each subband is first temporally adjusted according to the value of a posterior signal-to-noise ratio (SNR. To prevent the degradation of unvoiced sounds during noise, the algorithm utilizes a simple speech/noise detector (SND and further divides speech signal into unvoiced and voiced sounds. Then, we apply appropriate wavelet thresholding according to voiced/unvoiced (V/U decision. Based on the masking properties of human auditory system, a perceptual gain factor is adopted into wavelet thresholding for suppressing musical residual noise. Simulation results show that the proposed method is capable of reducing noise with little speech degradation and the overall performance is superior to several competitive methods.

  2. ECG Analysis based on Wavelet Transform and Modulus Maxima

    Directory of Open Access Journals (Sweden)

    Mourad Talbi

    2012-01-01

    Full Text Available In this paper, we have developed a new technique of P, Q, R, S and T Peaks detection using Wavelet Transform (WT and Modulus maxima. One of the commonest problems in electrocardiogram (ECG signal processing, is baseline wander removal suppression. Therefore we have removed the baseline wander in order to make easier the detection of the peaks P and T. Those peaks are detected after the QRS detection. The proposed method is based on the application of the discritized continuous wavelet transform (Mycwt used for the Bionic wavelet transform, to the ECG signal in order to detect R-peaks in the first stage and in the second stage, the Q and S peaks are detected using the R-peaks localization. Finally the Modulus maxima are used in the undecimated wavelet transform (UDWT domain in order to detect the others peaks (P, T. This detection is performed by using a varying-length window that is moving along the whole signal. For evaluating the proposed method, we have compared it to others techniques based on wavelets. In this evaluation, we have used many ECG signals taken from MIT-BIH database. The obtained results show that the proposed method outperforms a number of conventional techniques used for our evaluation.

  3. A Wavelet-Based Approach to Pattern Discovery in Melodies

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David; Weyde, Tillman

    2016-01-01

    We present a computational method for pattern discovery based on the application of the wavelet transform to symbolic representations of melodies or monophonic voices. We model the importance of a discovered pattern in terms of the compression ratio that can be achieved by using it to describe...... transform (CWT) at a single scale using the Haar wavelet. These representations are segmented using various approaches and the segments are then concatenated based on their similarity. The concatenated segments are compared, clustered and ranked. The method was evaluated on two musicological tasks...

  4. Wavelet neural network based fault diagnosis in nonlinear analog circuits

    Institute of Scientific and Technical Information of China (English)

    Yin Shirong; Chen Guangju; Xie Yongle

    2006-01-01

    The theories of diagnosing nonlinear analog circuits by means of the transient response testing are studied. Wavelet analysis is made to extract the transient response signature of nonlinear circuits and compress the signature dada. The best wavelet function is selected based on the between-category total scatter of signature. The fault dictionary of nonlinear circuits is constructed based on improved back-propagation(BP) neural network. Experimental results demonstrate that the method proposed has high diagnostic sensitivity and fast fault identification and deducibility.

  5. EMD-based Adaptive Wavelet Threshold for Pulse Wave Denoising

    Institute of Scientific and Technical Information of China (English)

    XU Li-shengl; SHEN Yan-hua; ZHONG Yue; KANG Yan; Max Q-H Meng

    2015-01-01

    It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave, and affect the hemodynamic analysis and diagnosis based on pulse wave signalsIn order to remove these noises, an adaptive de-noising method based on empirical mode decomposition (EMD) and wavelet threshold is proposed in this paperCompared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.

  6. Palmprint Recognition by Applying Wavelet-Based Kernel PCA

    Institute of Scientific and Technical Information of China (English)

    Murat Ekinci; Murat Aykut

    2008-01-01

    This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coefficients are chosen for palm representation.The kernel PCA method is then applied to extract non-linear features from the subband coefficients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.

  7. Steganography based on wavelet transform and modulus function

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In order to provide larger capacity of the hidden secret data while maintaining a good visual quality of stego-image,in accordance with the visual property that human eyes are less sensitive to strong texture,a novel steganographic method based on wavelet and modulus function is presented.First,an image is divided into blocks of prescribed size,and every block is decomposed into one-level wavelet.Then,the capacity of the hidden secret data is decided with the number of wavelet coefficients of larger magnitude.Finall,secret information is embedded by steganography based on modulus function. From the experimental results,the proposed method hides much more information and maintains a good visual quality of stego-image.Besides,the embedded data can be extracted from the stego-image without referencing the original image.

  8. A chaos-based robust wavelet-domain watermarking algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Zhao Dawei E-mail: davidzhaodw@hotmail.com; Chen Guanrong; Liu Wenbo

    2004-10-01

    In this paper, a chaos-based watermarking algorithm is developed in the wavelet domain for still images. The wavelet transform is commonly applied for watermarking, where the whole image is transformed in the frequency domain. In contrast to this conventional approach, we apply the wavelet transform only locally. We transform the subimage, which is extracted from the original image, in the frequency domain by using DWT and then embed the chaotic watermark into part of the subband coefficients. As usual, the watermark is detected by computing the correlation between the watermarked coefficients and the watermarking signal, where the watermarking threshold is chosen according to the Neyman-Pearson criterion based on some statistical assumptions. Watermark detection is accomplished without using the original image. Simulation results show that we can gain high fidelity and high robustness, especially under the typical attack of geometric operations.

  9. Method of Infrared Image Enhancement Based on Stationary Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    QI Fei; LI Yan-jun; ZHANG Ke

    2008-01-01

    Aiming at the problem, i.e. infrared images own the characters of bad contrast ratio and fuzzy edges, a method to enhance the contrast of infrared image is given, which is based on stationary wavelet transform. After making stationary wavelet transform to an infrared image, denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity. For the approximation coefficient matrix with low noisy intensity, enhancement is done by the proposed method based on histogram. The enhanced image can be got by wavelet coefficient reconstruction. Furthermore, an evaluation criterion of enhancement performance is introduced. The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively. At the same time, its amount of calculation is small and operation speed is fast.

  10. Face Recognition System Based on Spectral Graph Wavelet Theory

    Directory of Open Access Journals (Sweden)

    R. Premalatha Kanikannan

    2014-09-01

    Full Text Available This study presents an efficient approach for automatic face recognition based on Spectral Graph Wavelet Theory (SGWT. SGWT is analogous to wavelet transform and the transform functions are defined on the vertices of a weighted graph. The given face image is decomposed by SGWT at first. The energies of obtained sub-bands are fused together and considered as feature vector for the corresponding image. The performance of proposed system is analyzed on ORL face database using nearest neighbor classifier. The face images used in this study has variations in pose, expression and facial details. The results indicate that the proposed system based on SGWT is better than wavelet transform and 94% recognition accuracy is achieved.

  11. Bearing Fault Diagnosis Based on Laplace Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Yingjie Yin

    2012-12-01

    Full Text Available The roller bearing characteristic frequencies contain very little energy, and are usually overwhelmed by noise and higher levels of structural vibrations. Therefore, envelope spectrum analysis is widely used to detection bearing localized fault. In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the Laplace wavelet transform and envelope spectrum is proposed for detection and diagnosis defects in roller element bearings. The basic principle is introduced in detail. Laplace wavelet transform is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original times series data in intrinsic oscillation modes, using the Laplace wavelet transform. Then the envelope spectrum is applied to the selected daughter wavelet that stands for the bearing faults. The experimental results show that Laplace wavelet can extract the impulse response from strong noise signals and can effectively diagnose the faults of bearing.

  12. An Automated Parallel Image Registration Technique Based on the Correlation of Wavelet Features

    Science.gov (United States)

    LeMoigne, Jacqueline; Campbell, William J.; Cromp, Robert F.; Zukor, Dorothy (Technical Monitor)

    2001-01-01

    With the increasing importance of multiple platform/multiple remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Our work utilizes maxima of wavelet coefficients to form the basic features of a correlation-based automatic registration algorithm. Our wavelet-based registration algorithm is tested successfully with data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Landsat/Thematic Mapper(TM), which differ by translation and/or rotation. By the choice of high-frequency wavelet features, this method is similar to an edge-based correlation method, but by exploiting the multi-resolution nature of a wavelet decomposition, our method achieves higher computational speeds for comparable accuracies. This algorithm has been implemented on a Single Instruction Multiple Data (SIMD) massively parallel computer, the MasPar MP-2, as well as on the CrayT3D, the Cray T3E and a Beowulf cluster of Pentium workstations.

  13. Relationship of d-dimensional continuous multi-scale wavelet shrinkage with integro-differential equations

    Energy Technology Data Exchange (ETDEWEB)

    Liu Guojun [Department of Applied Mathematics, Xidian University, Mail box: 245-59, Xi' an Shaanxi 710071 (China); School of Mathematics and Computer Science, Ningxia University, Yinchuan, Ningxia 750021 (China)], E-mail: liugj@nxu.edu.cn; Feng Xiangchu; Li Min [Department of Applied Mathematics, Xidian University, Mail box: 245-59, Xi' an Shaanxi 710071 (China)

    2009-05-15

    The goal of this paper is to extend the results of Didas and Weickert [Didas, S, Weickert, J. Integrodifferential equations for continuous multi-scale wavelet shrinkage. Inverse Prob Imag 2007;1:47-62.] to d-dimensional (d {>=} 1) case. Firstly, we relate a d-dimensional continuous mother wavelet {psi}(x) with a fast decay and n vanishing moments to the sum of the order partial derivative of a group of functions {theta}{sup k}(x)(|k| = n) with fast decay, which also makes wavelet transform equal to a sum of smoothed partial derivative operators. Moreover, d-dimensional continuous wavelet transform can be explained as a weighted average of pseudo-differential equations, too. For d = 1, our results are completely same as Didas and Weickert (2007), but for d > 1, it is different from the type of one variable. Finally, we exploit the reason with an example of 2-dimensional and 3-dimensional Mexican hat wavelet.

  14. WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

    Directory of Open Access Journals (Sweden)

    Li Song

    2010-04-01

    Full Text Available Abstract Background Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification. Results We developed a novel discrete wavelet transform (DWT and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the Trans-Proteomic Pipeline (TPP, a commonly used open source proteomics analysis pipeline. Conclusions We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at http://systemsbiozju.org/data/WaveletQuant.

  15. Simulation-based design using wavelets

    Science.gov (United States)

    Williams, John R.; Amaratunga, Kevin S.

    1994-03-01

    The design of large-scale systems requires methods of analysis which have the flexibility to provide a fast interactive simulation capability, while retaining the ability to provide high-order solution accuracy when required. This suggests that a hierarchical solution procedure is required that allows us to trade off accuracy for solution speed in a rational manner. In this paper, we examine the properties of the biorthogonal wavelets recently constructed by Dahlke and Weinreich and show how they can be used to implement a highly efficient multiscale solution procedure for solving a certain class of one-dimensional problems.

  16. Pautomatic Sea Target Detection Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    PEI Li-li; LUO Hai-bo

    2009-01-01

    An effective automatic target detection algorithm based on wavelet transform, which takes advantage of the localization and the orientation of wavelet analysis, is proposed. The algorithm detects the target in the vertical component of the wavelet transformation of the image. After mutual energy combination and sea clutter suppression through spatial weighting and thresholding, the target is located through maximum energy determination and its size is indicated through similarity measurement function of two overlapping windows. Experiment results show that the target can be detected by the algorithm in a single image frame and the better efficiency can be obtained also under the complicated backgrounds of existing the disturbances of cloud layer and fish scale light.

  17. Adaptively wavelet-based image denoising algorithm with edge preserving

    Institute of Scientific and Technical Information of China (English)

    Yihua Tan; Jinwen Tian; Jian Liu

    2006-01-01

    @@ A new wavelet-based image denoising algorithm, which exploits the edge information hidden in the corrupted image, is presented. Firstly, a canny-like edge detector identifies the edges in each subband.Secondly, multiplying the wavelet coefficients in neighboring scales is implemented to suppress the noise while magnifying the edge information, and the result is utilized to exclude the fake edges. The isolated edge pixel is also identified as noise. Unlike the thresholding method, after that we use local window filter in the wavelet domain to remove noise in which the variance estimation is elaborated to utilize the edge information. This method is adaptive to local image details, and can achieve better performance than the methods of state of the art.

  18. FAST TEXT LOCATION BASED ON DISCRETE WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Li Xiaohua; Shen Lansun

    2005-01-01

    The paper describes a texture-based fast text location scheme which operates directly in the Discrete Wavelet Transform (DWT) domain. By the distinguishing texture characteristics encoded in wavelet transform domain, the text is fast detected from complex background images stored in the compressed format such as JPEG2000 without full decompress. Compared with some traditional character location methods, the proposed scheme has the advantages of low computational cost, robust to size and font of characters and high accuracy. Preliminary experimental results show that the proposed scheme is efficient and effective.

  19. A Wavelet-Based Approach to Fall Detection

    OpenAIRE

    Luca Palmerini; Fabio Bagalà; Andrea Zanetti; Jochen Klenk; Clemens Becker; Angelo Cappello

    2015-01-01

    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 ...

  20. A Robust Wavelet Based Watermarking System for Color Video

    Directory of Open Access Journals (Sweden)

    Mohsen Ashourian

    2011-09-01

    Full Text Available In this paper, we propose a wavelet based watermarking system. The system uses wavelet transform for red, green and blues channel independently. We use space-time coding for encoding the watermark message before data embedding. The bit-error-rate of the recovered message is calculated. The embedding factor is selected in such a way that the host video maintains the same quality with/without using space-time coding. The developed system is further examined, when host video faces compression and noise addition. The result shows the effectiveness of the proposed watermarking system, especially when space-time coding is used.

  1. Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

    OpenAIRE

    Hong He; Yonghong Tan; Yuexia Wang

    2015-01-01

    The selection of an appropriate wavelet is an essential issue that should be addressed in the wavelet-based filtering of electrocardiogram (ECG) signals. Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion Ecom based on multiple criteria related to entropy and energy is proposed in this paper to search for an optimal base wavelet for a specific ECG signal. Taking account of the decomposition capability of wavelets and t...

  2. WAVELET BASED SPECTRAL CORRELATION METHOD FOR DPSK CHIP RATE ESTIMATION

    Institute of Scientific and Technical Information of China (English)

    Li Yingxiang; Xiao Xianci; Tai Hengming

    2004-01-01

    A wavelet-based spectral correlation algorithm to detect and estimate BPSK signal chip rate is proposed. Simulation results show that the proposed method can correctly estimate the BPSK signal chip rate, which may be corrupted by the quadratic characteristics of the spectral correlation function, in a low SNR environment.

  3. Medical Image Fusion via an Effective Wavelet-Based Approach

    Directory of Open Access Journals (Sweden)

    Park DongSun

    2010-01-01

    Full Text Available A novel wavelet-based approach for medical image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS but also the physical meaning of the wavelet coefficients. After the medical images to be fused are decomposed by the wavelet transform, different-fusion schemes for combining the coefficients are proposed: coefficients in low-frequency band are selected with a visibility-based scheme, and coefficients in high-frequency bands are selected with a variance based method. To overcome the presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing fusion methods are carried out in the paper. Experimental results on simulated and real medical images indicate that the proposed method is effective and can get satisfactory fusion results.

  4. On the equivalence of brushlet and wavelet bases

    DEFF Research Database (Denmark)

    Nielsen, Morten; Borup, Lasse

    2005-01-01

    We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results on nonl...

  5. 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.

  6. 3D Wavelet-Based Filter and Method

    Science.gov (United States)

    Moss, William C.; Haase, Sebastian; Sedat, John W.

    2008-08-12

    A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.

  7. On the equivalence of brushlet and wavelet bases

    DEFF Research Database (Denmark)

    Borup, Lasse; Nielsen, Morten

    We prove that the Meyer wavelet basis and a class of brushlet systems associated with exponential type partitions of the frequency axis form a family of equivalent (unconditional) bases for the Besov and Triebel-Lizorkin function spaces. This equivalence is then used to obtain new results...

  8. Single Epoch GPS Deformation Signals Extraction and Gross Error Detection Technique Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    WANG Jian; GAO Jingxiang; XU Changhui

    2006-01-01

    Wavelet theory is efficient as an adequate tool for analyzing single epoch GPS deformation signal. Wavelet analysis technique on gross error detection and recovery is advanced. Criteria of wavelet function choosing and Mallat decomposition levels decision are discussed. An effective deformation signal extracting method is proposed, that is wavelet noise reduction technique considering gross error recovery, which combines wavelet multi-resolution gross error detection results. Time position recognizing of gross errors and their repairing performance are realized. In the experiment, compactly supported orthogonal wavelet with short support block is more efficient than the longer one when discerning gross errors, which can obtain more finely analyses. And the shape of discerned gross error of short support wavelet is simpler than that of the longer one. Meanwhile, the time scale is easier to identify.

  9. Analytical development of vibration signals analysis using D G H M multi wavelet system for local fault and transient phenomena detection

    International Nuclear Information System (INIS)

    In this paper vibration analysis for local faults and transient phenomena detection, using multi wavelet systems is developed. Unlike the scalar wavelet systems in which their coefficients are scalar parameters, the transformation parameters of multi wavelet systems are vector valued, and their calculation required some special techniques. In this investigation, having considered the technique used to obtain the scalar wavelet systems and frequency analysis techniques. The combination of the contributions of the analyzed signal at different levels of the multi scale and multi wavelet function spaces results in original signal which shows the validity of the results. One of the main advantages of the multi wavelet systems over the scalar wavelet systems is their ability to analyze the signal in more frequency intervals. Using this property, the detection of the local and transient phenomena from the vibration signals, which may be caused by a small and local defects in a mechanical system, may be performed more efficiently

  10. Fast wavelet based sparse approximate inverse preconditioner

    Energy Technology Data Exchange (ETDEWEB)

    Wan, W.L. [Univ. of California, Los Angeles, CA (United States)

    1996-12-31

    Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

  11. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  12. Fast Wavelet-Based Visual Classification

    CERN Document Server

    Yu, Guoshen

    2008-01-01

    We investigate a biologically motivated approach to fast visual classification, directly inspired by the recent work of Serre et al. Specifically, trading-off biological accuracy for computational efficiency, we explore using wavelet and grouplet-like transforms to parallel the tuning of visual cortex V1 and V2 cells, alternated with max operations to achieve scale and translation invariance. A feature selection procedure is applied during learning to accelerate recognition. We introduce a simple attention-like feedback mechanism, significantly improving recognition and robustness in multiple-object scenes. In experiments, the proposed algorithm achieves or exceeds state-of-the-art success rate on object recognition, texture and satellite image classification, language identification and sound classification.

  13. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  14. Wavelet Based Fractal Analysis of Airborne Pollen

    CERN Document Server

    Degaudenzi, M E

    1999-01-01

    The most abundant biological particles in the atmosphere are pollen grains and spores. Self protection of pollen allergy is possible through the information of future pollen contents in the air. In spite of the importance of airborne pol len concentration forecasting, it has not been possible to predict the pollen concentrations with great accuracy, and about 25% of the daily pollen forecasts have resulted in failures. Previous analysis of the dynamic characteristics of atmospheric pollen time series indicate that the system can be described by a low dimensional chaotic map. We apply the wavelet transform to study the multifractal characteristics of an a irborne pollen time series. We find the persistence behaviour associated to low pollen concentration values and to the most rare events of highest pollen co ncentration values. The information and the correlation dimensions correspond to a chaotic system showing loss of information with time evolution.

  15. A wavelet-based approach to fall detection.

    Science.gov (United States)

    Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo

    2015-01-01

    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. PMID:26007719

  16. 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.

  17. Wavelet-based multicomponent matching pursuit trace interpolation

    Science.gov (United States)

    Choi, Jihun; Byun, Joongmoo; Seol, Soon Jee; Kim, Young

    2016-09-01

    Typically, seismic data are sparsely and irregularly sampled due to limitations in the survey environment and these cause problems for key seismic processing steps such as surface-related multiple elimination or wave-equation-based migration. Various interpolation techniques have been developed to alleviate the problems caused by sparse and irregular sampling. Among many interpolation techniques, matching pursuit interpolation is a robust tool to interpolate the regularly sampled data with large receiver separation such as crossline data in marine seismic acquisition when both pressure and particle velocity data are used. Multicomponent matching pursuit methods generally used the sinusoidal basis function, which have shown to be effective for interpolating multicomponent marine seismic data in the crossline direction. In this paper, we report the use of wavelet basis functions which further enhances the performance of matching pursuit methods for de-aliasing than sinusoidal basis functions. We also found that the range of the peak wavenumber of the wavelet is critical to the stability of the interpolation results and the de-aliasing performance and that the range should be determined based on Nyquist criteria. In addition, we reduced the computational cost by adopting the inner product of the wavelet and the input data to find the parameters of the wavelet basis function instead of using L-2 norm minimization. Using synthetic data, we illustrate that for aliased data, wavelet-based matching pursuit interpolation yields more stable results than sinusoidal function-based one when we use not only pressure data only but also both pressure and particle velocity together.

  18. Congestion detection within multi-service TCP/IP networks using wavelets.

    OpenAIRE

    Jarrett, W. O. B.

    2004-01-01

    Using passive observation within the multi-service TCP/IP networking domain, we have developed a methodology that associates the frequency composition of composite traffic signals with the packet transmission mechanisms of TCP. At the core of our design is the Discrete Wavelet Transform (DWT), used to temporally localise the frequency variations of a signal. Our design exploits transmission mechanisms (including Fast Retransmit/Fast Recovery, Congestion Avoidance, Slow start, and Retransmissi...

  19. Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification

    Institute of Scientific and Technical Information of China (English)

    Kun Tan; Peijun Du

    2011-01-01

    @@ Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four principal components from principal component analysis and wavelet texture provides the highest accuracy (97.06%). Multi-kernel SVM is therefore an effective approach to improve the accuracy of hyperspectral image classification and to expand possibilities for remote sensing image interpretation and application.%Many remote sensing image classifiers are limited in their ability to combine spectral features with spatial features. Multi-kernel classifiers, however, are capable of integrating spectral features with spatial or structural features using multiple kernels and summing them for final outputs. Using a support vector machine (SVM) as classifier, different multi-kernel classifiers are constructed and tested using 64-band Operational Modular Imaging Spectrometer Ⅱ hyperspectral image of Changping Area, Beijing City. Results show that by integrating spectral and wavelet texture information, multi-kernel SVM classifiers can obtain more accurate classification results than sole-kernel SVM classifiers and cross-information SVM kernel classifiers. Moreover, when the multi-kernel SVM classifier is used, the combination of the first four

  20. UNIFORM ANALYTIC CONSTRUCTION OF WAVELET ANALYSIS FILTERS BASED ON SINE AND COSINE TRIGONOMETRIC FUNCTIONS

    Institute of Scientific and Technical Information of China (English)

    李建平; 唐远炎; 严中洪; 张万萍

    2001-01-01

    Based on sine and cosine functions, the compactly supported orthogonal wavelet filter coefficients with arbitrary length are constructed for the first time. When/N = 2k- 1 and N = 2k , the unified analytic constructions of orthogonal wavelet filters are put forward,respectively. The famous Daubechies filter and some other well-known wavelet filters are tested by the proposed novel method which is very useful for wavelet theory research and many application areas such as pattern recognition.

  1. THE CONSTRUCTION OF WAVELET-BASED TRUNCATED CONICAL SHELL ELEMENT USING B-SPLINE WAVELET ON THE INTERVAL

    Institute of Scientific and Technical Information of China (English)

    Xiang Jiawei; He Zhengjia; Chen Xuefeng

    2006-01-01

    Based on B-spline wavelet on the interval (BSWI), two classes of truncated conical shell elements were constructed to solve axisymmetric problems, i.e. BSWI thin truncated conical shell element and BSWI moderately thick truncated conical shell element with independent slopedeformation interpolation. In the construction of wavelet-based element, instead of traditional polynomial interpolation, the scaling functions of BSWI were employed to form the shape functions through the constructed elemental transformation matrix, and then construct BSWI element via the variational principle. Unlike the process of direct wavelets adding in the wavelet Galerkin method, the elemental displacement field represented by the coefficients of wavelets expansion was transformed into edges and internal modes via the constructed transformation matrix. BSWI element combines the accuracy of B-spline function approximation and various wavelet-based lements for structural analysis. Some static and dynamic numerical examples of conical shells were studied to demonstrate the present element with higher efficiency and precision than the traditional element.

  2. A NOVEL BIOMETRICS TRIGGERED WATERMARKING OF IMAGES BASED ON WAVELET BASED CONTOURLET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Elakkiya Soundar

    2013-01-01

    Full Text Available The rapid development of network and digital technology has led to several issues to the digital content. The technical solution to provide law enforcement and copyright protection is achieved by digital watermarking Digital watermarking is the process of embedding information into a digital image in a way that is difficult to remove. The proposed method contains following phases (i Pre-processing of biometric image (ii key generation from the biometrics of the owner/user and randomization of the host image using Speeded-Up Robust Features (SURF (iii Wavelet-Based Contourlet Transform (WBCT is applied on the host image. The WBCT can give the anisotropy optimal representation of the edges and contours in the image by virtue of the characteristics of multi-scale framework and multi-directionality (iv Singular Value Decomposition (SVD is enforced over the watermark image (v Embedding of the host image with the watermark image. The comparative analysis confirms the efficiency and robustness of the proposed system Index Terms— Digital Watermarking, copyright, Pre-processing, wavelet, Speeded-Up Robust Features.

  3. Analysis of a wavelet-based robust hash algorithm

    Science.gov (United States)

    Meixner, Albert; Uhl, Andreas

    2004-06-01

    This paper paper is a quantitative evaluation of a wavelet-based, robust authentication hashing algorithm. Based on the results of a series of robustness and tampering sensitivity tests, we describepossible shortcomings and propose variousmodifications to the algorithm to improve its performance. The second part of the paper describes and attack against the scheme. It allows an attacker to modify a tampered image, such that it's hash value closely matches the hash value of the original.

  4. Functional Bipartite Ranking: a Wavelet-Based Filtering Approach

    OpenAIRE

    Clémençon, Stéphan; Depecker, Marine

    2013-01-01

    It is the main goal of this article to address the bipartite ranking issue from the perspective of functional data analysis (FDA). Given a training set of independent realizations of a (possibly sampled) second-order random function with a (locally) smooth autocorrelation structure and to which a binary label is randomly assigned, the objective is to learn a scoring function s with optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it is shown how to select compact fin...

  5. Adaptive Wavelets Based on Second Generation Wavelet Transform and Their Applications to Trend Analysis and Prediction

    Institute of Scientific and Technical Information of China (English)

    DUAN Chen-dong; JIANG Hong-kai; HE Zheng-jia

    2004-01-01

    In order to make trend analysis and prediction to acquisition data in a mechanical equipment condition monitoring system, a new method of trend feature extraction and prediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisition data by means of second generation wavelet transform (SGWT). Firstly, taking the vanishing moment number of the predictor as a constraint, the linear predictor and updater are designed according to the acquisition data by using symmetrical interpolating scheme. Then the trend of the data is obtained through doing SGWT decomposition, threshold processing and SGWT reconstruction. Secondly, under the constraint of the vanishing moment number of the predictor, another predictor based on the acquisition data is devised to predict the future trend of the data using a non-symmetrical interpolating scheme. A one-step prediction algorithm is presented to predict the future evolution trend with historical data. The proposed method obtained a desirable effect in peak-to-peak value trend analysis for a machine set in an oil refinery.

  6. Wavelet packet transform-based robust video watermarking technique

    Indian Academy of Sciences (India)

    Gaurav Bhatnagar; Balasubrmanian Raman

    2012-06-01

    In this paper, a wavelet packet transform (WPT)-based robust video watermarking algorithm is proposed. A visible meaningful binary image is used as the watermark. First, sequent frames are extracted from the video clip. Then, WPT is applied on each frame and from each orientation one sub-band is selected based on block mean intensity value called robust sub-band. Watermark is embedded in the robust sub-bands based on the relationship between wavelet packet coefficient and its 8-neighbour $(D_8)$ coefficients considering the robustness and invisibility. Experimental results and comparison with existing algorithms show the robustness and the better performance of the proposed algorithm.

  7. [Epileptic EEG signal classification based on wavelet packet transform and multivariate multiscale entropy].

    Science.gov (United States)

    Xu, Yonghong; Li, Xingxing; Zhao, Yong

    2013-10-01

    In this paper, a new method combining wavelet packet transform and multivariate multiscale entropy for the classification of epilepsy EEG signals is introduced. Firstly, the original EEG signals are decomposed at multi-scales with the wavelet packet transform, and the wavelet packet coefficients of the required frequency bands are extracted. Secondly, the wavelet packet coefficients are processed with multivariate multiscale entropy algorithm. Finally, the EEG data are classified by support vector machines (SVM). The experimental results on the international public Bonn epilepsy EEG dataset show that the proposed method can efficiently extract epileptic features and the accuracy of classification result is satisfactory. PMID:24459973

  8. 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.

  9. Enhanced ATM Security using Biometric Authentication and Wavelet Based AES

    Directory of Open Access Journals (Sweden)

    Sreedharan Ajish

    2016-01-01

    Full Text Available The traditional ATM terminal customer recognition systems rely only on bank cards, passwords and such identity verification methods are not perfect and functions are too single. Biometrics-based authentication offers several advantages over other authentication methods, there has been a significant surge in the use of biometrics for user authentication in recent years. This paper presents a highly secured ATM banking system using biometric authentication and wavelet based Advanced Encryption Standard (AES algorithm. Two levels of security are provided in this proposed design. Firstly we consider the security level at the client side by providing biometric authentication scheme along with a password of 4-digit long. Biometric authentication is achieved by considering the fingerprint image of the client. Secondly we ensure a secured communication link between the client machine to the bank server using an optimized energy efficient and wavelet based AES processor. The fingerprint image is the data for encryption process and 4-digit long password is the symmetric key for the encryption process. The performance of ATM machine depends on ultra-high-speed encryption, very low power consumption, and algorithmic integrity. To get a low power consuming and ultra-high speed encryption at the ATM machine, an optimized and wavelet based AES algorithm is proposed. In this system biometric and cryptography techniques are used together for personal identity authentication to improve the security level. The design of the wavelet based AES processor is simulated and the design of the energy efficient AES processor is simulated in Quartus-II software. Simulation results ensure its proper functionality. A comparison among other research works proves its superiority.

  10. Double-Wavelet Neuron Based on Analytical Activation Functions

    OpenAIRE

    Bodyanskiy, Yevgeniy; Lamonova, Nataliya; Vynokurova, Olena

    2007-01-01

    In this paper a new double-wavelet neuron architecture obtained by modification of standard wavelet neuron, and its learning algorithm are proposed. The offered architecture allows to improve the approximation properties of wavelet neuron. Double-wavelet neuron and its learning algorithm are examined for forecasting non-stationary chaotic time series.

  11. Representation of 1/f signal with wavelet bases

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The representation of 1/f signal with wavelet transformation is explored. It is shown that a class of 1/f signal can be represented via wavelet synthetic formula and that a statistically self-similar property of signals may be characterized by the correlation functions of wavelet coefficients in the wavelet domain.

  12. Continuous wavelet transform and discrete multi-resolution analysis of surface fluxes and atmospheric stability

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Variations of land surface fluxes of sensible heat (H), latent heat ( LE ), and CO2(F-CO2) obtained from the eddy-covariance measurements above a winter wheat field from March 30 to April 24, 2001 have been studied at scales ranging from 10 minutes to days. Wavelet transform is used in the analysis of land surface fluxes and atmospheric stability (ζ) calculated from the measurements to reveal the changes in land surface fluxes in hours to days scales. The main results are: (1) Concise and compact information about the fluxes, net radiation (Rn), temperature (T) and ζ in the scale-time domain are extracted from the data by continuous wavelet analysis,and 1 day, 0.5 day and short-period (shorter than 0.5 day) components are revealed. Continuous wavelet coefficients can be used to characterize periodic components of changes in fluxes and ζ. (2) Discrete-time multi-resolution analysis can be used to concentrate total energy variance of time series of the measurements to a small number of coefficients, plotting the relative energy distribution to get several meaningful characteristics of the data. (3) Under neutral atmospheric conditions, the relative energy distributions of the Haar multi-resolution analysis of the three non-dimensional coefficients (T/T* , q/q * and c/c * ) display clear similarities.

  13. WAVELET BASED CLASSIFICATION OF VOLTAGE SAG, SWELL & TRANSIENTS

    Directory of Open Access Journals (Sweden)

    Vijay Gajanan Neve

    2013-05-01

    Full Text Available When the time localization of the spectral components is needed, the WAVELE TRANSFORM (WT can be used to obtain the optimal time frequency representation of the signal. This paper deals with the use of a wavelet transform to detect and analyze voltage sags, voltage swell and transients. It introduces voltage disturbance detection approach based on wavelet transform, identifies voltage disturbances, and discriminates the type of event which has resulted in the voltage disturbance, e.g. either a fault or a capacitor-switching incident.Feasibility of the proposed disturbance detection approach is demonstrated based on digital time-domain simulation of a distribution power system using the PSCAD software package, and is implemented using MATLAB. The developed algorithm has been applied to the 14-buses IEEE system to illustrate its application. Results are analyzed.

  14. Wavelet-based deconvolution of ultrasonic signals in nondestructive evaluation

    Institute of Scientific and Technical Information of China (English)

    HERRERA Roberto Henry; OROZCO Rubén; RODRIGUEZ Manuel

    2006-01-01

    In this paper, the inverse problem of reconstructing reflectivity function of a medium is examined within a blind deconvolution framework. The ultrasound pulse is estimated using higher-order statistics, and Wiener filter is used to obtain the ultrasonic reflectivity function through wavelet-based models. A new approach to the parameter estimation of the inverse filtering step is proposed in the nondestructive evaluation field, which is based on the theory of Fourier-Wavelet regularized deconvolution (ForWaRD). This new approach can be viewed as a solution to the open problem of adaptation of the ForWaRD framework to perform the convolution kernel estimation and deconvolution interdependently. The results indicate stable solutions of the estimated pulse and an improvement in the radio-frequency (RF) signal taking into account its signal-to-noise ratio (SNR) and axial resolution. Simulations and experiments showed that the proposed approach can provide robust and optimal estimates of the reflectivity function.

  15. Face Recognition Approach Based on Wavelet - Curvelet Technique

    Directory of Open Access Journals (Sweden)

    Muzhir Shaban Al-Ani

    2012-04-01

    Full Text Available In this paper, a novel face recognition approach based on wavelet-curvelet technique, is proposed. This algorithm based on the similarities embedded in the images, That utilize the wavelet-curvelet technique to extract facial features. The implemented technique can overcome on the other mathematical image analysis approaches. This approaches may suffered from the potential for a high dimensional feature space, Therefore it aims to reduce the dimensionality that reduce the required computational power and memory size. Then the Nearest Mean Classifier (NMC is adopted to recognize different faces. In this work, three major experiments were done. two face databases (MAFD & ORL, and higher recognition rate is obtained by the implementation of this techniques.

  16. SVD-based digital image watermarking using complex wavelet transform

    Indian Academy of Sciences (India)

    A Mansouri; A Mahmoudi Aznaveh; F Torkamani Azar

    2009-06-01

    A new robust method of non-blind image watermarking is proposed in this paper. The suggested method is performed by modification on singular value decomposition (SVD) of images in Complex Wavelet Transform (CWT) domain while CWT provides higher capacity than the real wavelet domain. Modification of the appropriate sub-bands leads to a watermarking scheme which favourably preserves the quality. The additional advantage of the proposed technique is its robustness against the most of common attacks. Analysis and experimental results show much improved performance of the proposed method in comparison with the pure SVD-based as well as hybrid methods (e.g. DWT-SVD as the recent best SVD-based scheme).

  17. Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space.

    Science.gov (United States)

    Kim, Won Hwa; Adluru, Nagesh; Chung, Moo K; Charchut, Sylvia; GadElkarim, Johnson J; Altshuler, Lori; Moody, Teena; Kumar, Anand; Singh, Vikas; Leow, Alex D

    2013-01-01

    Advances in resting state fMRI and diffusion weighted imaging (DWI) have led to much interest in studies that evaluate hypotheses focused on how brain connectivity networks show variations across clinically disparate groups. However, various sources of error (e.g., tractography errors, magnetic field distortion, and motion artifacts) leak into the data, and make downstream statistical analysis problematic. In small sample size studies, such noise have an unfortunate effect that the differential signal may not be identifiable and so the null hypothesis cannot be rejected. Traditionally, smoothing is often used to filter out noise. But the construction of convolving with a Gaussian kernel is not well understood on arbitrarily connected graphs. Furthermore, there are no direct analogues of scale-space theory for graphs--ones which allow to view the signal at multiple resolutions. We provide rigorous frameworks for performing 'multi-resolutional' analysis on brain connectivity graphs. These are based on the recent theory of non-Euclidean wavelets. We provide strong evidence, on brain connectivity data from a network analysis study (structural connectivity differences in adult euthymic bipolar subjects), that the proposed algorithm allows identifying statistically significant network variations, which are clinically meaningful, where classical statistical tests, if applied directly, fail.

  18. Pulse filtering and correction for CZT detectors using simple digital algorithms based on the wavelet transform

    International Nuclear Information System (INIS)

    The authors report an approach to double gaussian filtering used in classical works as dual parameter pulse processing. This technique has been implemented by creating a bank of gaussian-like digital filters based on wavelet transforms. A simple method to correct for the charge loss inherent to room temperature semiconductor gamma detectors has been developed. This method is based on multi-resolution signal analysis. Results are reported from tests of these algorithms on commercial CZT detectors and two trapped hole charge correction levels are compared. Finally, the advantages and limitations of this new approach to detector pulse processing are discussed

  19. Wavelet-based Image Compression Using Human Visual System Models

    OpenAIRE

    Beegan, Andrew Peter

    2001-01-01

    Recent research in transform-based image compression has focused on the wavelet transform due to its superior performance over other transforms. Performance is often measured solely in terms of peak signal-to-noise ratio (PSNR) and compression algorithms are optimized for this quantitative metric. The performance in terms of subjective quality is typically not evaluated. Moreover, the sensitivities of the human visual system (HVS) are often not incorporated into compression schemes. ...

  20. Fast wavelet based algorithms for linear evolution equations

    Science.gov (United States)

    Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

    1992-01-01

    A class was devised of fast wavelet based algorithms for linear evolution equations whose coefficients are time independent. The method draws on the work of Beylkin, Coifman, and Rokhlin which they applied to general Calderon-Zygmund type integral operators. A modification of their idea is applied to linear hyperbolic and parabolic equations, with spatially varying coefficients. A significant speedup over standard methods is obtained when applied to hyperbolic equations in one space dimension and parabolic equations in multidimensions.

  1. 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.

  2. Wavelet packet based feature extraction and recognition of license plate characters

    Institute of Scientific and Technical Information of China (English)

    HUANG Wei; LU Xiaobo; LING Xiaojing

    2005-01-01

    To study the characteristics of license plate characters recognition, this paper proposes a method for feature extraction of license plate characters based on two-dimensional wavelet packet. We decompose license plate character images with two dimensional-wavelet packet and search for the optimal wavelet packet basis. This paper presents a criterion of searching for the optimal wavelet packet basis, and a practical algorithm. The obtained optimal wavelet packet basis is used as the feature of license plate character, and a BP neural network is used to classify the character.The testing results show that the proposed method achieved higher recognition rate than the traditional methods.

  3. A wavelet transformation approach for multi-source gravity fusion: Applications and uncertainty tests

    Science.gov (United States)

    Bai, Yongliang; Dong, Dongdong; Wu, Shiguo; Liu, Zhan; Zhang, Guangxu; Xu, Kaijun

    2016-05-01

    Gravity anomalies detected by different measurement platforms have different characteristics and advantages. There are different kinds of gravity data fusion methods for generating single gravity anomaly map with a rich and accurate spectral content. Former studies using wavelet based gravity fusion method which is a newly developed approach did not pay more attention to the fusion uncertainties. In this paper, we firstly introduce the wavelet based gravity fusion method, and then apply this method to one synthetic model and also to the northern margin of the South China Sea. Wavelet type and the decomposition level are two input parameters for this fusion method, and the uncertainty tests show that fusion results are more sensitive to wavelet type than the decomposition level. The optimal application result of the fusion methodology on the synthetic model is closer to the true anomaly field than either of the simulated shipborne anomaly and altimetry-based anomaly grid. The best fusion result on the northern margin of the South China Sea is based on the 'rbio1.3' wavelet and four-level decomposition. The fusion result contains more accurate short-wavelength anomalies than the altimetry-based gravity anomalies along ship tracks, and it also has more accurate long wavelength characteristics than the shipborne gravity anomalies between ship tracks. The real application case shows that the fusion result has better correspondences to the seafloor topography variations and sub-surface structures than each of the two input gravity anomaly maps (shipborne based gravity anomaly map and altimetry based gravity anomaly map). Therefore, it is possible to map and detect more precise seafloor topography and geologic structures by the new gravity anomaly map.

  4. Remote Sensing Image Resolution Enlargement Algorithm Based on Wavelet Transformation

    Directory of Open Access Journals (Sweden)

    Samiul Azam

    2014-05-01

    Full Text Available In this paper, we present a new image resolution enhancement algorithm based on cycle spinning and stationary wavelet subband padding. The proposed technique or algorithm uses stationary wavelet transformation (SWT to decompose the low resolution (LR image into frequency subbands. All these frequency subbands are interpolated using either bicubic or lanczos interpolation, and these interpolated subbands are put into inverse SWT process for generating intermediate high resolution (HR image. Finally, cycle spinning (CS is applied on this intermediate high resolution image for reducing blocking artifacts, followed by, traditional Laplacian sharpening filter is used to make the generated high resolution image sharper. This new technique has been tested on several satellite images. Experimental result shows that the proposed technique outperforms the conventional and the state-of-the-art techniques in terms of peak signal to noise ratio, root mean square error, entropy, as well as, visual perspective.

  5. REANALYSIS OF BER FOR WAVELET BASED MC-CDMA COMMUNICATION

    Directory of Open Access Journals (Sweden)

    Anil Kumar Dubey

    2011-05-01

    Full Text Available As demand for higher data rates is continuously rising,there is always a need to develop more efficientwireless communication systems. The workdescribed in this paper is my effort in thisdirection. We developed and evaluated a waveletpacket based multicarrier CDMA wirelesscommunication system. In this system design a set ofwavelet packets are used as the modulation waveformsin a multicarrier CDMA system. The need for cyclic prefix is eliminated in the system design due to the good orthogonality and time-frequency localization properties of the wavelet packets.Wavelet Packets have good properties such as orthogonality and multirate flexibility, and have resulted in a number of works for its applications to code division multiple access communications.

  6. Complete quantum circuit of Haar wavelet based MRA

    Institute of Scientific and Technical Information of China (English)

    HE Yuguo; SUN Jigui

    2005-01-01

    Wavelet analysis has applications in many areas, such as signal analysis and image processing. We propose a method for generating the complete circuit of Haar wavelet based MRA by factoring butterfly matrices and conditional perfect shuffle permutation matrices. The factorization of butterfly matrices is the essential part of the design. As a result, it is the key point to obtain the circuits of .I2t()W()I2n-2t-2. In this paper, we use a simple means to develop quantum circuits for this kind of matrices. Similarly, the conditional permutation matrix is implemented entirely, combined with the scheme of Fijany and Williams. The cir-cuits and the ideas adopted in the design are simple and in-telligible.

  7. Diffusion filtering in image processing based on wavelet transform

    Institute of Scientific and Technical Information of China (English)

    LIU Feng

    2006-01-01

    The nonlinear diffusion filtering in image processing bases on the heat diffusion equations. Its key is the control of diffusion amount. In the previous models, the diffusivity depends on the gradients of images. So it is easily affected by noises. This paper first gives a new multiscale computational technique for diffusivity. Then we proposed a class of nonlinear wavelet diffusion (NWD) models that are used to restore images. The NWD model has strong ability to resist noise.But it, like the previous models, requires higher computational effort. Thus, by simplifying the NWD, we establish linear wavelet diffusion (LWD) models that consist of advection and diffusion. Since there exists the advection, the LWD filter is anisotropic, and hence can well preserve edges although the diffusion at edges is isotropic. The advantage is that the LWD model is easy to be analyzed and has lesser computational load. Finally, a variety of numerical experiments compared with the previous model are shown.

  8. Wavelet-based gray-level digital image watermarking

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The watermarking technique has been proposed as a method by hiding secret information into the im age to protect the copyright of multimedia data. But most previous work focuses on the algorithms of embedding one-dimensional watermarks or two-dimensional binary digital watermarks. In this paper, a wavelet-based method for embedding a gray-level digital watermark into an image is proposed. By still image decomposition technique, a gray-level digital watermark is decompounded into a series of bitplanes. By discrete wavelet transform ( DWT ), the host image is decomposed into multiresolution representations with hierarchical structure. Thedifferent bitplanes of the gray-level watermark is embedded into the corresponding resolution of the decomposed host image. The experimental results show that the proposed techniques can successfully survive image processing operations and the lossy compression techniques such as Joint Photographic Experts Group (JPEG).

  9. Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems.

    Science.gov (United States)

    Kim, Won Hwa; Chung, Moo K; Singh, Vikas

    2013-01-01

    The analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.

  10. FPGA based wavelet trigger in radio detection of cosmic rays

    Energy Technology Data Exchange (ETDEWEB)

    Szadkowski, Zbigniew, E-mail: zszadkow@uni.lodz.pl [Department of Physics and Applied Informatics, University of Ł´od´z, Lodz (Poland); Szadkowska, Anna [Center of Mathematics and Physics, Ł´od´z, University of Technology, Lodz (Poland)

    2014-07-01

    Experiments which show coherent radio emission from extensive air showers induced by ultra-high-energy cosmic rays are designed for a detailed study of the development of the electromagnetic part of air showers. Radio detectors can operate with 100 % up time as, e.g., surface detectors based on water-Cherenkov tanks. They are being developed for ground-based experiments (e.g., the Pierre Auger Observatory) as another type of air-shower detector in addition to fluorescence detectors, which operate with only ∼10 % of duty on dark nights. The radio signals from air showers are caused by coherent emission from geomagnetic radiation and charge-excess processes. The self-triggers in radio detectors currently in use often generate a dense stream of data, which is analyzed afterwards. Huge amounts of registered data require significant manpower for off-line analysis. Improvement of trigger efficiency is a relevant factor. The wavelet trigger, which investigates on-line the power of radio signals (∼ V 2/R), is promising; however, it requires some improvements with respect to current designs. In this work, Morlet wavelets with various scaling factors were used for an analysis of real data from the Auger Engineering Radio Array and for optimization of the utilization of the resources in an FPGA. The wavelet analysis showed that the power of events is concentrated mostly in a limited range of the frequency spectrum (consistent with a range imposed by the input analog band-pass filter). However, we found several events with suspicious spectral characteristics, where the signal power is spread over the full band-width sampled by a 200 MHz digitizer with significant contribution of very high and very low frequencies. These events may not originate from cosmic ray showers but could be the result of human contamination. The engine of the wavelet analysis can be implemented in the modern powerful FPGAs and can remove suspicious events on-line to reduce the trigger rate. (author)

  11. Improving 3D Wavelet-Based Compression of Hyperspectral Images

    Science.gov (United States)

    Klimesh, Matthew; Kiely, Aaron; Xie, Hua; Aranki, Nazeeh

    2009-01-01

    Two methods of increasing the effectiveness of three-dimensional (3D) wavelet-based compression of hyperspectral images have been developed. (As used here, images signifies both images and digital data representing images.) The methods are oriented toward reducing or eliminating detrimental effects of a phenomenon, referred to as spectral ringing, that is described below. In 3D wavelet-based compression, an image is represented by a multiresolution wavelet decomposition consisting of several subbands obtained by applying wavelet transforms in the two spatial dimensions corresponding to the two spatial coordinate axes of the image plane, and by applying wavelet transforms in the spectral dimension. Spectral ringing is named after the more familiar spatial ringing (spurious spatial oscillations) that can be seen parallel to and near edges in ordinary images reconstructed from compressed data. These ringing phenomena are attributable to effects of quantization. In hyperspectral data, the individual spectral bands play the role of edges, causing spurious oscillations to occur in the spectral dimension. In the absence of such corrective measures as the present two methods, spectral ringing can manifest itself as systematic biases in some reconstructed spectral bands and can reduce the effectiveness of compression of spatially-low-pass subbands. One of the two methods is denoted mean subtraction. The basic idea of this method is to subtract mean values from spatial planes of spatially low-pass subbands prior to encoding, because (a) such spatial planes often have mean values that are far from zero and (b) zero-mean data are better suited for compression by methods that are effective for subbands of two-dimensional (2D) images. In this method, after the 3D wavelet decomposition is performed, mean values are computed for and subtracted from each spatial plane of each spatially-low-pass subband. The resulting data are converted to sign-magnitude form and compressed in a

  12. Wavelet-based localization of oscillatory sources from magnetoencephalography data.

    Science.gov (United States)

    Lina, J M; Chowdhury, R; Lemay, E; Kobayashi, E; Grova, C

    2014-08-01

    Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy.

  13. Wavelet and ANN Based Relaying for Power Transformer Protection

    Directory of Open Access Journals (Sweden)

    S. Sudha

    2007-01-01

    Full Text Available This paper presents an efficient wavelet and neural network (WNN based algorithm for distinguishing magnetizing inrush currents from internal fault currents in three phase power transformers. The wavelet transform is applied first to decompose the current signals of the power transformer into a series of detailed wavelet components. The values of the detailed coefficients obtained can accurately discriminate between an internal fault and magnetizing inrush currents in power transformers. The detailed coefficients are further used to train an Artificial Neural Network (ANN. The trained ANN clearly distinguishes an internal fault current from magnetizing inrush current. A typical 750 MVA, 27/420KV, ∆/Y power transformer connected between a 27KV source at the sending end and a 420KV transmission line connected to an infinite bus power system at the receiving end were simulated using PSCAD/EMTDC software. The generated data were used by the MATLAB software to test the performance of the proposed technique. The simulation results obtained show that the new algorithm is more reliable and accurate. It provides a high operating sensitivity for internal faults and remains stable for inrush currents of the power transformers.

  14. A framework for evaluating wavelet based watermarking for scalable coded digital item adaptation attacks

    OpenAIRE

    Bhowmik, D.; Abhayaratne, C.

    2009-01-01

    A framework for evaluating wavelet based watermarking schemes against scalable coded visual media content adaptation attacks is presented. The framework, Watermark Evaluation Bench for Content Adaptation Modes (WEBCAM), aims to facilitate controlled evaluation of wavelet based watermarking schemes under MPEG-21 part-7 digital item adaptations (DIA). WEBCAM accommodates all major wavelet based watermarking in single generalised framework by considering a global parameter space, from which t...

  15. The Brera Multi-scale Wavelet Chandra Survey. The serendipitous source catalogue

    CERN Document Server

    Romano, P; Mignani, R P; Moretti, A; Panzera, M R; Tagliaferri, G; Mottini, M

    2009-01-01

    We present the Brera Multi-scale Wavelet Chandra (BMW-Chandra) source catalogue drawn from essentially all Chandra ACIS-I pointed observations with an exposure time in excess of 10ks public as of March 2003 (136 observations). Using the wavelet detection algorithm developed by Lazzati et al. (1999) and Campana et al. (1999), which can characterise both point-like and extended sources, we identified 21325 sources. Among them, 16758 are serendipitous, i.e. not associated with the targets of the pointings. This makes our catalogue the largest compilation of Chandra sources to date. The 0.5-10keV absorption corrected fluxes of these sources range from 3E-16 to 9E-12 erg/cm2/s with a median of 7E-15 erg/cm2/s. The catalogue consists of count rates and relative errors in three energy bands (total, 0.5-7keV; soft, 0.5-2keV; and hard, 2-7keV), where the detection was performed, and source positions relative to the highest signal-to-noise detection among the three bands. The wavelet algorithm also provides an estimate...

  16. Multi-scale wavelet separation of aeromagnetic anomaly and study of faults in Beijing area

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xian; ZHAO Li; LIU Tian-you; YANG Yu-shan

    2006-01-01

    In this paper, through a multi-scale separation of the aeromagnetic anomaly by wavelet transform technique, we reprocessed the aeromagnetic data collected 20 years ago in Beijing area and analyzed the aeromagnetic anomaly qualitatively, integrating geological structure features in the area. In particular, we studied the spatial distributions of the two main faults called Shunyi-Liangxiang fault and Banqiao-Babaoshan-Tongxian fault, which have cut and gone through the central Beijing area striking in NE and EW directions, respectively. The influences of these two faults on the earthquakes have also been discussed briefly.

  17. Orthogonal Wavelet Transform Dynamic Weighted Multi-modulus Blind Equalization Algorithm Based on the Improved Cuckoo Search Algorithm%基于改进的布谷鸟搜索算法优化的正交小波动态加权多模盲均衡算法

    Institute of Scientific and Technical Information of China (English)

    郑亚强

    2014-01-01

    为了更好地均衡高阶 QAM信号,本文提出了基于改进的布谷鸟搜索算法优化的正交小波动态加权多模盲均衡算法(ICS-WT-DWMMA),利用改进了的布谷鸟搜索算法初始化均衡器的权向量,利用小波变换(WT)降低信号自相关性,其中动态加权多模盲均衡算法(DWMMA)利用由判决符号的指数幂构成的加权项来调整代价函数中的模值。水声信道的MATLAB仿真实验结果表明,与小波加权多模盲均衡算法和小波动态加权多模盲均衡算法比较,新算法收敛速度更快,稳态误差更小。%In order to improve the equalization of high-order QAM signals,the Orthogonal Wavelet Transform Dynamic Weighted Multi-Modulus blind equalization Algorithm based on the Improved Cuck-oo Search Algorithm (ICS-WT-DWMMA)was proposed.It took advantage of the weight vector which improved cuckoo search algorithm initialization of equalizer and the wavelet transform (WT)to reduce the signal autocorrelation.The DWMMA (Dynamic Weighted Multi-Modulus blind equalization Algorithm ) adj usted the modulus in the cost function by weighted term composed of exponent of decision symbol.The MATLAB simulation results of underwater acoustic channel shew that,compared with Wavelet Weighted Multimodulus blind equalization algorithm and wavelet dynamic weighted Multimodulus blind equalization algorithm,the new algorithm had a faster convergence speed and steady-state error was smaller.

  18. A Fast Leak Locating Method Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    GE Chuanhu; YANG Hongying; YE Hao; WANG Guizeng

    2009-01-01

    The problem of leak location is actually a time delay estimation (TDE) problem. Since most exist-ing TDE methods may encounter the problem of high computational complexity when used for online leak location. This paper presents a fast leak locating method based on wavelet transform (WT). The method first gets a rough estimate of the time delay from the WT coefficients of the pressure signals at the largest scale, then keeps refining the estimate using WT coefficients on smaller and smaller scales. Quantitative analyses and test results based on real data show that the method reduces the computational complexity while main-taining the time delay estimation accuracy.

  19. A Regression Analysis Model Based on Wavelet Networks

    Institute of Scientific and Technical Information of China (English)

    XIONG Zheng-feng

    2002-01-01

    In this paper, an approach is proposed to combine wavelet networks and techniques of regression analysis. The resulting wavelet regression estimator is well suited for regression estimation of moderately large dimension, in particular for regressions with localized irregularities.

  20. A ROBUST WAVELET BASED WATERMARKING SCHEME FOR DIGITAL AUDIO

    Directory of Open Access Journals (Sweden)

    Ayad Ibrahim Abdulsada

    2012-06-01

    Full Text Available In this paper, a robust wavelet based watermarking scheme has been proposed for digital audio. A single bit is embedded in the approximation part of each frame. The watermark bits are embedded in two subsets of indexes randomly generated by using two keys for security purpose. The embedding process is done in adaptively fashion according to the mean of each approximation part. The detection of watermark does not depend on the original audio. To measure the robustness of the algorithm, different signal processing operations have been applied on the watermarked audio. Several experimental results have been conducted to illustrate the robustness and efficiency of the proposed watermarked audio scheme.

  1. Multi-scale autocorrelation via morphological wavelet slices for rolling element bearing fault diagnosis

    Science.gov (United States)

    Li, Chuan; Liang, Ming; Zhang, Yi; Hou, Shumin

    2012-08-01

    Fault features of rolling element bearings can be reflected by geometrical structures of the bearing vibration signals. These symptoms, however, often spread over various morphological scales without a known pattern. For this reason, we propose a multi-scale autocorrelation via morphological wavelet slices (MAMWS) approach to detect bearing fault signatures. The vibration measurement of a bearing is decomposed using morphological stationary wavelet with different resolutions of structuring elements. The extracted temporal components are then transformed to form a frequency-domain view of morphological slices by the Fourier transform. Although this three-dimensional representation is more intuitive in terms of fault diagnosis, the existence of the noise may reduce its readability. Hence the autocorrelation function is exploited to produce a multi-scale autocorrelation spectrogram from which the maximal autocorrelation values of all frequencies are aggregated into an ichnographical spectral representation. Accordingly the fault signature is highlighted for easy diagnosis of bearing faults. The effectiveness of the proposed approach has been demonstrated by both the simulation and experimental signal analyses.

  2. An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing

    OpenAIRE

    Hajiaghababa, Fatemeh; Kermani, Saeed; Marateb, Hamid R.

    2014-01-01

    A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech ...

  3. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

    OpenAIRE

    K. Daqrouq; A. Dobaie

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation m...

  4. Wavelet-based detection of transients in biological signals

    Science.gov (United States)

    Mzaik, Tahsin; Jagadeesh, Jogikal M.

    1994-10-01

    This paper presents two multiresolution algorithms for detection and separation of mixed signals using the wavelet transform. The first algorithm allows one to design a mother wavelet and its associated wavelet grid that guarantees the separation of signal components if information about the expected minimum signal time and frequency separation of the individual components is known. The second algorithm expands this idea to design two mother wavelets which are then combined to achieve the required separation otherwise impossible with a single wavelet. Potential applications include many biological signals such as ECG, EKG, and retinal signals.

  5. Wavelet Neural Network Based Traffic Prediction for Next Generation Network

    Institute of Scientific and Technical Information of China (English)

    Zhao Qigang; Li Qunzhan; He Zhengyou

    2005-01-01

    By using netflow traffic collecting technology, some traffic data for analysis are collected from a next generation network (NGN) operator. To build a wavelet basis neural network (NN), the Sigmoid function is replaced with the wavelet in NN. Then the wavelet multiresolution analysis method is used to decompose the traffic signal, and the decomposed component sequences are employed to train the NN. By using the methods, an NGN traffic prediction model is built to predict one day's traffic. The experimental results show that the traffic prediction method of wavelet NN is more accurate than that without using wavelet in the NGN traffic forecasting.

  6. A New Text Location Approach Based Wavelet

    Institute of Scientific and Technical Information of China (English)

    Weihua Li; Zhen Fang; Shuozhong Wang

    2002-01-01

    With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regions in the input image will facilitate the retrieving task, and the optical character recognizer can then be applied to only those regions of the image which contain text. In this paper a new text location method is described, which can be used to locate textual regions from complex image and video frame. Experimental results show that the textual regions in image can be located effectively and quickly.

  7. The Brera Multi-scale Wavelet HRI Cluster Survey: I Selection of the Sample and Number Counts

    CERN Document Server

    Moretti, A; Campana, S; Lazzati, D; Panzera, M R; Tagliaferri, G; Arena, S; Braglia, F; Dell'Antonio, I; Longhetti, M

    2004-01-01

    We describe the construction of the Brera Multi-scale Wavelet (BMW) HRI Cluster Survey, a deep sample of serendipitous X-ray selected clusters of galaxies based on the ROSAT HRI archive. This is the first cluster catalog exploiting the high angular resolution of this instrument. Cluster candidates are selected on the basis of their X-ray extension only, a parameter which is well measured by the BMW wavelet detection algorithm. The survey includes 154 candidates over a total solid angle of ~160 deg2 at 10^{-12}erg s^{-1} cm^{-2} and ~80 deg^2 at 1.8*10^{-13} erg s^{-1}$ cm^{-2}. At the same time, a fairly good sky coverage in the faintest flux bins (3-5*10^{-14}erg s^{-1} cm^{-2}) gives this survey the capability to detect a few clusters with z\\sim 1-1.2, depending on evolution. We present the results of extensive Monte Carlo simulations, providing a complete statistical characterization of the survey selection function and contamination level. We also present a new estimate of the surface density of clusters ...

  8. 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.

  9. Wavelet-based method for computing elastic band gaps of one-dimensional phononic crystals

    Institute of Scientific and Technical Information of China (English)

    YAN; ZhiZhong; WANG; YueSheng

    2007-01-01

    A wavelet-based method was developed to compute elastic band gaps of one-dimensional phononic crystals. The wave field was expanded in the wavelet basis and an equivalent eigenvalue problem was derived in a matrix form involving the adaptive computation of integrals of the wavelets. The method was then applied to a binary system. For comparison, the elastic band gaps of the same one-di- mensional phononic crystals computed with the wavelet method and the well- known plane wave expansion (PWE) method are both presented in this paper. The numerical results of the two methods are in good agreement while the computation costs of the wavelet method are much lower than that of PWE method. In addition, the adaptability of wavelets makes the method possible for efficient band gap computation of more complex phononic structures.

  10. Implemented Wavelet Packet Tree based Denoising Algorithm in Bus Signals of a Wearable Sensorarray

    Science.gov (United States)

    Schimmack, M.; Nguyen, S.; Mercorelli, P.

    2015-11-01

    This paper introduces a thermosensing embedded system with a sensor bus that uses wavelets for the purposes of noise location and denoising. From the principle of the filter bank the measured signal is separated in two bands, low and high frequency. The proposed algorithm identifies the defined noise in these two bands. With the Wavelet Packet Transform as a method of Discrete Wavelet Transform, it is able to decompose and reconstruct bus input signals of a sensor network. Using a seminorm, the noise of a sequence can be detected and located, so that the wavelet basis can be rearranged. This particularly allows for elimination of any incoherent parts that make up unavoidable measuring noise of bus signals. The proposed method was built based on wavelet algorithms from the WaveLab 850 library of the Stanford University (USA). This work gives an insight to the workings of Wavelet Transformation.

  11. A NEW DE-NOISING METHOD BASED ON 3-BAND WAVELET AND NONPARAMETRIC ADAPTIVE ESTIMATION

    Institute of Scientific and Technical Information of China (English)

    Li Li; Peng Yuhua; Yang Mingqiang; Xue Peijun

    2007-01-01

    Wavelet de-noising has been well known as an important method of signal de-noising.Recently,most of the research efforts about wavelet de-noising focus on how to select the threshold,where Donoho method is applied widely.Compared with traditional 2-band wavelet,3-band wavelet has advantages in many aspects.According to this theory,an adaptive signal de-noising method in 3-band wavelet domain based on nonparametric adaptive estimation is proposed.The experimental results show that in 3-band wavelet domain,the proposed method represents better characteristics than Donoho method in protecting detail and improving the signal-to-noise ratio of reconstruction signal.

  12. A Study on Integrated Wavelet Neural Networks in Fault Diagnosis Based on Information Fusion

    Institute of Scientific and Technical Information of China (English)

    ANG Xue-ye

    2007-01-01

    The tight wavelet neural network was constituted by taking the nonlinear Morlet wavelet radices as the excitation function. The idiographic algorithm was presented. It combined the advantages of wavelet analysis and neural networks. The integrated wavelet neural network fault diagnosis system was set up based on both the information fusion technology and actual fault diagnosis, which took the sub-wavelet neural network as primary diagnosis from different sides, then came to the conclusions through decision-making fusion. The realizable policy of the diagnosis system and established principle of the sub-wavelet neural networks were given . It can be deduced from the examples that it takes full advantage of diversified characteristic information, and improves the diagnosis rate.

  13. Model of Information Security Risk Assessment based on Improved Wavelet Neural Network

    OpenAIRE

    Gang Chen; Dawei Zhao

    2013-01-01

    This paper concentrates on the information security risk assessment model utilizing the improved wavelet neural network. The structure of wavelet neural network is similar to the multi-layer neural network, which is a feed-forward neural network with one or more inputs. Afterwards, we point out that the training process of wavelet neural networks is made up of four steps until the value of error function can satisfy a pre-defined error criteria. In order to enhance the quality of information ...

  14. A wavelet-based computational method for solving stochastic Itô–Volterra integral equations

    Energy Technology Data Exchange (ETDEWEB)

    Mohammadi, Fakhrodin, E-mail: f.mohammadi@hormozgan.ac.ir

    2015-10-01

    This paper presents a computational method based on the Chebyshev wavelets for solving stochastic Itô–Volterra integral equations. First, a stochastic operational matrix for the Chebyshev wavelets is presented and a general procedure for forming this matrix is given. Then, the Chebyshev wavelets basis along with this stochastic operational matrix are applied for solving stochastic Itô–Volterra integral equations. Convergence and error analysis of the Chebyshev wavelets basis are investigated. To reveal the accuracy and efficiency of the proposed method some numerical examples are included.

  15. SPATIALLY SCALABLE RESOLUTION IMAGE CODING METHOD WITH MEMORY OPTIMIZATION BASED ON WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Wang Na; Zhang Li; Zhou Xiao'an; Jia Chuanying; Li Xia

    2005-01-01

    This letter exploits fundamental characteristics of a wavelet transform image to form a progressive octave-based spatial resolution. Each wavelet subband is coded based on zeroblock and quardtree partitioning ordering scheme with memory optimization technique. The method proposed in this letter is of low complexity and efficient for Internet plug-in software.

  16. Change detection in multi-temporal remote sensing images based on the wavelet-domain hidden Markov chain model%利用小波域HMC模型进行遥感图像变化检测

    Institute of Scientific and Technical Information of China (English)

    辛芳芳; 焦李成; 王桂婷; 万红林

    2012-01-01

    传统阈值检测算法都是基于单函数模型进行的,当差异影像分布函数较复杂时检测结果较差.针对这个问题,提出一种基于小波域的隐马尔科夫链模型的遥感图像变化检测算法.将双高斯混合模型与小波变换结合,解决了单函数模型匹配率低的问题,并通过小波变换引入了图像的空间信息,提高了检测精度.利用双高斯混合模型对小波分解后的多层差异影像进行拟合,根据拟合结果判定待检测点类别,对得到的多层初始分割结果,利用隐马尔科夫链模型根据连续最大后验概率融合,得到最终变化检测图.对真实遥感数据集进行实验,证明这种算法可以得到较好的检测结果.%The traditional threshold algorithms detect the changes in multitemporal remote sensing images based on the analysis of the signal function model, which has a poor accuracy for difference images with complex distribution. In this paper, a new approach is proposed by virtue of the double Gaussian mixture model and the wavelet transform. The proposed algorithm has better matching than the signal function model and introduces the spatial information by using the wavelet transform. After using the double Gaussian mixture models to detect the changed regions, the change maps in different scales are fused using the HMC model based on sequential maximum a posteriori estimation. The experiments on the real remote sensing images confirm the effectiveness of the proposed algorithm.

  17. Perceptual security of encrypted images based on wavelet scaling analysis

    Science.gov (United States)

    Vargas-Olmos, C.; Murguía, J. S.; Ramírez-Torres, M. T.; Mejía Carlos, M.; Rosu, H. C.; González-Aguilar, H.

    2016-08-01

    The scaling behavior of the pixel fluctuations of encrypted images is evaluated by using the detrended fluctuation analysis based on wavelets, a modern technique that has been successfully used recently for a wide range of natural phenomena and technological processes. As encryption algorithms, we use the Advanced Encryption System (AES) in RBT mode and two versions of a cryptosystem based on cellular automata, with the encryption process applied both fully and partially by selecting different bitplanes. In all cases, the results show that the encrypted images in which no understandable information can be visually appreciated and whose pixels look totally random present a persistent scaling behavior with the scaling exponent α close to 0.5, implying no correlation between pixels when the DFA with wavelets is applied. This suggests that the scaling exponents of the encrypted images can be used as a perceptual security criterion in the sense that when their values are close to 0.5 (the white noise value) the encrypted images are more secure also from the perceptual point of view.

  18. Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform.

    Science.gov (United States)

    Han, Guang; Wang, Jinkuan; Cai, Xi

    2016-01-01

    Background subtraction without a separate training phase has become a critical task, because a sufficiently long and clean training sequence is usually unavailable, and people generally thirst for immediate detection results from the first frame of a video. Without a training phase, we propose a background subtraction method based on three-dimensional (3D) discrete wavelet transform (DWT). Static backgrounds with few variations along the time axis are characterized by intensity temporal consistency in the 3D space-time domain and, hence, correspond to low-frequency components in the 3D frequency domain. Enlightened by this, we eliminate low-frequency components that correspond to static backgrounds using the 3D DWT in order to extract moving objects. Owing to the multiscale analysis property of the 3D DWT, the elimination of low-frequency components in sub-bands of the 3D DWT is equivalent to performing a pyramidal 3D filter. This 3D filter brings advantages to our method in reserving the inner parts of detected objects and reducing the ringing around object boundaries. Moreover, we make use of wavelet shrinkage to remove disturbance of intensity temporal consistency and introduce an adaptive threshold based on the entropy of the histogram to obtain optimal detection results. Experimental results show that our method works effectively in situations lacking training opportunities and outperforms several popular techniques. PMID:27043570

  19. Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

    Directory of Open Access Journals (Sweden)

    Niya Chen

    2013-01-01

    Full Text Available Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

  20. Wavelet Transform Modulus Maxima-Based Robust Digital Image Watermarking in Wavelet Domain

    Institute of Scientific and Technical Information of China (English)

    LUO Ting; HONG Fan

    2009-01-01

    A new robust watermarking approach was proposed in 2D continuous wavelet domain (CWT).The watermark is embedded into the large coefficients in the middle band of wavelet transform modulus maxima (WTMM) of the host image.After possible attacks,the watermark is then detected and extracted by correlation analysis.Compared with other wavelet domain watermarking approaches,the WTMM approach can endow the image with both rotation and shift invariant properties.On the other hand,scale invariance is achieved with the geometric normalization during watermark detection.Case studies involve various attacks such as shifting,lossy compression,scaling,rotation and median filtering on the watermarked image,and the result shows that the approach is robust to these attacks.

  1. A wavelet-based structural damage assessment approach with progressively downloaded sensor data

    International Nuclear Information System (INIS)

    This paper presents a wavelet-based on-line damage assessment approach based on the use of progressively transmitted multi-resolution sensor data. In extreme events like strong earthquakes, real-time retrieval of structural monitoring data and on-line damage assessment of civil infrastructures are crucial for emergency relief and disaster assistance efforts such as resource allocation and evacuation route arrangement. Due to the limited communication bandwidth available to data transmission during and immediately after major earthquakes, innovative methods for integrated sensor data transmission and on-line damage assessment are highly desired. The proposed approach utilizes a lifting scheme wavelet transform to generate multi-resolution sensor data, which are transmitted progressively in increasing resolution. Multi-resolution sensor data enable interactive on-line condition assessment of structural damages. To validate this concept, a hysteresis-based damage assessment method, proposed by Iwan for extreme-event use, is selected in this study. A sensitivity study on the hysteresis-based damage assessment method under varying data resolution levels was conducted using simulation data from a six-story steel braced frame building subjected to earthquake ground motion. The results of this study show that the proposed approach is capable of reducing the raw sensor data size by a significant amount while having a minor effect on the accuracy of hysteresis-based damage assessment. The proposed approach provides a valuable decision support tool for engineers and emergency response personnel who want to access the data in real time and perform on-line damage assessment in an efficient manner

  2. Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

    Directory of Open Access Journals (Sweden)

    Wentao He

    2016-01-01

    Full Text Available Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

  3. Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis

    Science.gov (United States)

    Qin, Yi; Xing, Jianfeng; Mao, Yongfang

    2016-08-01

    Aimed at solving the key problem in weak transient detection, the present study proposes a new transient feature extraction approach using the optimized Morlet wavelet transform, kurtosis index and soft-thresholding. Firstly, a fast optimization algorithm based on the Shannon entropy is developed to obtain the optimized Morlet wavelet parameter. Compared to the existing Morlet wavelet parameter optimization algorithm, this algorithm has lower computation complexity. After performing the optimized Morlet wavelet transform on the analyzed signal, the kurtosis index is used to select the characteristic scales and obtain the corresponding wavelet coefficients. From the time-frequency distribution of the periodic impulsive signal, it is found that the transient signal can be reconstructed by the wavelet coefficients at several characteristic scales, rather than the wavelet coefficients at just one characteristic scale, so as to improve the accuracy of transient detection. Due to the noise influence on the characteristic wavelet coefficients, the adaptive soft-thresholding method is applied to denoise these coefficients. With the denoised wavelet coefficients, the transient signal can be reconstructed. The proposed method was applied to the analysis of two simulated signals, and the diagnosis of a rolling bearing fault and a gearbox fault. The superiority of the method over the fast kurtogram method was verified by the results of simulation analysis and real experiments. It is concluded that the proposed method is extremely suitable for extracting the periodic impulsive feature from strong background noise.

  4. 基于加权小波变换及MTFC的多光谱影像融合方法%An Image Fusion Method Based on Multi-phase Wavelet Transformation and MTFC Restoration

    Institute of Scientific and Technical Information of China (English)

    张炳先; 何红艳; 李岩

    2015-01-01

    IHS(intensity-hue-saturation)变换是影像融合实际生产中使用最多的一种方法,而小波变换是近几年来影像融合中的热门研究方向。但是现有方法存在纹理畸变和光谱畸变的现象,尤其是当地物光谱特性在全色和多光谱中存在较大差异的时候,融合后光谱畸变将会十分突出。为了解决上述问题,在分析现有小波变换方法的基础上提出了一种基于加权小波变换及调制传递函数补偿(MTFC)的多光谱影像融合方法,通过引入多相位小波变换的方式来抑制小波变换产生的纹理畸变,同时通过引入MTFC的方法来恢复影像融合中丢失的纹理信息。文章选用“高分二号”卫星影像来验证算法的有效性,试验结果表明,与现有的融合方法相比,文章中提出的算法能够很好地抑制影像中的光谱畸变,同时保留更多的有效纹理信息。%AbstractImage fusion is an important tool to fuse high spectral and high spatial information into one image for image interpretation and target recognition in remote sensing application. To date, many image fusion methods have been developed, among which IHS technique is the most widely used, and the wavelet fusion is the most frequently discussed in recent publications due to its obvious advantages. However, the available methods can hardly produce a satisfactory fusion result, in which spectral and spatial distortions often take place, especially when spectral properties of the same surface features are different between natural color images and panchromatic ones. To solve the problem, we propose a new method by introducing a multi-phase approach to restrain spatial distortion caused by the shift variant attribution of wavelet transformation. With suitable rules of fusion, the method uses modulation transfer function compensation (MTFC) to restore details, so that the spectral distortion can be avoided. The performance of the method was

  5. Electrocardiogram signal denoising based on a new improved wavelet thresholding

    Science.gov (United States)

    Han, Guoqiang; Xu, Zhijun

    2016-08-01

    Good quality electrocardiogram (ECG) is utilized by physicians for the interpretation and identification of physiological and pathological phenomena. In general, ECG signals may mix various noises such as baseline wander, power line interference, and electromagnetic interference in gathering and recording process. As ECG signals are non-stationary physiological signals, wavelet transform is investigated to be an effective tool to discard noises from corrupted signals. A new compromising threshold function called sigmoid function-based thresholding scheme is adopted in processing ECG signals. Compared with other methods such as hard/soft thresholding or other existing thresholding functions, the new algorithm has many advantages in the noise reduction of ECG signals. It perfectly overcomes the discontinuity at ±T of hard thresholding and reduces the fixed deviation of soft thresholding. The improved wavelet thresholding denoising can be proved to be more efficient than existing algorithms in ECG signal denoising. The signal to noise ratio, mean square error, and percent root mean square difference are calculated to verify the denoising performance as quantitative tools. The experimental results reveal that the waves including P, Q, R, and S waves of ECG signals after denoising coincide with the original ECG signals by employing the new proposed method.

  6. A New Wavelet-Based Document Image Segmentation Scheme

    Institute of Scientific and Technical Information of China (English)

    赵健; 李道京; 俞卞章; 耿军平

    2002-01-01

    The document image segmentation is very useful for printing, faxing and data processing. An algorithm is developed for segmenting and classifying document image. Feature used for classification is based on the histogram distribution pattern of different image classes. The important attribute of the algorithm is using wavelet correlation image to enhance raw image's pattern, so the classification accuracy is improved. In this paper document image is divided into four types: background, photo, text and graph. Firstly, the document image background has been distingusished easily by former normally method; secondly, three image types will be distinguished by their typical histograms, in order to make histograms feature clearer, each resolution' s HH wavelet subimage is used to add to the raw image at their resolution. At last, the photo, text and praph have been devided according to how the feature fit to the Laplacian distrbution by -X2 and L. Simulations show that classification accuracy is significantly improved. The comparison with related shows that our algorithm provides both lower classification error rates and better visual results.

  7. A wavelet-based method for multispectral face recognition

    Science.gov (United States)

    Zheng, Yufeng; Zhang, Chaoyang; Zhou, Zhaoxian

    2012-06-01

    A wavelet-based method is proposed for multispectral face recognition in this paper. Gabor wavelet transform is a common tool for orientation analysis of a 2D image; whereas Hamming distance is an efficient distance measurement for face identification. Specifically, at each frequency band, an index number representing the strongest orientational response is selected, and then encoded in binary format to favor the Hamming distance calculation. Multiband orientation bit codes are then organized into a face pattern byte (FPB) by using order statistics. With the FPB, Hamming distances are calculated and compared to achieve face identification. The FPB algorithm was initially created using thermal images, while the EBGM method was originated with visible images. When two or more spectral images from the same subject are available, the identification accuracy and reliability can be enhanced using score fusion. We compare the identification performance of applying five recognition algorithms to the three-band (visible, near infrared, thermal) face images, and explore the fusion performance of combing the multiple scores from three recognition algorithms and from three-band face images, respectively. The experimental results show that the FPB is the best recognition algorithm, the HMM yields the best fusion result, and the thermal dataset results in the best fusion performance compared to other two datasets.

  8. The Brera Multi-scale Wavelet ROSAT HRI source catalogue (BMW-HRI)

    CERN Document Server

    Panzera, M R; Covino, S; Lazzati, D; Mignani, R P; Moretti, A; Tagliaferri, G

    2003-01-01

    We present the Brera Multi-scale Wavelet ROSAT HRI source catalogue (BMW-HRI) derived from all ROSAT HRI pointed observations with exposure time longer than 100 s available in the ROSAT public archives. The data were analyzed automatically using a wavelet detection algorithm suited to the detection and characterization of both point-like and extended sources. This algorithm is able to detect and disentangle sources in very crowded fields and/or in presence of extended or bright sources. Images have been also visually inspected after the analysis to ensure verification. The final catalogue, derived from 4,303 observations, consists of 29,089 sources detected with a detection probability of greater or equal 4.2 sigma. For each source, the primary catalogue entries provide name, position, count rate, flux and extension along with the relative errors. In addition, results of cross-correlations with existing catalogues at different wavelengths (FIRST, IRAS, 2MASS and GSC2) are also reported. All these information ...

  9. Using Wavelet-Based Method for Detection of Atrial Late Potentials and ECG Classification

    OpenAIRE

    Ivanushkina, N. G.; Ivan?ko, E. O.; Fesechko, V. A.; Dorosh, N. V.

    2010-01-01

    The wavelet-based method is proposed to improve detection of low-amplitude components of P wave. Atrial Late Potentials are simulated by solving the Hodgkin-Huxley Equations. Classification of ECG is accomplished by cluster analysis of wavelet coefficients.

  10. Nonlinear Accelerator Problems via Wavelets; 8, Invariant Bases, Loops and KAM

    CERN Document Server

    Fedorova, A N; Fedorova, Antonina N.; Zeitlin, Michael G.

    1999-01-01

    In this series of eight papers we present the applications of methods from wavelet analysis to polynomial approximations for a number of accelerator physics problems. In this part we consider variational wavelet approach for loops, invariant bases on semidirect product, KAM calculation via FWT.

  11. Wavelet Based Analytical Expressions to Steady State Biofilm Model Arising in Biochemical Engineering.

    Science.gov (United States)

    Padma, S; Hariharan, G

    2016-06-01

    In this paper, we have developed an efficient wavelet based approximation method to biofilm model under steady state arising in enzyme kinetics. Chebyshev wavelet based approximation method is successfully introduced in solving nonlinear steady state biofilm reaction model. To the best of our knowledge, until now there is no rigorous wavelet based solution has been addressed for the proposed model. Analytical solutions for substrate concentration have been derived for all values of the parameters δ and SL. The power of the manageable method is confirmed. Some numerical examples are presented to demonstrate the validity and applicability of the wavelet method. Moreover the use of Chebyshev wavelets is found to be simple, efficient, flexible, convenient, small computation costs and computationally attractive. PMID:26661721

  12. Adaptive directional wavelet transform based on directional prefiltering.

    Science.gov (United States)

    Tanaka, Yuichi; Hasegawa, Madoka; Kato, Shigeo; Ikehara, Masaaki; Nguyen, Truong Q

    2010-04-01

    This paper proposes an efficient approach for adaptive directional wavelet transform (WT) based on directional prefiltering. Although the adaptive directional WT is able to transform an image along diagonal orientations as well as traditional horizontal and vertical directions, it sacrifices computation speed for good image coding performance. We present two efficient methods to find the best transform directions by prefiltering using 2-D filter bank or 1-D directional WT along two fixed directions. The proposed direction calculation methods achieve comparable image coding performance comparing to the conventional one with less complexity. Furthermore, transform direction data of the proposed method can be used for content-based image retrieval to increase retrieval ratio. PMID:20028625

  13. A new phase comparison pilot protection based on wavelet transform

    Institute of Scientific and Technical Information of China (English)

    YANG Ying; TAI Neng-ling; YU Wei-yong

    2006-01-01

    Current phase comparison based pilot protection had been generally utilized as primary protection of the transmission lines in China from the 1950's to the 1980's. Conventional phase comparison pilot protection has a long phase comparison time, which results in a longer fault-clearing time. This paper proposes a new current phase comparison. pilot protection scheme that is based on non-power frequency fault current component.The phase of the fourth harmonic current of each end of the protected line has been abstracted by utilizing complex wavelet transformation and then compared in order to determine whether the inner fault occurs or not. This way can greatly decrease fault-clearing time and improve performances of this pilot protection when fault occurs under the heavy-load current and asymmetrical operation conditions. Many EMTP simulations have verified theproposed scheme's correctness and effectiveness.

  14. Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm

    Science.gov (United States)

    Mahdavi, Seyed Hossein; Razak, Hashim Abdul

    2016-06-01

    This paper presents a wavelet-based genetic algorithm strategy for optimal sensor placement (OSP) effective for time-domain structural identification. Initially, the GA-based fitness evaluation is significantly improved by using adaptive wavelet functions. Later, a multi-species decimal GA coding system is modified to be suitable for an efficient search around the local optima. In this regard, a local operation of mutation is introduced in addition with regeneration and reintroduction operators. It is concluded that different characteristics of applied force influence the features of structural responses, and therefore the accuracy of time-domain structural identification is directly affected. Thus, the reliable OSP strategy prior to the time-domain identification will be achieved by those methods dealing with minimizing the distance of simulated responses for the entire system and condensed system considering the force effects. The numerical and experimental verification on the effectiveness of the proposed strategy demonstrates the considerably high computational performance of the proposed OSP strategy, in terms of computational cost and the accuracy of identification. It is deduced that the robustness of the proposed OSP algorithm lies in the precise and fast fitness evaluation at larger sampling rates which result in the optimum evaluation of the GA-based exploration and exploitation phases towards the global optimum solution.

  15. Roll Eccentricity Compensation Based on Anti-Alias-sing Wavelet Analysis Method

    Institute of Scientific and Technical Information of China (English)

    CHEN Zhi-ming; LUO Fei; XU Yu-ge; YU Wei

    2009-01-01

    Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi-resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency-aliasing problem owing to the less than ideal cut-off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi-resolution wavelet analyzer was designed. Experimental results showed that the anti-aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was im-proved from ±40 μm to ±15 μm.

  16. Wavelet transform based ECG signal filtering implemented on FPGA

    Directory of Open Access Journals (Sweden)

    Germán-Salló Zoltán

    2011-12-01

    Full Text Available Filtering electrocardiographic (ECG signals is always a challenge because the accuracy of their interpretation depends strongly on filtering results. The Discrete Wavelet Transform (DWT is an efficient, new and useful tool for signal processing applications and it’s adopted in many domains as biomedical signal filtering. This transform came about from different fields, including mathematics, physics and signal processing, it has a growing applicability due to its so-called multiresolution analyzing capabilities. FPGAs are reconfigurable logic devices made up of arrays of logic cells and routing channels having some specific characteristics which allow to use them in signal processing applications. This paper presents a DWT based ECG signal denoising method implemented on FPGA, using Matlab specific Xilinx tool, as System Generator, the procedure is simulated and evaluated through filtering specific parameters.

  17. Analytic discrete cosine harmonic wavelet transform based OFDM system

    Indian Academy of Sciences (India)

    M N Suma; S V Narasimhan; B Kanmani

    2015-02-01

    An OFDM based on Analytic Discrete Cosine HarmonicWavelet Transform (ADCHWT_OFDM) has been proposed in this paper. Analytic DCHWT has been realized by applying DCHWT to the original signal and to its Hilbert transform. ADCHWT has been found to be computationally efficient and very effective in improving Bit Error Rate (BER) and Peak to Average Power Ratio (PAPR) performance. Improvement compared to that of Haar-WT OFDM and DFT OFDM is achieved without employing Cyclic Prefix BER is 0.002 for ADCHWT OFDM compared to Haar WT, DFT OFDM which have BER of 0.06 and 0.4, respectively, at 15 dB SNR. PAPR is also reduced by 3 dB compared to DFT OFDM and 0.3 dB reduction compared to Haar WT OFDM.

  18. Adaptive inpainting algorithm based on DCT induced wavelet regularization.

    Science.gov (United States)

    Li, Yan-Ran; Shen, Lixin; Suter, Bruce W

    2013-02-01

    In this paper, we propose an image inpainting optimization model whose objective function is a smoothed l(1) norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting.

  19. Research on Wavelet-Based Algorithm for Image Contrast Enhancement

    Institute of Scientific and Technical Information of China (English)

    Wu Ying-qian; Du Pei-jun; Shi Peng-fei

    2004-01-01

    A novel wavelet-based algorithm for image enhancement is proposed in the paper. On the basis of multiscale analysis, the proposed algorithm solves efficiently the problem of noise over-enhancement, which commonly occurs in the traditional methods for contrast enhancement. The decomposed coefficients at same scales are processed by a nonlinear method, and the coefficients at different scales are enhanced in different degree. During the procedure, the method takes full advantage of the properties of Human visual system so as to achieve better performance. The simulations demonstrate that these characters of the proposed approach enable it to fully enhance the content in images, to efficiently alleviate the enhancement of noise and to achieve much better enhancement effect than the traditional approaches.

  20. 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.

  1. Automatic key frame selection using a wavelet-based approach

    Science.gov (United States)

    Campisi, Patrizio; Longari, Andrea; Neri, Alessandro

    1999-10-01

    In a multimedia framework, digital image sequences (videos) are by far the most demanding as far as storage, search, browsing and retrieval requirements are concerned. In order to reduce the computational burden associated to video browsing and retrieval, a video sequence is usually decomposed into several scenes (shots) and each of them is characterized by means of some key frames. The proper selection of these key frames, i.e. the most representative frames in the scene, is of paramount importance for computational efficiency. In this contribution a novel key frame extraction technique based on the wavelet analysis is presented. Experimental results show the capability of the proposed algorithm to select key frames properly summarizing the shot.

  2. A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle

    Science.gov (United States)

    Hester, D.; González, A.

    2012-04-01

    Previous research based on theoretical simulations has shown the potential of the wavelet transform to detect damage in a beam by analysing the time-deflection response due to a constant moving load. However, its application to identify damage from the response of a bridge to a vehicle raises a number of questions. Firstly, it may be difficult to record the difference in the deflection signal between a healthy and a slightly damaged structure to the required level of accuracy and high scanning frequencies in the field. Secondly, the bridge is going to have a road profile and it will be loaded by a sprung vehicle and time-varying forces rather than a constant load. Therefore, an algorithm based on a plot of wavelet coefficients versus time to detect damage (a singularity in the plot) appears to be very sensitive to noise. This paper addresses these questions by: (a) using the acceleration signal, instead of the deflection signal, (b) employing a vehicle-bridge finite element interaction model, and (c) developing a novel wavelet-based approach using wavelet energy content at each bridge section, which proves to be more sensitive to damage than a wavelet coefficient line plot at a given scale as employed by others.

  3. Research on the detecting methods of singularity in deformation signal based on two kinds of wavelet entropy

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hua-rong; QU Guo-qing; REN Ting

    2012-01-01

    There are various influencing factors that affect the deformation observation,and deformation signals show different characteristics under different scales.Wavelet analysis possesses multi-scale property,and the information entropy has great representational capability to the complexity of information.By hamming window to the wavelet coefficients and windowed wavelet energy obtained by multi-resolution analysis (MRA),it can be achieved to measure the wavelet time entropy (WTE) and wavelet energy entropy (WEE).The paper established deformation signals,selected the parameters,and compared the singularity detection ability and anti-noise ability of two kinds of wavelet entropy and applied them to the singularity detection at the GPS continuously operating reference stations.It is shown that the WTE performs well in weak singularity information detection in finite frequency components signals and the WEE is more suitable for detecting the singularity in the signals with complex,strong background noise.

  4. Image denoising using statistical model based on quaternion wavelet domain

    Institute of Scientific and Technical Information of China (English)

    YIN Ming; LIU Wei; KONG Ranran

    2012-01-01

    Image denoising is the basic problem of image processing. Quaternion wavelet transform is a new kind of multiresolution analysis tools. Image via quaternion wavelet transform, wavelet coefficients both in intrascale and in interscale have certain correla- tions. First, according to the correlation of quaternion wavelet coefficients in interscale, non-Ganssian distribution model is used to model its correlations, and the coefficients are divided into important and unimportance coefficients. Then we use the non-Gaussian distribution model to model the important coefficients and its adjacent coefficients, and utilize the MAP method estimate original image wavelet coefficients from noisy coefficients, so as to achieve the purpose of denoising. Experimental results show that our al- gorithm outperforms the other classical algorithms in peak signal-to-noise ratio and visual quality.

  5. Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

    In this study, we introduce an indirect adaptive fuzzy wavelet neural controller (IAFWNC) as a power system stabilizer to damp inter-area modes of oscillations in a multi-machine power system. Quantum computing is an efficient method for improving the computational efficiency of neural networks, so we developed an identifier based on a quantum neural network (QNN) to train the IAFWNC in the proposed scheme. All of the controller parameters are tuned online based on the Lyapunov stability theory to guarantee the closed-loop stability. A two-machine, two-area power system equipped with a static synchronous series compensator as a series flexible ac transmission system was used to demonstrate the effectiveness of the proposed controller. The simulation and experimental results demonstrated that the proposed IAFWNC scheme can achieve favorable control performance. PMID:26363960

  6. Novel Gauss-Hermite integration based Bayesian inference on optimal wavelet parameters for bearing fault diagnosis

    Science.gov (United States)

    Wang, Dong; Tsui, Kwok-Leung; Zhou, Qiang

    2016-05-01

    Rolling element bearings are commonly used in machines to provide support for rotating shafts. Bearing failures may cause unexpected machine breakdowns and increase economic cost. To prevent machine breakdowns and reduce unnecessary economic loss, bearing faults should be detected as early as possible. Because wavelet transform can be used to highlight impulses caused by localized bearing faults, wavelet transform has been widely investigated and proven to be one of the most effective and efficient methods for bearing fault diagnosis. In this paper, a new Gauss-Hermite integration based Bayesian inference method is proposed to estimate the posterior distribution of wavelet parameters. The innovations of this paper are illustrated as follows. Firstly, a non-linear state space model of wavelet parameters is constructed to describe the relationship between wavelet parameters and hypothetical measurements. Secondly, the joint posterior probability density function of wavelet parameters and hypothetical measurements is assumed to follow a joint Gaussian distribution so as to generate Gaussian perturbations for the state space model. Thirdly, Gauss-Hermite integration is introduced to analytically predict and update moments of the joint Gaussian distribution, from which optimal wavelet parameters are derived. At last, an optimal wavelet filtering is conducted to extract bearing fault features and thus identify localized bearing faults. Two instances are investigated to illustrate how the proposed method works. Two comparisons with the fast kurtogram are used to demonstrate that the proposed method can achieve better visual inspection performances than the fast kurtogram.

  7. Features of energy distribution for blast vibration signals based on wavelet packet decomposition

    Institute of Scientific and Technical Information of China (English)

    LING Tong-hua; LI Xi-bing; DAI Ta-gen; PENG Zhen-bin

    2005-01-01

    Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.

  8. Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition

    CERN Document Server

    Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas

    2010-01-01

    This paper investigates the multiresolution level-1 and level-2 Quotient based Fusion of thermal and visual images. In the proposed system, the method-1 namely "Decompose then Quotient Fuse Level-1" and the method-2 namely "Decompose-Reconstruct then Quotient Fuse Level-2" both work on wavelet transformations of the visual and thermal face images. The wavelet transform is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information without any loss. This approach is based on a definition of an illumination invariant signature image which enables an analytic generation of the image space with varying illumination. The quotient fused images are passed through Principal Component Analysis (PCA) for dimension reduction and then those images are classified using a multi-layer perceptron (MLP). The performances of both the methods have been evaluated using OTCBVS and IRIS databases. All the different classes have been ...

  9. Dyadic Bivariate Fourier Multipliers for Multi-Wavelets in L2(R2)

    Institute of Scientific and Technical Information of China (English)

    Zhongyan Li∗; Xiaodi Xu

    2015-01-01

    The single 2 dilation orthogonal wavelet multipliers in one dimensional case and single A-dilation (where A is any expansive matrix with integer entries and|detA|=2) wavelet multipliers in high dimensional case were completely characterized by the Wutam Consortium (1998) and Z. Y. Li, et al. (2010). But there exist no more results on orthogonal multivariate wavelet matrix multipliers corresponding integer expansive dilation matrix with the absolute value of determinant not 2 in L2(R2). In this paper, we choose as the dilation matrix and consider the 2I2-dilation orthogonal multivariate wavelet Y={y1,y2,y3}, (which is called a dyadic bivariate wavelet) multipliers. We call the 3×3 matrix-valued function A(s)=[ fi,j(s)]3×3, where fi,j are measurable functions, a dyadic bivariate matrix Fourier wavelet multiplier if the inverse Fourier transform of A(s)(cy1(s),cy2(s),cy3(s))⊤ = ( b g1(s), b g2(s), b g3(s))⊤ is a dyadic bivariate wavelet whenever (y1,y2,y3) is any dyadic bivariate wavelet. We give some conditions for dyadic matrix bivariate wavelet multipliers. The results extended that of Z. Y. Li and X. L. Shi (2011). As an application, we construct some useful dyadic bivariate wavelets by using dyadic Fourier matrix wavelet multipliers and use them to image denoising.

  10. Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

    Science.gov (United States)

    Cifter, Atilla

    2011-06-01

    This paper introduces wavelet-based extreme value theory (EVT) for univariate value-at-risk estimation. Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model. In the first stage, wavelets are used as a threshold in generalized Pareto distribution, and in the second stage, EVT is applied with a wavelet-based threshold. This new model is applied to two major emerging stock markets: the Istanbul Stock Exchange (ISE) and the Budapest Stock Exchange (BUX). The relative performance of wavelet-based EVT is benchmarked against the Riskmetrics-EWMA, ARMA-GARCH, generalized Pareto distribution, and conditional generalized Pareto distribution models. The empirical results show that the wavelet-based extreme value theory increases predictive performance of financial forecasting according to number of violations and tail-loss tests. The superior forecasting performance of the wavelet-based EVT model is also consistent with Basel II requirements, and this new model can be used by financial institutions as well.

  11. Prediction of Al(OH)3 fluidized roasting temperature based on wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The recycle fluidization roasting in alumina production was studied and a temperature forecast model was established based on wavelet neural network that had a momentum item and an adjustable learning rate. By analyzing the roasting process, coal gas flux, aluminium hydroxide feeding and oxygen content were ascertained as the main parameters for the forecast model. The order and delay time of each parameter in the model were deduced by F test method. With 400 groups of sample data (sampled with the period of 1.5 min) for its training, a wavelet neural network model was acquired that had a structure of {7-21-1}, i.e., seven nodes in the input layer, twenty-one nodes in the hidden layer and one node in the output layer. Testing on the prediction accuracy of the model shows that as the absolute error ±5.0 ℃ is adopted, the single-step prediction accuracy can achieve 90% and within 6 steps the multi-step forecast result of model for temperature is receivable.

  12. Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Wu Xiang

    2014-01-01

    Full Text Available It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM termed as WELM (wavelet based ELM for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

  13. 基于小波分形的图像分割算法%Wavelet Fractal-Based Image Segment Algorithm

    Institute of Scientific and Technical Information of China (English)

    叶俊勇; 汪同庆; 彭健; 杨波

    2002-01-01

    The image of shoe leather lumen is not very satisfaction because of technology of CT. The smart imagesegment is the base of getting smart measurement data. An algorithm of image segment based on wavelet and fractalhas been proposed after analyzing the specialty of images. The image is decomposed through wavelet multi-resolutiondecomposition , and the fractal dimension is calculated by the decomposed image. This approach is more satisfied thangeneral method in image segment of shoe leather lumen image by CT. This algorithm can segment the edge of shoe lu-men exactly. The experimentations prove the approach is rational.

  14. Wavelet-based Image Enhancement Using Fourth Order PDE

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren

    2011-01-01

    differential equations (PDEs) and wavelet transform. In the existing wavelet thresholding methods, the final noise reduced image has limited improvement. It is due to keeping the approximate coefficients of the image unchanged. These coefficients have the main information of the image. Since noise affects both...... the approximate and detail coefficients, in this research, the anisotropic diffusion technique for noise reduction is applied on the approximation band to alleviate the deficiency of the existing wavelet thresholding methods. The proposed method was applied on several standard noisy images and the...

  15. 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.

  16. Diagnosis of Gearbox Typical Fault in Rolling Mills Based on the Wavelet Packets Technology

    Institute of Scientific and Technical Information of China (English)

    CUI Lingli; GAO Lixin; ZHANG Jianyu; DING Fang

    2006-01-01

    The early impulse fault diagnosis of the gearbox in rolling mills is often difficult and labour intensive because the gearbox of that high speed machine is multi-shafting transmission system, in which many gearsets and rolling bears work together at the same time and there are much complex frequency structure and various disturb. A new time-frequency method based on the wavelet packets technique was developed and used to extract the impact feature from signals collected from faulty data of one rolling mills gearbox. The method improves the signal to noise ration so that results obtained using this method represents features with fine resolution in both low-frequency and the high frequency bands. The results of analysis indicate the validity and the practicability of the method proposed here.

  17. Discrete wavelet transform-based fault diagnosis for driving system of pipeline detection robot arm

    Institute of Scientific and Technical Information of China (English)

    Deng Huiyu; Wang Xinli; Ma Peisun

    2005-01-01

    A real-time wavelet multi-resolution analysis (MRA)-based fault detection algorithm is proposed. The first stage detailed MRA signals extracted from the original signals were used as the criteria for fault detection. By measuring sharp variations in the detailed MRA signals, faults in the motor driving system of pipeline detection robot arm could be detected. The fault type was then identified by comparison of the three-phase MRA sharp variations. The effects of the faults were examined. The simulation results show that this algorithm is effective and robust, it is promising for fault detection in a robot's joint driving system. The method is simple, rapid and it can operate in real time.

  18. Passive microrheology of soft materials with atomic force microscopy: A wavelet-based spectral analysis

    Energy Technology Data Exchange (ETDEWEB)

    Martinez-Torres, C.; Streppa, L. [CNRS, UMR5672, Laboratoire de Physique, Ecole Normale Supérieure de Lyon, 46 Allée d' Italie, Université de Lyon, 69007 Lyon (France); Arneodo, A.; Argoul, F. [CNRS, UMR5672, Laboratoire de Physique, Ecole Normale Supérieure de Lyon, 46 Allée d' Italie, Université de Lyon, 69007 Lyon (France); CNRS, UMR5798, Laboratoire Ondes et Matière d' Aquitaine, Université de Bordeaux, 351 Cours de la Libération, 33405 Talence (France); Argoul, P. [Université Paris-Est, Ecole des Ponts ParisTech, SDOA, MAST, IFSTTAR, 14-20 Bd Newton, Cité Descartes, 77420 Champs sur Marne (France)

    2016-01-18

    Compared to active microrheology where a known force or modulation is periodically imposed to a soft material, passive microrheology relies on the spectral analysis of the spontaneous motion of tracers inherent or external to the material. Passive microrheology studies of soft or living materials with atomic force microscopy (AFM) cantilever tips are rather rare because, in the spectral densities, the rheological response of the materials is hardly distinguishable from other sources of random or periodic perturbations. To circumvent this difficulty, we propose here a wavelet-based decomposition of AFM cantilever tip fluctuations and we show that when applying this multi-scale method to soft polymer layers and to living myoblasts, the structural damping exponents of these soft materials can be retrieved.

  19. Wavelet transform based nonlınear predıctıon of sıgnals

    Directory of Open Access Journals (Sweden)

    Zoltan German-Sallo

    2014-12-01

    Full Text Available Estimating signals from time series is a common task in many domains of science and has been addressed for a long time by specialists. Predicting a signal from recorded time series remains however a very specific task, a great challenge. The wavelet transform provides multi-resolution analysis and allows accurate time-frequency localization of different signal properties. This paper presents a nonlinear prediction method implemented on artificial neural network based learning structure. From a discrete wavelet transform provided tree structure, specific coefficients are obtained and predicted with the already mentioned method, the reconstruction of signal is carried out using these new coefficients. The predicted signal is compared with the original one through parameters as the absolute mean error, using different analyzing functions and different learning structures. To evaluate the prediction, noise of different levels is added and the absolute mean error is recomputed and compared after every prediction.

  20. Model of Information Security Risk Assessment based on Improved Wavelet Neural Network

    Directory of Open Access Journals (Sweden)

    Gang Chen

    2013-09-01

    Full Text Available This paper concentrates on the information security risk assessment model utilizing the improved wavelet neural network. The structure of wavelet neural network is similar to the multi-layer neural network, which is a feed-forward neural network with one or more inputs. Afterwards, we point out that the training process of wavelet neural networks is made up of four steps until the value of error function can satisfy a pre-defined error criteria. In order to enhance the quality of information security risk assessment, we proposed a modified version of wavelet neural network which can effectively combine all influencing factors in assessing information security risk by linear integrating several weights. Furthermore, the proposed wavelet neural network is trained by the BP algorithm with batch mode, and the weight coefficients of the wavelet are modified with the adopting mode. Finally, a series of experiments are conduct to make performance evaluation. From the experimental results, we can see that the proposed model can assess information security risk accurately and rapidly

  1. Analysis of Efficient Wavelet Based Volumetric Image Compression

    Directory of Open Access Journals (Sweden)

    Krishna Kumar

    2012-04-01

    Full Text Available Recently, the wavelet transform has emerged as a cutting edge technology, within the field ofimage compression research. Telemedicine, among other things, involves storage andtransmission of medical images, popularly known as Teleradiology. Due to constraints onbandwidth and storage capacity, a medical image may be needed to be compressed beforetransmission/storage. This paper is focused on selecting the most appropriate wavelettransform for a given type of medical image compression. In this paper we have analyzed thebehavior of different type of wavelet transforms with different type of medical images andidentified the most appropriate wavelet transform that can perform optimum compression for agiven type of medical imaging. To analyze the performance of the wavelet transform with themedical images at constant PSNR, we calculated SSIM and their respective percentagecompression.

  2. Optimization of Wavelet-Based De-noising in MRI

    Directory of Open Access Journals (Sweden)

    K. Bartusek

    2011-04-01

    Full Text Available In the paper, a method for MR image enhancement using the wavelet analysis is described. The wavelet analysis is concentrated on the influence of threshold level and mother wavelet choices on the resultant MR image. The influence is expressed by the measurement and mutual comparison of three MT image parameters: signal to noise ratio, image contrast, and linear slope edge approximation. Unlike most standard methods working exclusively with the MR image magnitude, in our case both the MR image magnitude and the MR image phase were used in the enhancement process. Some recommendations are mentioned in conclusion, such as how to use a combination of mother wavelets with threshold levels for various types of MR images.

  3. A framework for evaluating wavelet based watermarking for scalable coded digital item adaptation attacks

    Science.gov (United States)

    Bhowmik, Deepayan; Abhayaratne, Charith

    2009-02-01

    A framework for evaluating wavelet based watermarking schemes against scalable coded visual media content adaptation attacks is presented. The framework, Watermark Evaluation Bench for Content Adaptation Modes (WEBCAM), aims to facilitate controlled evaluation of wavelet based watermarking schemes under MPEG-21 part-7 digital item adaptations (DIA). WEBCAM accommodates all major wavelet based watermarking in single generalised framework by considering a global parameter space, from which the optimum parameters for a specific algorithm may be chosen. WEBCAM considers the traversing of media content along various links and required content adaptations at various nodes of media supply chains. In this paper, the content adaptation is emulated by the JPEG2000 coded bit stream extraction for various spatial resolution and quality levels of the content. The proposed framework is beneficial not only as an evaluation tool but also as design tool for new wavelet based watermark algorithms by picking and mixing of available tools and finding the optimum design parameters.

  4. High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a novel, high-accuracy, high-fidelity, multiresolution (MRES), wavelet-based framework for efficient prediction of airframe noise sources and...

  5. High Order Wavelet-Based Multiresolution Technology for Airframe Noise Prediction Project

    Data.gov (United States)

    National Aeronautics and Space Administration — An integrated framework is proposed for efficient prediction of rotorcraft and airframe noise. A novel wavelet-based multiresolution technique and high-order...

  6. Extraction of MHD Signal Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    赵晴初; 赵彤; 李旻; 黄胜华; 徐佩霞

    2002-01-01

    Mirnov signals mixed with interferences are a kind of non-stationary signal. It can not obtain satisfactory effects to extract MHD signals from mirnov signals by Fourier Transform. This paper suggests that the wavelet transform can be used to treat mirnov signals. Theoretical analysis and experimental result have indicated that using the time-frequency analysis characteristics of the wavelet transform to filter mirnov signals can remove effectively interferences and extract useful MHD signals.

  7. Wavelet Based Analysis of Airborne Gravity Data For Interpretation of Geological Boundaries

    Science.gov (United States)

    Leblanc, George E.; Ferguson, Stephen

    Airborne gravimeters have only very recently been developed with the sensitivity necessary for useful exploration geophysics. In this study, an airborne gravimeter - an inertially-stabilized platform which converts accelerometer readings into gravity values - has been installed aboard the NRC's Convair 580 research aircraft and a survey performed over the Geological Survey of Canada's gravity test area. These data are used in a new wavelet transform methodology that quickly analyses and locates geological boundaries of various spatial extents within real aerogravity data. The raw aerogravity data were GPS corrected and then noise minimised - to reduce high frequency random noise - with a separate wavelet transform denoising algorithm. The multi-resolution nature of the wavelet transform was then used to investigate the presence of boundaries at various scales. Examination of each wavelet detail scale shows that there is a coherent and localizable signal that conforms to geological boundaries over the entire range of scales. However, the boundaries are more apparent in the lower wavelet scales (corresponding to higher frequencies). The location of the local maximum values of the wavelet coefficents on each wavelet level provides a means to quickly determine and evaluate regional and/or local boundaries. The boundaries that are determined as a function of wavelet scale are able to be well-localized with the wavelet transform, and provides a method to locate, in ground coordinates, the edges of the boundary. In this study it is clear that wavelet methodologies are very well suited to being used effectively with aerogravity data due to the non-stationary nature of these data. Using these same methods on the horizontal and vertical derivatives of the data can provide visually clearer boundary definition, however, thus far there has not been any new boundaries identified in the derivative data. It is also possible to draw potential structural information, such as general

  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

    by employing wavelet transform under different fault conditions. Then the fuzzy logic rules are automatically trained based on the fuzzified fault features to diagnose the different faults. Neither additional sensor nor the capacitor voltages are needed in the proposed method. The high accuracy, good...... 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. A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components

    OpenAIRE

    A., Arun Kumar; Philip, Ninan Sajeeth; Samar, Vincent J; Desjardins, James A; Segalowitz, Sidney J.

    2014-01-01

    Event Related Potentials (ERPs) are very feeble alterations in the ongoing Electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate diff...

  10. Bayesian wavelet-based Poisson intensity estimation of images using the Fisz transformation

    OpenAIRE

    Fadili, Jalal M.; Mathieu, Jérôme; Romaniuk, Barbara; Desvignes, Michel

    2003-01-01

    International audience A novel wavelet-based Poisson intensity estimator of images is presented. This method is based on the asymptotic normality of a certain function of the Haar wavelet and scaling coefficients called the Fisz transformation. Soma asymptotic results such as normality and decorrelation of the transformed image samples are extended to the 2D case. This Fisz-transformed image is then treated as if it was independent and Gaussian variables and we apply a novel Bayesian denoi...

  11. Implementation of Time-Scale Transformation Based on Continuous Wavelet Theory

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The basic objective of time-scale transformation is to compress or expand the signal in time field while keeping the same spectral properties.This paper presents two methods to derive time-scale transformation formula based on continuous wavelet transform.For an arbitrary given square-integrable function f(t),g(t) = f(t/λ) is derived by continuous wavelet transform and its inverse transform.The result shows that time-scale transformation may be obtained through the modification of the time-scale of wavelet function filter using equivalent substitution. The paper demonstrates the result by theoretic derivations and experimental simulation.

  12. EXTENDED SELF SIMILARITY OF PASSIVE SCALAR IN RAYLEIGH-BENARD CONVECTION FLOW BASED ON WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Wavelet transform is used to analyze the scaling rule convection flow from two aspects. By utilizing the method of extended self similarity (ESS), one can find the obtained scaling exponent agrees well with the one obtained from the temperature data in a experiment of wind tunnel. And then we propose a newly defined formula based on wavelet transform, and can determine the scaling exponent ξ(q) of temperature data. The obtained results demonstrate that we can correctly extract ξ(q) by using the method which is named as wavelet transform maximum modulus (WTMM).``

  13. Audio watermarking based on psychoacoustic model and critical band wavelet transform

    Institute of Scientific and Technical Information of China (English)

    TAO Zhi; ZHAO Heming; GU Jihua; WU Di

    2007-01-01

    Watermark embedding algorithm based on critical band wavelet transform of digital audio signal is proposed in this paper. The masking threshold for each audio signal segment was calculated on the basic of psychoacoustic model. According to the similarity between critical band of human auditory system and critical band wavelet transform, a watermark was embedded into the low-band and mid-band coefficients of digital wavelet. The embedding strength was adaptively controlled by the masking threshold. The experiment results show that the embedded watermark signal is inaudible, and the watermarked audio signal has good robustness against many attacks such as compression, noise, re-sampling, low-pass filtering.

  14. A wavelet-based Projector Augmented-Wave (PAW) method: reaching frozen-core all-electron precision with a systematic, adaptive and localized wavelet basis set

    CERN Document Server

    Rangel, Tonatiuh; Genovese, Luigi; Torrent, Marc

    2016-01-01

    We present a Projector Augmented-Wave~(PAW) method based on a wavelet basis set. We implemented our wavelet-PAW method as a PAW library in the ABINIT package [http://www.abinit.org] and into BigDFT [http://www.bigdft.org]. We test our implementation in prototypical systems to illustrate the potential usage of our code. By using the wavelet-PAW method, we can simulate charged and special boundary condition systems with frozen-core all-electron precision. Furthermore, our work paves the way to large-scale and potentially order-N simulations within a PAW method.

  15. Are Eurozone Fixed Income Markets Integrated? An Analysis Based on Wavelet Multiple Correlation and Cross Correlation

    Directory of Open Access Journals (Sweden)

    Arif Billah Dar

    2014-01-01

    Full Text Available This paper investigates the synchronization of fixed income markets within Eurozone countries using the new wavelet based methodology. Conventional wavelet methods that use multivariate set of variables to calculate pairwise correlation and cross correlation lead to spurious correlation due to possible relationships with other variables, amplification of type-1 errors, and results, in the form of large set of erroneous graphs. Given these disadvantages of conventional wavelet based pairwise correlation and cross-correlation method, we avoid these limitations by using wavelet multiple correlation and multiple cross correlations to analyze the relationships in Eurozone fixed income markets. Our results based on this methodology indicate that Eurozone fixed income markets are highly integrated and this integration grows with timescales, and hence there is almost no scope for independent monetary policy and bond diversification in these countries.

  16. Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization

    Science.gov (United States)

    Wang, Syu-Siang; Chern, Alan; Tsao, Yu; Hung, Jeih-weih; Lu, Xugang; Lai, Ying-Hui; Su, Borching

    2016-08-01

    For most of the state-of-the-art speech enhancement techniques, a spectrogram is usually preferred than the respective time-domain raw data since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, the short-time Fourier transform (STFT) that creates the spectrogram in general distorts the original signal and thereby limits the capability of the associated speech enhancement techniques. In this study, we propose a novel speech enhancement method that adopts the algorithms of discrete wavelet packet transform (DWPT) and nonnegative matrix factorization (NMF) in order to conquer the aforementioned limitation. In brief, the DWPT is first applied to split a time-domain speech signal into a series of subband signals without introducing any distortion. Then we exploit NMF to highlight the speech component for each subband. Finally, the enhanced subband signals are joined together via the inverse DWPT to reconstruct a noise-reduced signal in time domain. We evaluate the proposed DWPT-NMF based speech enhancement method on the MHINT task. Experimental results show that this new method behaves very well in prompting speech quality and intelligibility and it outperforms the convnenitional STFT-NMF based method.

  17. Wavelet-based multiresolution analysis of Wivenhoe Dam water temperatures

    Science.gov (United States)

    Percival, D. B.; Lennox, S. M.; Wang, Y.-G.; Darnell, R. E.

    2011-05-01

    Water temperature measurements from Wivenhoe Dam offer a unique opportunity for studying fluctuations of temperatures in a subtropical dam as a function of time and depth. Cursory examination of the data indicate a complicated structure across both time and depth. We propose simplifying the task of describing these data by breaking the time series at each depth into physically meaningful components that individually capture daily, subannual, and annual (DSA) variations. Precise definitions for each component are formulated in terms of a wavelet-based multiresolution analysis. The DSA components are approximately pairwise uncorrelated within a given depth and between different depths. They also satisfy an additive property in that their sum is exactly equal to the original time series. Each component is based upon a set of coefficients that decomposes the sample variance of each time series exactly across time and that can be used to study both time-varying variances of water temperature at each depth and time-varying correlations between temperatures at different depths. Each DSA component is amenable for studying a certain aspect of the relationship between the series at different depths. The daily component in general is weakly correlated between depths, including those that are adjacent to one another. The subannual component quantifies seasonal effects and in particular isolates phenomena associated with the thermocline, thus simplifying its study across time. The annual component can be used for a trend analysis. The descriptive analysis provided by the DSA decomposition is a useful precursor to a more formal statistical analysis.

  18. Tuning of a Wavelet Filter for Miniature Accelerometers Denoising based Joint Symbolic Dynamics (JSD Method

    Directory of Open Access Journals (Sweden)

    Ioana Raluca EDU

    2015-06-01

    Full Text Available The paper exposes a wavelet filtering mechanism related to the noise suppression in the acceleration sensors, with direct application in the strap-down inertial navigation systems. The presented procedure is related to the actual trend in the inertial navigation field to use miniaturized inertial measurement units, which includes MEMS or NEMS sensors. Beside the already wavelet filtering used method, based on different thresholding mechanisms, the here proposed work refers to the use of an alternative tuning mechanism for the wavelet filters, based on the Joint Symbolic Dynamics (JSD method. The main idea of the proposed method is to process and analyze signals received from the sensors in the inertial measurement unit of the navigator by using the Wavelet transform until optimal levels of decomposition are established and the useful signals are achieved.

  19. Fractal analysis of polyethylene catalysts surface morphologies based on wavelet transform modulus maxima method

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Surface morphologies of supported polyethylene (PE) catalysts are investigated by an approach combining fractal with wavelet. The multiscale edge (detail) pictures of catalyst surface are extracted by wavelet transform modulus maxima (WTMM) method. And, the distribution of edge points on the edge image at every scale is studied with fractal and multifractal method. Furthermore, the singularity intensity distribution of edge points in the PE catalyst is analyzed by multifractal spectrum based on WTMM. The results reveal that the fractal dimension values and multifractal spectrums of edge images at small scales have a good relation with the activity and surface morphology of PE catalyst. Meanwhile the catalyst exhibiting the higher activity shows the wider singular strength span of multifractal spectrum based on WTMM, as well as the more edge points with the higher singular intensity. The research on catalyst surface morphology with hybrid fractal and wavelet method exerts the superiorities of wavelet and fractal theories and offers a thought for studying solid surfaces morphologies.

  20. 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.

  1. R-peaks detection based on stationary wavelet transform.

    Science.gov (United States)

    Merah, M; Abdelmalik, T A; Larbi, B H

    2015-10-01

    Automatic detection of the QRS complexes/R-peaks in an electrocardiogram (ECG) signal is the most important step preceding any kind of ECG processing and analysis. The performance of these systems heavily relies on the accuracy of the QRS detector. The objective of present work is to drive a new robust method based on stationary wavelet transform (SWT) for R-peaks detection. The decimation of the coefficients at each level of the transformation algorithm is omitted, more samples in the coefficient sequences are available and hence a better outlier detection can be performed. Using the information of local maxima, minima and zero crossings of the fourth SWT coefficient detail, the proposed algorithm identifies the significant points for detection and delineation of the QRS complexes, as well as detection and identification of the QRS individual waves peaks of the pre-processed ECG signal. Various experimental results show that the proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, achieving excellent performance on different databases, on the MIT-BIH database (Se=99.84%, P=99.88%), on the QT Database (Se=99.94%, P=99.89%) and on MIT-BIH Noise Stress Test Database, (Se=95.30%, P=93.98%). Reliability and accuracy are close to the highest among the ones obtained in other studies. Experiments results being satisfactory, the SWT may represent a novel QRS detection tool, for a robust ECG signal analysis. PMID:26105724

  2. A Wavelet-Based Approach for Ultrasound Image Restoration

    Directory of Open Access Journals (Sweden)

    Mohammed Tarek GadAllah

    2014-08-01

    Full Text Available Ultrasound's images are generally affected by speckle noise which is mainly due to the scattering phenomenon’s coherent nature. Speckle filtration is accompanied with loss of diagnostic features. In this paper a modest new trial introduced to remove speckles while keeping the fine features of the tissue under diagnosis by enhancing image’s edges; via Curvelet denoising and Wavelet based image fusion. Performance evaluation of our work is done by four quantitative measures: the peak signal to noise ratio (PSNR, the square root of the mean square of error (RMSE, a universal image quality index (Q, and the Pratt’s figure of merit (FOM as a quantitative measure for edge preservation. Plus Canny edge map which is extracted as a qualitative measure of edge preservation. The measurements of the proposed approach assured its qualitative and quantitative success into image denoising while maintaining edges as possible. A Gray phantom is designed to test our proposed enhancement method. The phantom results assure the success and applicability of this paper approach not only to this research works but also for gray scale diagnostic scans’ images including ultrasound’s B-scans.

  3. A wavelet based investigation of long memory in stock returns

    Science.gov (United States)

    Tan, Pei P.; Galagedera, Don U. A.; Maharaj, Elizabeth A.

    2012-04-01

    Using a wavelet-based maximum likelihood fractional integration estimator, we test long memory (return predictability) in the returns at the market, industry and firm level. In an analysis of emerging market daily returns over the full sample period, we find that long-memory is not present and in approximately twenty percent of 175 stocks there is evidence of long memory. The absence of long memory in the market returns may be a consequence of contemporaneous aggregation of stock returns. However, when the analysis is carried out with rolling windows evidence of long memory is observed in certain time frames. These results are largely consistent with that of detrended fluctuation analysis. A test of firm-level information in explaining stock return predictability using a logistic regression model reveal that returns of large firms are more likely to possess long memory feature than in the returns of small firms. There is no evidence to suggest that turnover, earnings per share, book-to-market ratio, systematic risk and abnormal return with respect to the market model is associated with return predictability. However, degree of long-range dependence appears to be associated positively with earnings per share, systematic risk and abnormal return and negatively with book-to-market ratio.

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

    OpenAIRE

    Fei Chen; Yuan-Ting Zhang

    2003-01-01

    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 s...

  5. A wavelet-based method for estimating damping in power systems

    OpenAIRE

    Turunen, Jukka

    2011-01-01

    This thesis presents a novel approach to electromechanical oscillation damping estimation under the ambient conditions of a power system. The power system is said to operate under the ambient conditions when it is only subjected to ever present small excitations such as constantly varying load. The damping estimation method is based on the wavelet transform and the random decrement technique. The thesis reviews the properties of the wavelet transform that are essential in damping estimat...

  6. A Wavelet Based Multiscale Weighted Permutation Entropy Method for Sensor Fault Feature Extraction and Identification

    OpenAIRE

    Qiaoning Yang; Jianlin Wang

    2016-01-01

    Sensor is the core module in signal perception and measurement applications. Due to the harsh external environment, aging, and so forth, sensor easily causes failure and unreliability. In this paper, three kinds of common faults of single sensor, bias, drift, and stuck-at, are investigated. And a fault diagnosis method based on wavelet permutation entropy is proposed. It takes advantage of the multiresolution ability of wavelet and the internal structure complexity measure of permutation entr...

  7. Biomedical Image Processing Using FCM Algorithm Based on the Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    YAN Yu-hua; WANG Hui-min; LI Shi-pu

    2004-01-01

    An effective processing method for biomedical images and the Fuzzy C-mean (FCM) algorithm based on the wavelet transform are investigated.By using hierarchical wavelet decomposition, an original image could be decomposed into one lower image and several detail images. The segmentation started at the lowest resolution with the FCM clustering algorithm and the texture feature extracted from various sub-bands. With the improvement of the FCM algorithm, FCM alternation frequency was decreased and the accuracy of segmentation was advanced.

  8. EXTENDED WAVELET TRANSFORM BASED IMAGE INPAINTING ALGORITHM FOR NATURAL SCENE IMAGE COMPLETION

    OpenAIRE

    K. Sangeetha; Dr.P.Sengottuvelan; E.Balamurugan

    2012-01-01

    This paper proposes an exemplar based image inpainting using extended wavelet transform. The Image inpainting modifies an image with the available information outside the region to be inpainted in an undetectable way. The extended wavelet transform is in two dimensions. The Laplacian pyramid is first used to capture the point discontinuities, and then followed by a directional filter bank to link point discontinuities into linear structures. The proposed model effectively captu...

  9. Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2012-03-01

    Full Text Available A least square method based on data fitting is proposed to construct a new lifting wavelet, together with the nonlinear idea and redundant algorithm, the adaptive redundant lifting transform based on fitting is firstly stated in this paper. By variable combination selections of basis function, sample number and dimension of basis function, a total of nine wavelets with different characteristics are constructed, which are respectively adopted to perform redundant lifting wavelet transforms on low-frequency approximate signals at each layer. Then the normalized lP norms of the new node-signal obtained through decomposition are calculated to adaptively determine the optimal wavelet for the decomposed approximate signal. Next, the original signal is taken for subsection power spectrum analysis to choose the node-signal for single branch reconstruction and demodulation. Experiment signals and engineering signals are respectively used to verify the above method and the results show that bearing faults can be diagnosed more effectively by the method presented here than by both spectrum analysis and demodulation analysis. Meanwhile, compared with the symmetrical wavelets constructed with Lagrange interpolation algorithm, the asymmetrical wavelets constructed based on data fitting are more suitable in feature extraction of fault signal of roller bearings.

  10. Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction

    Science.gov (United States)

    Rizal Isnanto, R.

    2015-06-01

    Human iris has a very unique pattern which is possible to be used as a biometric recognition. To identify texture in an image, texture analysis method can be used. One of method is wavelet that extract the image feature based on energy. Wavelet transforms used are Haar, Daubechies, Coiflets, Symlets, and Biorthogonal. In the research, iris recognition based on five mentioned wavelets was done and then comparison analysis was conducted for which some conclusions taken. Some steps have to be done in the research. First, the iris image is segmented from eye image then enhanced with histogram equalization. The features obtained is energy value. The next step is recognition using normalized Euclidean distance. Comparison analysis is done based on recognition rate percentage with two samples stored in database for reference images. After finding the recognition rate, some tests are conducted using Energy Compaction for all five types of wavelets above. As the result, the highest recognition rate is achieved using Haar, whereas for coefficients cutting for C(i) < 0.1, Haar wavelet has a highest percentage, therefore the retention rate or significan coefficient retained for Haaris lower than other wavelet types (db5, coif3, sym4, and bior2.4)

  11. NEW TECHNOLOGY FOR FAULT DIAGNOSIS BASED ON WAVELET DENOISING AND MODIFIED EXPONENTIAL TIME-FREQUENCY DISTRIBUTION

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Fast wavelet multi-resolution analysis (wavelet MRk)provides a effective tool for analyzing and canceling disturbing components in original signal. Because of its exponential frequency axis, this method isn't suitable for extracting harmonic components. The modified exponential time-frequency distribution(MED)overcomes the problems of Wigner distribution(WD), can suppress cross-terms and cancel noise further more. MED provides high resolution in both time and frequency domains, so it can make out weak period impulse components from signal with mighty harmonic components. According to the "time" behavior, together with "frequency" behavior in one figure, the essential structure of a signal is revealed clearly. According to the analysis of algorithm and fault diagnosis example, the joint of wavelet MRA and MED is a powerful tool for fault diagnosis.

  12. Wavelet-based calculation of cerebral angiographic data from time-resolved CT perfusion acquisitions

    Energy Technology Data Exchange (ETDEWEB)

    Havla, Lukas; Dietrich, Olaf [Ludwig-Maximilians-University Hospital Munich, Josef-Lissner-Laboratory for Biomedical Imaging, Institute for Clinical Radiology, Munich (Germany); Thierfelder, Kolja M.; Beyer, Sebastian E.; Sommer, Wieland H. [Ludwig-Maximilians-University Hospital Munich, Institute for Clinical Radiology, Munich (Germany)

    2015-08-15

    To evaluate a new approach for reconstructing angiographic images by application of wavelet transforms on CT perfusion data. Fifteen consecutive patients with suspected stroke were examined with a multi-detector CT acquiring 32 dynamic phases (∇t = 1.5s) of 99 slices (total slab thickness 99mm) at 80kV/200mAs. Thirty-five mL of iomeprol-350 was injected (flow rate = 4.5mL/s). Angiographic datasets were calculated after initial rigid-body motion correction using (a) temporally filtered maximum intensity projections (tMIP) and (b) the wavelet transform (Paul wavelet, order 1) of each voxel time course. The maximum of the wavelet-power-spectrum was defined as the angiographic signal intensity. The contrast-to-noise ratio (CNR) of 18 different vessel segments was quantified and two blinded readers rated the images qualitatively using 5pt Likert scales. The CNR for the wavelet angiography (501.8 ± 433.0) was significantly higher than for the tMIP approach (55.7 ± 29.7, Wilcoxon test p < 0.00001). Image quality was rated to be significantly higher (p < 0.001) for the wavelet angiography with median scores of 4/4 (reader 1/reader 2) than the tMIP (scores of 3/3). The proposed calculation approach for angiography data using temporal wavelet transforms of intracranial CT perfusion datasets provides higher vascular contrast and intrinsic removal of non-enhancing structures such as bone. (orig.)

  13. Content-based image classification with circular harmonic wavelets

    Science.gov (United States)

    Jacovitti, Giovanni; Neri, Alessandro

    1998-07-01

    Classification of an image on the basis of contained patterns is considered in a context of detection and estimation theory. To simplify mathematical derivations, image and reference patterns are represented on a complex support. This allows to convert the four positional parameters into two complex numbers: complex displacement and complex scale factor. The latter one represents isotropic dilations with its magnitude, and rotations with its phase. In this context, evaluation of the likelihood function under additive Gaussian noise assumption allows to relate basic template matching strategy to wavelet theory. It is shown that using circular harmonic wavelets simplifies the problem from a computational viewpoint. A general purpose pattern detection/estimation scheme is introduced by decomposing the images on a orthogonal basis formed by complex Laguerre-Gauss Harmonic wavelets.

  14. Iris image recognition wavelet filter-banks based iris feature extraction schemes

    CERN Document Server

    Rahulkar, Amol D

    2014-01-01

    This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. It provides the broad treatment on the design of separable, non-separable wavelets filter banks, and the classifier. The design techniques presented in the book are applied on iris image analysis for person authentication. This book also brings together the three strands of research (wavelets, iris image analysis, and classifier). It compares the performance of the presented techniques with state-of-the-art available schemes. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books. Therefore, it provide an easier path for the new-comers, researchers to master the contents. In addition, the designed filter banks and classifier can also be effectively used than existing filter-banks in many signal processing applications like pattern classification, data-compression, watermarking, denoising etc.  that will...

  15. Wavelet Transform-Based Distributed Compressed Sensing in Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Hu Haifeng; Yang Zhen; Bao Jianmin

    2012-01-01

    Wireless Sensor Networks (WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink. In this paper, we present a Distributed Wavelet Basis Gener- ation (DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN. And on this basis, a Wavelet Transform-based Distributed Compressed Sensing (WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation. Finally, we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio, the channel Signal-to-Noise Ratio (SNR), the observation noise power and the correlation decay parameter by simulation. The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy, as compared to the conventional distributed wavelet transform algorithm.

  16. An Ensemble 4D Seismic History Matching Framework with Sparse Representation Based on Wavelet Multiresolution Analysis

    CERN Document Server

    Luo, Xiaodong; Jakobsen, Morten; Nævdal, Geir

    2016-01-01

    In this work we propose an ensemble 4D seismic history matching framework for reservoir characterization. Compared to similar existing frameworks in reservoir engineering community, the proposed one consists of some relatively new ingredients, in terms of the type of seismic data in choice, wavelet multiresolution analysis for the chosen seismic data and related data noise estimation, and the use of recently developed iterative ensemble history matching algorithms. Typical seismic data used for history matching, such as acoustic impedance, are inverted quantities, whereas extra uncertainties may arise during the inversion processes. In the proposed framework we avoid such intermediate inversion processes. In addition, we also adopt wavelet-based sparse representation to reduce data size. Concretely, we use intercept and gradient attributes derived from amplitude versus angle (AVA) data, apply multilevel discrete wavelet transforms (DWT) to attribute data, and estimate noise level of resulting wavelet coeffici...

  17. Multicomponent Kinetic Determination by Wavelet Packet Transform Based Elman Recurrent Neural Network Method

    Institute of Scientific and Technical Information of China (English)

    REN Shou-xin; GAO Ling

    2004-01-01

    This paper covers a novel method named wavelet packet transform based Elman recurrent neural network(WPTERNN) for the simultaneous kinetic determination of periodate and iodate. The wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. The Elman recurrent network was applied to non-linear multivariate calibration. In this case, by means of optimization, the wavelet function, decomposition level and number of hidden nodes for WPTERNN method were selected as D4, 5 and 5 respectively. A program PWPTERNN was designed to perform multicomponent kinetic determination. The relative standard error of prediction(RSEP) for all the components with WPTERNN, Elman RNN and PLS were 3.23%, 11.8% and 10.9% respectively. The experimental results show that the method is better than the others.

  18. A Numerical Method Based on Daubechies Wavelet Basis and B-Spline Patches for Elasticity Problems

    Directory of Open Access Journals (Sweden)

    Yanan Liu

    2016-01-01

    Full Text Available The Daubechies (DB wavelets are used for solving 2D plane elasticity problems. In order to improve the accuracy and stability in computation, the DB wavelet scaling functions in 0,+∞ comprising boundary scaling functions are chosen as basis functions for approximation. The B-spline patches used in isogeometry analysis method are constructed to describe the problem domain. Through the isoparametric analysis approach, the function approximation and relevant computation based on DB wavelet functions are implemented on B-spline patches. This work makes an attempt to break the limitation that problems only can be discretized on uniform grids in the traditional wavelet numerical method. Numerical examples of 2D elasticity problems illustrate that this kind of analysis method is effective and stable.

  19. Assessing artificial neural networks coupled with wavelet analysis for multi-layer soil moisture dynamics prediction

    Institute of Scientific and Technical Information of China (English)

    JunJun Yang; ZhiBin He; WeiJun Zhao; Jun Du; LongFei Chen; Xi Zhu

    2016-01-01

    Soil moisture simulation and prediction in semi-arid regions are important for agricultural production, soil conservation and climate change. However, considerable heterogeneity in the spatial distribution of soil moisture, and poor ability of distributed hydrological models to estimate it, severely impact the use of soil moisture models in research and practical applications. In this study, a newly-developed technique of coupled (WA-ANN) wavelet analysis (WA) and artificial neural network (ANN) was applied for a multi-layer soil moisture simulation in the Pailugou catchment of the Qilian Mountains, Gansu Province, China. Datasets included seven meteorological factors: air and land surface temperatures, relative humidity, global radiation, atmospheric pressure, wind speed, precipitation, and soil water content at 20, 40, 60, 80, 120 and 160 cm. To investigate the effectiveness of WA-ANN, ANN was applied by itself to conduct a comparison. Three main findings of this study were: (1) ANN and WA-ANN provided a statistically reliable and robust prediction of soil moisture in both the root zone and deepest soil layer studied (NSE >0.85, NSE means Nash-Sutcliffe Efficiency coefficient); (2) when input meteorological factors were transformed using maximum signal to noise ratio (SNR) and one-dimensional auto de-noising algorithm (heursure) in WA, the coupling technique improved the performance of ANN especially for soil moisture at 160 cm depth; (3) the results of multi-layer soil moisture prediction indicated that there may be different sources of water at different soil layers, and this can be used as an indicator of the maximum impact depth of meteorological factors on the soil water content at this study site. We conclude that our results show that appropriate simulation methodology can provide optimal simulation with a minimum distortion of the raw-time series; the new method used here is applicable to soil sciences and management applications.

  20. 多分辨率分析和小波能量曲率的框架结构损伤识别%Damage identification of the frame structure based on multi-resolution analysis and wavelet energy curvature

    Institute of Scientific and Technical Information of China (English)

    常鹏; 杨娜; 张国培

    2016-01-01

    为研究运用多分辨率和小波包分析方法识别结构损伤的有效性,以三层混凝土框架结构为研究对象,分别建立不同损伤工况下的三维有限元数值模型,采用ANSYS程序进行动力时程分析,研究不同识别指标和输入信号的识别灵敏度以及其他因素对损伤识别结果的影响。分析结果表明:小波包能量曲率差法能够较好达到损伤识别的目的,输入信号使用加速度响应信号比速度和位移响应信号具有更好的识别效果;有限元模型网格划分、加速度时程响应信号的提取位置以及采样频率对损伤位置的识别有较大影响;有限元网格划分越密、加速度时程响应信号提取位置离损伤位置越近、采样频率越大,损伤位置识别越精确。%The three⁃story frame models with different damage conditions were established to study the application of wavelet analysis in damage identification. The time history analysis was applied, and the response data were used to identify the damage location through wavelet analysis. The different damage identification indexes are discussed, and the results show that the wavelet packet energy curvature is better than the other to identify local damage. Then the sensitivities of input signals are discussed, and the results show that the acceleration is more sensitive to local damage than velocity and displacement. In the end, the meshing size, the acceleration sensor location and the sampling frequency are discussed, and the results show that all of them affect the damage location identification result. The accuracy of the damage localization increases when the meshing size decreases, the distance between the acceleration sensor location and the damage location decreases, and the sampling frequency increases.

  1. Application of MultiScale Hidden Markov Modeling Wavelet Coefficients to fMRI Activation Detection

    Directory of Open Access Journals (Sweden)

    Fangyuan Nan

    2008-01-01

    Full Text Available Problem Statement: The problem of detection of functional magnetic resonance images (fMRIs, that is, to decide active and nonactive regions of human brain from fMRIs is studied in this paper. fMRI research is finding and will find more and more applications in diagnosing and treating brain diseases like depression and schizophrenia. At its initial stage fMRI detection are pixel-wise methods, which do not take advantage of mutual information among neighboring pixels. Ignoring such spatial information can reduce detection accuracy. During past decade, many efforts have been focusing on taking advantage of spatial correlation inherent in fMRI data. Most well known is smoothing using a fixed Gaussian filter and the compensation for multiple testing using Gaussian random field theory as used by Statistical Parametric Mapping (SPM. Other methods including wavelets had also been proposed by the community. Approach: In this study a novel two-step approach was put forward that incorporates spatial correlation information and is amenable to analysis and optimization. First, a new multi scale image segmentation algorithm was proposed to decompose the correlation image into several different regions, each of which is of homogeneous statistical behavior. Second, each region will be classified independently as active or inactive using existing pixel-wise test methods. The image segmentation consists of two procedures: Edge detection followed by label estimation. To deduce the presence or absence of an edge from continuous data, two fundamental assumption of our algorithm are 1 each wavelet coefficient was described by a 2-state Gaussian Mixture Model (GMM; 2 across scale, each state is caused by its parent state, hence the Multiscale Hidden Markov Model (MHMM. The states of Markov chain are unknown ("hidden" and represent the presence (state 1 or absence (state 0 of edges. Using this interpretation, the edge detection problem boils down to the posterior state

  2. Nondestructive Damage Assessment of Composite Structures Based on Wavelet Analysis of Modal Curvatures: State-of-the-Art Review and Description of Wavelet-Based Damage Assessment Benchmark

    Directory of Open Access Journals (Sweden)

    Andrzej Katunin

    2015-01-01

    Full Text Available The application of composite structures as elements of machines and vehicles working under various operational conditions causes degradation and occurrence of damage. Considering that composites are often used for responsible elements, for example, parts of aircrafts and other vehicles, it is extremely important to maintain them properly and detect, localize, and identify the damage occurring during their operation in possible early stage of its development. From a great variety of nondestructive testing methods developed to date, the vibration-based methods seem to be ones of the least expensive and simultaneously effective with appropriate processing of measurement data. Over the last decades a great popularity of vibration-based structural testing has been gained by wavelet analysis due to its high sensitivity to a damage. This paper presents an overview of results of numerous researchers working in the area of vibration-based damage assessment supported by the wavelet analysis and the detailed description of the Wavelet-based Structural Damage Assessment (WavStructDamAs Benchmark, which summarizes the author’s 5-year research in this area. The benchmark covers example problems of damage identification in various composite structures with various damage types using numerous wavelet transforms and supporting tools. The benchmark is openly available and allows performing the analysis on the example problems as well as on its own problems using available analysis tools.

  3. 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.

  4. CHARACTERIZATION OF RENAL BLOOD FLOW REGULATION BASED ON WAVELET COEFFICIENTS

    DEFF Research Database (Denmark)

    Pavlov, A.N.; Pavlova, O.N.; Mosekilde, Erik;

    2010-01-01

    The purpose of this study is to demonstrate the possibility of revealing new characteristic features of renal blood flow autoregulation in healthy and pathological states through the application of discrete wavelet transforms to experimental time series for normotensive and hypertensive rats....... A reduction in the variability of the wavelet coefficients in hypertension is observed at both the microscopic level of the blood flow in efferent arterioles of individual nephrons and at the macroscopic level of the blood pressure in the main arteries. The reduction is manifest in both of the main frequency...

  5. Wavelet Cross-Spectrum Analysis of Multi-Scale Disturbance Instability and Transition on Sharp Cone Hypersonic Boundary Layer

    Institute of Scientific and Technical Information of China (English)

    HAN Jian; JIANG Nan

    2008-01-01

    Experimental measurement of hypersonic boundary layer stability and transition on a sharp cone with a half angle of 5° is carried out at free-coming stream Mach number 6 in a hypersonic wind tunnel.Mean and fluctuation surface-thermal-flux characteristics of the hypersonic boundary layer flow are measured by Pt-thin-film thermocouple temperature sensors installed at 28 stations on the cone surface along longitudinal direction.At hypersonic speeds,the dominant flow instabilities demonstrate that the growth rate of the second mode tends to exceed that of the low-frequency mode.Wavelet-based cross-spectrum technique is introduced to obtain the multi-scale cross-spectral characteristics of the fluctuating signals in the frequency range of the second mode.Nonlinear interactions both of the second mode disturbance and the first mode disturbance axe demonstrated to be dominant instabilities in the initial stage of laminar-turbulence transition for hypersonic shear flow.

  6. PALMPRINT VERIFICATION USING INVARIANT MOMENTS BASED ON WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    Inass SH. Hussein

    2014-01-01

    Full Text Available Data security is one of the important issues among computer users. Data security can prevent fraudulent users from accessing an individual’s personal data. The biometrics recognition as one of the most important parts in the security of the data and the application of computer vision. The biometrics is the authentication method used in a wide variety of applications such as e-banking, e-commerce, e-government and many others. A biometric system is one which requires the recognition of a pattern, whereby it enables the differentiation of features from one individual to another. Biometric technologies, thus may be defined as the automated methods of identifying, or authenticating, the identity of a living person based on physiological or behavioral traits. This study emphasizes palmprint recognition, which provides a wide deployment range of authentication methods. The palmprint contains principal lines, wrinkles, fine lines, ridges and surface area; thus the palmprint of person differs from one to another. Previous researchers have difficulty extracting the features of a palm print, because of the effects of rotation, translation and scaling changes and the accuracy rate of verification performance needs to be improved. The aim of this study is to extract shape features using an invariant moments algorithm based on wavelet transform and identify the person’s verification. This model has shown a promising results without the effects of rotation, translation and scaling of objects, because it is associated with the use of a good description of shape features. This system has been tested using databases from the Indian Institute of Technology, Kanpur (IITK, by using the False Rejection Rate (FRR and False Acceptance Rate (FAR, we may calculate the accuracy rate of verification. The experiment shows a 97.99% accuracy rate of verification.

  7. Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis

    Energy Technology Data Exchange (ETDEWEB)

    Placidi, Giuseppe; Alecci, Marcello; Sotgiu, Antonello [INFM, c/o Centro di Risonanza Magnetica and Dipartimento di Scienze e Tecnologie Biomediche, Universita dell' Aquila, Via Vetoio 10, 67010 Coppito, L' Aquila (Italy)

    2003-07-07

    A post-processing noise suppression technique for biomedical MRI images is presented. The described procedure recovers both sharp edges and smooth surfaces from a given noisy MRI image; it does not blur the edges and does not introduce spikes or other artefacts. The fine details of the image are also preserved. The proposed algorithm first extracts the edges from the original image and then performs noise reduction by using a wavelet de-noise method. After the application of the wavelet method, the edges are restored to the filtered image. The result is the original image with less noise, fine detail and sharp edges. Edge extraction is performed by using an algorithm based on Sobel operators. The wavelet de-noise method is based on the calculation of the correlation factor between wavelet coefficients belonging to different scales. The algorithm was tested on several MRI images and, as an example of its application, we report the results obtained from a spin echo (multi echo) MRI image of a human wrist collected with a low field experimental scanner (the signal-to-noise ratio, SNR, of the experimental image was 12). Other filtering operations have been performed after the addition of white noise on both channels of the experimental image, before the magnitude calculation. The results at SNR = 7, SNR = 5 and SNR = 3 are also reported. For SNR values between 5 and 12, the improvement in SNR was substantial and the fine details were preserved, the edges were not blurred and no spikes or other artefacts were evident, demonstrating the good performances of our method. At very low SNR (SNR = 3) our result is worse than that obtained by a simpler filtering procedure.

  8. Detection of K-complexes based on the wavelet transform

    DEFF Research Database (Denmark)

    Krohne, Lærke K.; Hansen, Rie B.; Christensen, Julie Anja Engelhard;

    2014-01-01

    Sleep scoring needs computational assistance to reduce execution time and to assure high quality. In this pilot study a semi-automatic K-Complex detection algorithm was developed using wavelet transformation to identify pseudo-K-Complexes and various feature thresholds to reject false positives...

  9. Wavelet Packet based Detection of Surface Faults on Compact Discs

    DEFF Research Database (Denmark)

    Odgaard, Peter Fogh; Stoustrup, Jakob; Wickerhauser, Mladen Victor

    2006-01-01

    by the use of dedicated filters adapted to remove the faults from the measurements. In this paper detection using wavelet packet filters is demonstrated. The filters are designed using the joint best basis method. Detection using these filters shows a distinct improvement compared to detection using ordinary...

  10. Wavelet based deseasonalization for modelling and forecasting of daily discharge series considering long range dependence

    Directory of Open Access Journals (Sweden)

    Szolgayová Elena

    2014-03-01

    Full Text Available Short term streamflow forecasting is important for operational control and risk management in hydrology. Despite a wide range of models available, the impact of long range dependence is often neglected when considering short term forecasting. In this paper, the forecasting performance of a new model combining a long range dependent autoregressive fractionally integrated moving average (ARFIMA model with a wavelet transform used as a method of deseasonalization is examined. It is analysed, whether applying wavelets in order to model the seasonal component in a hydrological time series, is an alternative to moving average deseasonalization in combination with an ARFIMA model. The one-to-ten-steps-ahead forecasting performance of this model is compared with two other models, an ARFIMA model with moving average deseasonalization, and a multiresolution wavelet based model. All models are applied to a time series of mean daily discharge exhibiting long range dependence. For one and two day forecasting horizons, the combined wavelet - ARFIMA approach shows a similar performance as the other models tested. However, for longer forecasting horizons, the wavelet deseasonalization - ARFIMA combination outperforms the other two models. The results show that the wavelets provide an attractive alternative to the moving average deseasonalization.

  11. Improved deadzone modeling for bivariate wavelet shrinkage-based image denoising

    Science.gov (United States)

    DelMarco, Stephen

    2016-05-01

    Modern image processing performed on-board low Size, Weight, and Power (SWaP) platforms, must provide high- performance while simultaneously reducing memory footprint, power consumption, and computational complexity. Image preprocessing, along with downstream image exploitation algorithms such as object detection and recognition, and georegistration, place a heavy burden on power and processing resources. Image preprocessing often includes image denoising to improve data quality for downstream exploitation algorithms. High-performance image denoising is typically performed in the wavelet domain, where noise generally spreads and the wavelet transform compactly captures high information-bearing image characteristics. In this paper, we improve modeling fidelity of a previously-developed, computationally-efficient wavelet-based denoising algorithm. The modeling improvements enhance denoising performance without significantly increasing computational cost, thus making the approach suitable for low-SWAP platforms. Specifically, this paper presents modeling improvements to the Sendur-Selesnick model (SSM) which implements a bivariate wavelet shrinkage denoising algorithm that exploits interscale dependency between wavelet coefficients. We formulate optimization problems for parameters controlling deadzone size which leads to improved denoising performance. Two formulations are provided; one with a simple, closed form solution which we use for numerical result generation, and the second as an integral equation formulation involving elliptic integrals. We generate image denoising performance results over different image sets drawn from public domain imagery, and investigate the effect of wavelet filter tap length on denoising performance. We demonstrate denoising performance improvement when using the enhanced modeling over performance obtained with the baseline SSM model.

  12. 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.

  13. A Comparative Analysis of Exemplar Based and Wavelet Based Inpainting Technique

    Directory of Open Access Journals (Sweden)

    Vaibhav V Nalawade

    2012-06-01

    Full Text Available Image inpainting is the process of filling in of missing region so as to preserve its overall continuity. Image inpainting is manipulation and modification of an image in a form that is not easily detected. Digital image inpainting is relatively new area of research, but numerous and different approaches to tackle the inpainting problem have been proposed since the concept was first introduced. This paper compares two separate techniques viz, Exemplar based inpainting technique and Wavelet based inpainting technique, each portraying a different set of characteristics. The algorithms analyzed under exemplar technique are large object removal by exemplar based inpainting technique (Criminisi’s and modified exemplar (Cheng. The algorithm analyzed under wavelet is Chen’s visual image inpainting method. A number of examples on real and synthetic images are demonstrated to compare the results of different algorithms using both qualitative and quantitative parameters.

  14. A New Decomposition of Multi-Dimensional Tensorial Meyer Wavelets%高维张量积Meyer小波的一种新分解(英文)

    Institute of Scientific and Technical Information of China (English)

    杨占英; 于云霞

    2012-01-01

    A new decomposition of multi-dimensional tensorial Meyer wavelets is given.The proof mainly depends on some important properties of Meyer wavelets,such as infinite differentiability,rapidly decreasing property and vanishing moments property.%基于Meyer小波的无穷可微性、急减性和消失矩性等重要性质,给出了高维张量积Meyer小波的一种新的分解形式.

  15. A Wavelet Kernel-Based Primal Twin Support Vector Machine for Economic Development Prediction

    Directory of Open Access Journals (Sweden)

    Fang Su

    2013-01-01

    Full Text Available Economic development forecasting allows planners to choose the right strategies for the future. This study is to propose economic development prediction method based on the wavelet kernel-based primal twin support vector machine algorithm. As gross domestic product (GDP is an important indicator to measure economic development, economic development prediction means GDP prediction in this study. The wavelet kernel-based primal twin support vector machine algorithm can solve two smaller sized quadratic programming problems instead of solving a large one as in the traditional support vector machine algorithm. Economic development data of Anhui province from 1992 to 2009 are used to study the prediction performance of the wavelet kernel-based primal twin support vector machine algorithm. The comparison of mean error of economic development prediction between wavelet kernel-based primal twin support vector machine and traditional support vector machine models trained by the training samples with the 3–5 dimensional input vectors, respectively, is given in this paper. The testing results show that the economic development prediction accuracy of the wavelet kernel-based primal twin support vector machine model is better than that of traditional support vector machine.

  16. Optimal Base Wavelet Selection for ECG Noise Reduction Using a Comprehensive Entropy Criterion

    Directory of Open Access Journals (Sweden)

    Hong He

    2015-09-01

    Full Text Available The selection of an appropriate wavelet is an essential issue that should be addressed in the wavelet-based filtering of electrocardiogram (ECG signals. Since entropy can measure the features of uncertainty associated with the ECG signal, a novel comprehensive entropy criterion Ecom based on multiple criteria related to entropy and energy is proposed in this paper to search for an optimal base wavelet for a specific ECG signal. Taking account of the decomposition capability of wavelets and the similarity in information between the decomposed coefficients and the analyzed signal, the proposed Ecom criterion integrates eight criteria, i.e., energy, entropy, energy-to-entropy ratio, joint entropy, conditional entropy, mutual information, relative entropy, as well as comparison information entropy for optimal wavelet selection. The experimental validation is conducted on the basis of ECG signals of sixteen subjects selected from the MIT-BIH Arrhythmia Database. The Ecom is compared with each of these eight criteria through four filtering performance indexes, i.e., output signal to noise ratio (SNRo, root mean square error (RMSE, percent root mean-square difference (PRD and correlation coefficients. The filtering results of ninety-six ECG signals contaminated by noise have verified that Ecom has outperformed the other eight criteria in the selection of best base wavelets for ECG signal filtering. The wavelet identified by the Ecom has achieved the best filtering performance than the other comparative criteria. A hypothesis test also validates that SNRo, RMSE, PRD and correlation coefficients of Ecom are significantly different from those of the shape-matched approach (α = 0.05 , two-sided t- test.

  17. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

    Directory of Open Access Journals (Sweden)

    Lixin Gao

    2010-05-01

    Full Text Available Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM and rule-based reasoning (RBR. In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

  18. Developing a multi-Kinect-system for monitoring in dairy cows: object recognition and surface analysis using wavelets.

    Science.gov (United States)

    Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W

    2016-09-01

    Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69. PMID:26837672

  19. Wavelet Packet Based Features for Automatic Script Identification

    OpenAIRE

    M.C. Padma & P. A. Vijaya

    2010-01-01

    In a multi script environment, an archive of documents printed in different scriptsis in practice. For automatic processing of such documents through OpticalCharacter Recognition (OCR), it is necessary to identify the script type of thedocument. In this paper, a novel texture-based approach is presented to identifythe script type of the collection of documents printed in ten Indian scripts -Bangla, Devanagari, Roman (English), Gujarati, Malayalam, Oriya, Tamil,Telugu, Kannada and Urdu. The do...

  20. A wavelet-based damage detection algorithm based on bridge acceleration response to a vehicle

    OpenAIRE

    Hester, David; González, Arturo

    2012-01-01

    Previous research based on theoretical simulations has shown the potential of the wavelet transform to detect damage in a beam by analysing the time-deflection response due to a constant moving load. However, its application to identify damage from the response of a bridge to a vehicle raises a number of questions. Firstly, it may be difficult to record the difference in the deflection signal between a healthy and a slightly damaged structure to the required level of accuracy and high scannin...

  1. An ECT System Based on Improved RBF Network and Adaptive Wavelet Image Enhancement for Solid/Gas Two-phase Flow

    Institute of Scientific and Technical Information of China (English)

    陈夏; 胡红利; 张娟; 周屈兰

    2012-01-01

    Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].

  2. Research on Dynamic Transmission Mechanism between PPI and CPI Based on Wavelet Multi-resolution Analysis%基子小波多分辨分析的PPI和CPI动态传导机制研究

    Institute of Scientific and Technical Information of China (English)

    王晓芳; 王永宁; 李洁

    2011-01-01

    This paper makes a wavelet muhi-resolution analysis of PPl and CPI by Matlab simulation software, and a cross-stage impulse response analysis of low frequency of sub-components. Then it carries out an in-depth research on the volatility characteristics of%文章利用Matlab仿真软件对PPI和CPI进行小波多分辨分析,并将各子成分的低频分量分阶段作脉冲响应研究,探讨了PPI和CPI的波动特征及其关系。结果显示,PPI和CPI波动的波幅、频率和周期均存在较大差异,二者的阶段性传导关系只体现在中长期内;主导PPI和CPI波动的子成分在各阶段均有变化,唯有食品类子成分能够较稳定地主导CPI的波动和预测CPI的走势。通过研究不同时期子成分对总指数的影响可使价格调控和有效治理通货膨胀更具有针对性。

  3. Structural safety criteria for blasting vibration based on wavelet packet energy spectra

    Institute of Scientific and Technical Information of China (English)

    Zhong Guosheng; Li Jiang; Zhao Kui

    2011-01-01

    Given multi-resolution decomposition of wavelet packet transforms, wavelet packet frequency band energy has been deduced from different bands of blasting vibration signals. Our deduction reflects the total effect of all three key elements (intensity, frequency and duration of vibration) of blasting vibration.We considered and discuss the dynamic response of structures and the effect of inherent characteristics of controlled structures to blasting vibration. Frequency band response coefficients for controlled structures by blasting vibration have been obtained. We established multi-factor blasting vibration safety criteria, referred to as response energy criteria. These criteria reflect the total effect of intensity,frequency and duration of vibration and the inherent characteristics (natural frequency and damping ratio) of dynamic responses from controlled structures themselves. Feasibility and reliability of the criteria are validated by an example.

  4. A Steganographic Method Based on Integer Wavelet Transform & Genatic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Arora

    2014-05-01

    Full Text Available The proposed system presents a novel approach of building a secure data hiding technique of steganography using inverse wavelet transform along with Genetic algorithm. The prominent focus of the proposed work is to develop RS-analysis proof design with higest imperceptibility. Optimal Pixal Adjustment process is also adopted to minimize the difference error between the input cover image and the embedded-image and in order to maximize the hiding capacity with low distortions respectively. The analysis is done for mapping function, PSNR, image histogram, and parameter of RS analysis. The simulation results highlights that the proposed security measure basically gives better and optimal results in comparison to prior research work conducted using wavelets and genetic algorithm.

  5. A Wavelet Based Approach to Solar--Terrestrial Coupling

    CERN Document Server

    Katsavrias, Ch; Preka--Papadema, P

    2016-01-01

    Transient and recurrent solar activity drive geomagnetic disturbances; these are quantified (amongst others) by DST, AE indices time-series. Transient disturbances are related to the Interplanetary Coronal Mass Ejections (ICMEs) while recurrent disturbances are related to corotating interaction regions (CIR). We study the relationship of the geomagnetic disturbances to the solar wind drivers within solar Cycle 23 where the drivers are represented by ICMEs and CIRs occurrence rate and compared to the DST and AE as follows: terms with common periodicity in both the geomagnetic disturbances and the solar drivers are, firstly, detected using continuous wavelet transform (CWT). Then, common power and phase coherence of these periodic terms are calculated from the cross-wavelet spectra (XWT) and waveletcoherence (WTC) respectively. In time-scales of about 27 days our results indicate an anti-correlation of the effects of ICMEs and CIRs on the geomagnetic disturbances. The former modulates the DST and AE time series...

  6. Transform of Lightning Electromagnetic Pulses Based on Laplace Wavelet

    Directory of Open Access Journals (Sweden)

    Qin Li

    2013-09-01

    Full Text Available In this study, the fine structures of lightning electromagnetic pulse associated with 19 preliminary breakdown pulses, 37 stepped leaders, 8 dart leaders, 73 first and 52 subsequent return strokes were analyzed by using Laplace wavelet. The main characteristics of field waveforms such as, the correlation coefficient, the time of arrival and the dominant frequency of the initial peak field, the energy and the frequency of the power spectrum peak are presented. The instantaneous initial peak field pulse can be precisely located by the value of the correlation coefficient. The dominant frequencies of the initial peak field of PB pulses and leaders range from 100 kHz to 1 MHz, and that of the first and subsequent return strokes below 100 and 50 kHz, respectively. The statistical results show that the Laplace wavelet is an effective tool and can be used to determine time and frequency of the lightning events with greater accuracy.  

  7. The transverse Talbot effect: Scaling analyses based on wavelet transforms

    CERN Document Server

    Rosu, H C; Ludu, A

    2013-01-01

    Berry and Klein [J. Mod. Opt. 43, 2139 (1996)] studied the fractal properties of the paraxial diffracted field behind a Ronchi grating. In particular, they studied the transverse Talbot images formed at fractional distances in units of the Talbot distance chosen from the Fibonacci convergents to the complement of the inverse golden mean zeta_G=(3-square root of 5)/2. Here, we analyze these Talbot images with two well-known scaling methods, the wavelet transform modulus maxima (WTMM) and the wavelet transform multifractal detrended fluctuation analysis (WT-MFDFA). We use the widths of the singularity spectra, Delta(alpha)=alpha_H-alpha_min, as a characteristic feature of the Talbot images. The tau scaling exponents of the q moments are linear in q within the two methods, which is a strong argument in favor of the monofractality of the transverse diffractive paraxial field

  8. A wavelet packet based block-partitioning image coding algorithm with rate-distortion optimization

    Institute of Scientific and Technical Information of China (English)

    YANG YongMing; XU Chao

    2008-01-01

    As an elegant generalization of wavelet transform, wavelet packet (WP) provides an effective representation tool for adaptive waveform analysis. Recent work shows that image-coding methods based on WP decomposition can achieve significant gain over those based on a usual wavelet transform. However, most of the work adopts a tree-structured quantization scheme, which is a successful technique for wavelet image coding, but not appropriate for WP subbands. This paper presents an image-coding algorithm based on a rate-distortion optimized wavelet packet decomposition and on an intraband block-partitioning scheme. By encoding each WP subband separately with the block-partitioning algorithm and the JPEG2000 context modeling, the proposed algorithm naturally avoids the difficulty in defining parent-offspring relationships for the WP coefficients, which has to be faced when adopting the tree-structured quantization scheme. The experimental results show that the proposed algorithm significantly outperforms SPIHT and JPEG2000 schemes and also surpasses state-of-the-art WP image coding algorithms, in terms of both PSNR and visual quality.

  9. 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.

  10. Wavelet Based EMG Artifact Removal From ECG Signal

    OpenAIRE

    Joy, Josy; Manimegalai, P

    2013-01-01

    Electrocardiogram recordings (ECG) are obtained from the heart. Some sections of the recorded ECG may be corrupted by electromyography (EMG) noise from the muscle. In real situations, exercise test ECG recordings and long term recordings, are often corrupted by muscle artifacts. These EMG noise needs to be filtered before data processing. In this paper, wavelet transform is applied to remove the EMG noise from ECG signal. In this work, an improved thresholding is proposed for removing EMG noi...

  11. Multi-scale wavelet analysis of TOPEX/Poseidon altimeter significant wave height in eastern China seas

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The multi-scale characteristics of wave significant height (Hs) in eastern China seas were revealed by multi-scale wavelet analysis. In order to understand the relation between wave and wind, the TOPEX/Poseidon measurements of Hs and wind speed were analyzed. The result showed that Hs and wind speed change in multi-scale at one-, two-month, half-, one- and two-year cycles. But in a larger time scale, the variations in Hs and wind speed are different. Hs has a five-year cycle similar to the cycle of ENSO variation, while the wind speed has no such cycle. In the time domain, the correlation between Hs and ENSO is unclear.

  12. Colored Image Compression Using Gradient Adjustment Prediction Based Wavelet

    Directory of Open Access Journals (Sweden)

    Muna F.H. Al-Sammraie

    2011-01-01

    Full Text Available Problem statement: Uncompressed graphics, audio and video data require considerable storage capacity and transmission bandwidth. Despite rapid progress in mass-storage density, processor speeds and digital communication system performance, demand for data storage capacity and data transmission bandwidth continues to outstrip the capabilities of available technologies. The recent growth of data intensive digital audio, image and video (multimedia based web applications, have not only sustained the need for more efficient ways to encode signals and images but have made compression of such signals central to signal-storage and digital communication technology. Approach: The objective includes developing and applying an efficient Space-Frequency Segmentation (SFS as an image partitioning scheme, then using an appropriate entropy-coding algorithm that can be used with the developed segmentation to improve compression performance, particularly in the case of still image compression. The proposed compression system focuses on an innovative scheme for adaptive wavelet coding technique combined with spatial encoding. Result: Experiments conducted using the proposed system produced encouraging results. The entropyspatial coders used in the proposed system produced better results than those obtained by using the basic arithmetic coder. It provides more appropriate rate-distortion optimization for the spacefrequency segmentation than the basic arithmetic coder does. The proposed compression system implies some control coding parameters; the effects of these parameters were investigated to determine the suitable range for each one of them. Conclusion: We conclude that a comparison between the energy of two partitioning types (space and frequency shows that the energy of frequency partitioning is greater than the space partitioning from the point of view of quality of compressed image. And also the selection of parameter value used in SFS

  13. Development of content based image retrieval system using wavelet and Gabor transform

    Directory of Open Access Journals (Sweden)

    Manish Sharma

    2013-06-01

    Full Text Available A novel approach to image retrieval using color, texture and spatial information is proposed. The color information of an image is represented by the proposed color hologram, which takes into account both the occurrence of colors of pixels and the colors of their neighboring pixels. The proposed Fuzzy Color homogeneity, encoded by fuzzy sets, is incorporated in the color hologram computation. The texture information is described by the mean, variance and energy of wavelet decomposition coefficients in all sub bands. The spatial information is characterized by the class parameters obtained automatically from a unique unsupervised segmentation algorithm in combination with wavelet decomposition. Multi-stage filtering is applied to query processing to reduce the search range to speed up the query. Color homogram filter, wavelet texture filter, and spatial filter are used in sequence to eliminate images that are dissimilar to a query image in color, texture, and spatial information from the search ranges respectively. The proposed texture distance measure used in the wavelet texture filter considers the relationship between the coefficient value ranges and the decomposition levels, thus improving the retrieval performance.

  14. [Detection of R-wave in Fetal EGG Based on Wavelet Transform and Matched Filtering].

    Science.gov (United States)

    Yan, Wenhong; Jiang, Ning

    2015-09-01

    By analyzing the characteristics of maternal abdominal ECG (Electrocardiogram), a method based on wavelet transform and matched filtering is proposed to detect the R-wave in fetal EGG (FECG). In this method, the high-frequency coefficients are calculated by using wavelet transform. First, the maternal QRS template is obtained by using the arithmetic mean scheme. Finally, the R-wave of FECG is detected based on matched filtering. The experimental results show that this method can effectively eliminate the noises, such as the maternal ECG signal and baseline drift, enhancing the accuracy of the detection of fetal ECG. PMID:26904869

  15. Edge detection based on object tree image representation and wavelet transform

    Institute of Scientific and Technical Information of China (English)

    屈彦呈; 王常虹; 庄显义

    2003-01-01

    In applications such as image retrieval and recognition, precise edge detection for interested regions plays a decisive role. Existing methods generally take little care about local characteristics, or become time-consuming if every detail is considered. In the paper, a new method is put forward based on the combination of effective image representation and multiscale wavelet analysis. A new object tree image representation is introduced. Then a series of object trees are constructed based on wavelet transform modulus maxima at different scales in descending order. Computation is only needed for interested regions. Implementation steps are also given with an illustrative example.

  16. A wavelet based algorithm for the identification of oscillatory event-related potential components.

    Science.gov (United States)

    Aniyan, Arun Kumar; Philip, Ninan Sajeeth; Samar, Vincent J; Desjardins, James A; Segalowitz, Sidney J

    2014-08-15

    Event related potentials (ERPs) are very feeble alterations in the ongoing electroencephalogram (EEG) and their detection is a challenging problem. Based on the unique time-based parameters derived from wavelet coefficients and the asymmetry property of wavelets a novel algorithm to separate ERP components in single-trial EEG data is described. Though illustrated as a specific application to N170 ERP detection, the algorithm is a generalized approach that can be easily adapted to isolate different kinds of ERP components. The algorithm detected the N170 ERP component with a high level of accuracy. We demonstrate that the asymmetry method is more accurate than the matching wavelet algorithm and t-CWT method by 48.67 and 8.03 percent, respectively. This paper provides an off-line demonstration of the algorithm and considers issues related to the extension of the algorithm to real-time applications. PMID:24931710

  17. Application of Wavelet-based Active Power Filter in Accelerator Magnet Power Supply

    CERN Document Server

    Xiaoling, Guo

    2013-01-01

    As modern accelerators demand excellent stability to magnet power supply (PS), it is necessary to decrease harmonic currents passing magnets. Aim at depressing rappel current from PS in Beijing electron-positron collider II, a wavelet-based active power filter (APF) is proposed in this paper. APF is an effective device to improve the quality of currents. As a countermeasure to these harmonic currents, the APF circuit generates a harmonic current, countervailing harmonic current from PS. An active power filter based on wavelet transform is proposed in this paper. Discrete wavelet transform is used to analyze the harmonic components in supply current, and active power filter circuit works according to the analysis results. At end of this paper, the simulation and experiment results are given to prove the effect of the mentioned Active power filter.

  18. De-noising of Raman spectrum signal based on stationary wavelet transform

    Institute of Scientific and Technical Information of China (English)

    Qingwei Gao(高清维); Zhaoqi Sun(孙兆奇); Zhuoliang Cao(曹卓良); Pu Cheng(程蒲)

    2004-01-01

    @@ In this paper,the Raman spectrum signal de-noising based on stationary wavelet transform is discussed.Haar wavelet is selected to decompose the Raman spectrum signal for several levels based on stationarywavelet transform.The noise mean square σj is estimated by the wavelet details at every level,and thewavelet details toward 0 by a threshold σj √2lnn,where n is length of the detail,then recovery signalis reconstructed.Experimental results show this method not only suppresses noise effectively,but alsopreserves as many target characteristics of original signal as possible.This de-noising method offers a veryattractive alternative to Raman spectrum signal noise suppress.

  19. 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.

  20. Super-resolution image restoration algorithm based on orthogonal discrete wavelet transform

    Institute of Scientific and Technical Information of China (English)

    Yangyang Liu(刘扬阳); Weiqi Jin(金伟其); Binghua Su(苏秉华)

    2004-01-01

    By using orthogonal discrete wavelet transform(ODWT)and generalized cross validation(GCV),and combining with Luck-Richardson algorithm based on Poisson-Markovmodel (MPML),several new superresolution image restoration algorithms are proposed.According to simulation experiments for practical images,all the proposed algor ithms could retain image details better than MPML,and be more suitable to low signal-to-noise ratio(SNR)images.The single operation wavelet MPML(SW-MPML)algorithm and MPML algorithm based on single operation wavelet transform(MPML-SW)avoid the iterative operation of self-adaptive parameter in MPML particularly,and improve operating speed and precision.They are instantaneous to super-resolution image restoration process and have extensive application foreground.

  1. Characteristics and method of synthesis seismic wave based on wavelet reconstruction

    Institute of Scientific and Technical Information of China (English)

    ZOU Li-hua; LIU Ai-ping; YANG Hong; CHAI Xin-jian; SHANG Xin; DAI Su-liang; DONG Bo

    2007-01-01

    A novel method of synthesizing seismic wave using wavelet reconstruction is proposed and compared with the traditional method of using theory of Fourier transform. By adjusting the frequency band energy and taking it as criterion, the formula of synthesizing seismic wave is deduced. Using the design parameters specified in Chinese Seismic Design Code for buildings, seismic waves are synthesized. Moreover, the method of selecting wavelet bases in synthesizing seismic wave and the influence of the damping ratio on synthesizing results are analyzed.The results show that the synthesis seismic waves using wavelet bases can represent the characteristics of the seismic wave as well as the ground characteristic period, and have good time-frequency non-stationary.

  2. The Parabolic variance (PVAR), a wavelet variance based on least-square fit

    CERN Document Server

    Vernotte, F; Bourgeois, P -Y; Rubiola, E

    2015-01-01

    The Allan variance (AVAR) is one option among the wavelet variances. However a milestone in the analysis of frequency fluctuations and in the long-term stability of clocks, and certainly the most widely used one, AVAR is not suitable when fast noise processes show up, chiefly because of the poor rejection of white phase noise. The modified Allan variance (MVAR) features high resolution in the presence of white PM noise, but it is poorer for slow phenomena because the wavelet spans over 50% longer time. This article introduces the Parabolic Variance (PVAR), a wavelet variance similar to the Allan variance, based on the Linear Regression (LR) of phase data. The PVAR relates to the Omega frequency counter, which is the topics of a companion article [the reference to the article, or to the ArXiv manuscript, will be provided later]. The PVAR wavelet spans over 2 tau, the same of the AVAR wavelet. After setting the theoretical framework, we analyze the degrees of freedom and the detection of weak noise processes in...

  3. Study on the Laser-Based Weld Surface Flaw Identification System Employing Wavelet Analysis Methodology

    DEFF Research Database (Denmark)

    Qu, Zhigang; Chong, Alvin Yung Boon; Chacon, Juan Luis Ferrando;

    2016-01-01

    . The proposed technique employs the integration of a laser-line profile sensor and processing module based on wavelet analysis. The laser-line sensor acquired the two-dimensional profile of a target weld based on the principle of laser triangulation and yield the height and width information of the weld...

  4. Chaotic Synchronization with Filter Based on Wavelet Transformation

    Institute of Scientific and Technical Information of China (English)

    XiaoanZHOU; JunfengLAN; 等

    1999-01-01

    A kind of chaotic synchronization method is presented in the paper,In the transmitter,part signals are transformed by wavelet and the detail information is removed.In the receiver.the component with low frequency is reconstructed and discrete feedback is used,we show that synchronization of two identical structure chaotic systems is attained.The effect of feedback on chaotic synchronization is discussed.Using the synchronous method,the transmitting signal is transported in compressible way system resource is saved,the component with high frequency is filtered and the effect of disturbance on synchronization is reduced.The synchronization method is illustrated by numerical simulation experiment.

  5. WAVELETS based wireless VOIP and its future scenario

    Directory of Open Access Journals (Sweden)

    Singh Sarvjit

    2016-01-01

    Full Text Available Those who use VOIP, know that a good quality service cannot be ensured over the internet as the internet is not well suited to render real time services. Besides these users do not pay much for the VOIP calls as compared to the circuit switched phone calls. So they do not bother much about the quality of services. However, it is expected that the quality should be sound enough to pay for their time as well as money. In this paper, the recently published literature has been reviewed along with the introduction to wavelets.

  6. Mine-hoist fault-condition detection based on the wavelet packet transform and kernel PCA

    Institute of Scientific and Technical Information of China (English)

    XIA Shi-xiong; NIU Qiang; ZHOU Yong; ZHANG Lei

    2008-01-01

    A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Component Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimension feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.

  7. Wavelet based approach for posture transition estimation using a waist worn accelerometer.

    Science.gov (United States)

    Bidargaddi, Niranjan; Klingbeil, Lasse; Sarela, Antti; Boyle, Justin; Cheung, Vivian; Yelland, Catherine; Karunanithi, Mohanraj; Gray, Len

    2007-01-01

    The ability to rise from a chair is considered to be important to achieve functional independence and quality of life. This sit-to-stand task is also a good indicator to assess condition of patients with chronic diseases. We developed a wavelet based algorithm for detecting and calculating the durations of sit-to-stand and stand-to-sit transitions from the signal vector magnitude of the measured acceleration signal. The algorithm was tested on waist worn accelerometer data collected from young subjects as well as geriatric patients. The test demonstrates that both transitions can be detected by using wavelet transformation applied to signal magnitude vector. Wavelet analysis produces an estimate of the transition pattern that can be used to calculate the transition duration that further gives clinically significant information on the patients condition. The method can be applied in a real life ambulatory monitoring system for assessing the condition of a patient living at home. PMID:18002349

  8. Evaluation of optical properties for real photonic crystal fiber based on total variation in wavelet domain

    Science.gov (United States)

    Shen, Yan; Wang, Xin; Lou, Shuqin; Lian, Zhenggang; Zhao, Tongtong

    2016-09-01

    An evaluation method based on the total variation model (TV) in wavelet domain is proposed for modeling optical properties of real photonic crystal fibers (PCFs). The TV model in wavelet domain is set up to suppress the noise of the original image effectively and rebuild the cross section images of real PCFs with high accuracy. The optical properties of three PCFs are evaluated, including two kinds of PCFs that supplied from the Crystal Fiber A/S and a homemade side-leakage PCF, by using the combination of the proposed model and finite element method. Numerical results demonstrate that the proposed method can obtain high noise suppression ratio and effectively reduce the noise of cross section images of PCFs, which leads to an accurate evaluation of optical properties of real PCFs. To the best of our knowledge, it is the first time to denoise the cross section images of PCFs with the TV model in the wavelet domain.

  9. A Coiflets-Based Wavelet Laplace Method for Solving the Riccati Differential Equations

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2014-01-01

    Full Text Available A wavelet iterative method based on a numerical integration by using the Coiflets orthogonal wavelets for a nonlinear fractional differential equation is proposed. With the help of Laplace transform, the fractional differential equation was converted into equivalent integral equation of convolution type. By using the wavelet approximate scheme of a function, the undesired jump or wiggle phenomenon near the boundary points was avoided and the expansion constants in the approximation of arbitrary nonlinear term of the unknown function can be explicitly expressed in finite terms of the expansion ones of the approximation of the unknown function. Then a numerical integration method for the convolution is presented. As an example, an iterative method which can solve the singular nonlinear fractional Riccati equations is proposed. Numerical results are performed to show the efficiency of the method proposed.

  10. Improving performance of wavelet-based image denoising algorithm using complex diffusion process

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Sharifzadeh, Sara; Korhonen, Jari

    2012-01-01

    ). In this paper, we present a new algorithm for image noise reduction based on the combination of complex diffusion process and wavelet thresholding. In the existing wavelet thresholding methods, the noise reduction is limited, because the approximate coefficients containing the main information of the image...... are kept unchanged. Since noise affects both the approximate and detail coefficients, the proposed algorithm for noise reduction applies the complex diffusion process on the approximation band in order to alleviate the deficiency of the existing wavelet thresholding methods. The algorithm has been examined......Image enhancement and de-noising is an essential pre-processing step in many image processing algorithms. In any image de-noising algorithm, the main concern is to keep the interesting structures of the image. Such interesting structures often correspond to the discontinuities (edges...

  11. NEW METHOD FOR WEAK FAULT FEATURE EXTRACTION BASED ON SECOND GENERATION WAVELET TRANSFORM AND ITS APPLICATION

    Institute of Scientific and Technical Information of China (English)

    Duan Chendong; He Zhengjia; Jiang Hongkai

    2004-01-01

    A new time-domain analysis method that uses second generation wavelet transform (SGWT) for weak fault feature extraction is proposed. To extract incipient fault feature, a biorthogonal wavelet with the characteristics of impact is constructed by using SGWT. Processing detail signal of SGWT with a sliding window devised on the basis of rotating operation cycle, and extracting modulus maximum from each window, fault features in time-domain are highlighted. To make further analysis on the reason of the fault, wavelet package transform based on SGWT is used to process vibration data again. Calculating the energy of each frequency-band, the energy distribution features of the signal are attained. Then taking account of the fault features and the energy distribution, the reason of the fault is worked out. An early impact-rub fault caused by axis misalignment and rotor imbalance is successfully detected by using this method in an oil refinery.

  12. fMRI time series analysis based on stationary wavelet and spectrum analysis

    Institute of Scientific and Technical Information of China (English)

    ZHI Lianhe; ZHAO Xia; SHAN Baoci; PENG Silong; YAN Qiang; YUAN Xiuli; TANG Xiaowei

    2006-01-01

    The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method.

  13. Wavelet Spectrum for Investigating Statistical Characteristics of UDP-Based Internet Traffic

    Directory of Open Access Journals (Sweden)

    Richard J. Harris

    2012-10-01

    Full Text Available In this paper, we consider statistical characteristics of real User Datagram Protocol (UDP traffic. Fourmain issues in the study include(i the presence of long rangedependence (LRD in the UDP traffic,(ii themarginal distribution of the UDP traces,(iii dependence structure of wavelet coefficients,(iv andperformance evaluation of the Hurst parameter estimation based on different numbers of vanishingmoments of the mother wavelet. By analyzing a large set of real traffic data, it is evident that theUDP Internet traffic reveals the LRD properties with considerably high non-stationaryprocesses.Furthermore, it exhibits non-Gaussian marginal distributions. However, byincreasing the number of vanishing moments,it is impossible to achieve reduction fromLRD tobecome a short range dependence. Thus, it can be shown that there is no significant differencein performance estimation of the Hurst parameter for different numbers of vanishing momentsof the mother wavelet.

  14. Method and application of wavelet shrinkage denoising based on genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Genetic algorithm (GA) based on wavelet transform threshold shrinkage (WTS) and translation-invafiant threshold shrinkage (TIS) is introduced into the method of noise reduction, where parameters used in WTS and TIS, such as wavelet function,decomposition levels, hard or soft threshold and threshold can be selected automatically. This paper ends by comparing two noise reduction methods on the basis of their denoising performances, computation time, etc. The effectiveness of these methods introduced in this paper is validated by the results of analysis of the simulated and real signals.

  15. Multiple description scalable video coding based on 3D lifted wavelet transform

    Institute of Scientific and Technical Information of China (English)

    JIANG Gang-yi; YU Mei; YU Zhou; YE Xi-en; ZHANG Wen-qin; KIM Yong-deak

    2006-01-01

    In this work, a new method to deal with the unconnected pixels in motion compensated temporal filtering (MCTF) is presented, which is designed to improve the performance of 3D lifted wavelet coding. Furthermore, multiple description scalable coding (MDSC) is investigated, and novel MDSC schemes based on 3D wavelet coding are proposed, using the lifting implementation of temporal filtering. The proposed MDSC schemes can avoid the mismatch problem in multiple description video coding, and have high scalability and robustness of video transmission. Experimental results showed that the proposed schemes are feasible and adequately effective.

  16. A Fault Detection Method of Rolling Bearing Based on Wavelet Packet-cepstrum

    Directory of Open Access Journals (Sweden)

    Tingting Leng

    2013-04-01

    Full Text Available In this study, we put forward a fault detection method of rolling bearing based on the wavelet packet-cepstrum. Firstly, the original signal is decomposed using the wavelet packet. Secondly, calculate the energy of the decomposed sub-band reconstruction signal and select the relatively band which is concentrated on the fault energy. Finally, calculate cepstrum of the reconstruction signal to detect fault. The actual normal and fault data of the rolling bearing's outer ring is analyzed in applying this method in the MATLAB simulation circumstance. The result shows that the outer ring's failure frequency measured by the experiment is consistent with the theoretical calculation result.

  17. An Improved Watermarking Algorithm to Colour Image Based on Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Yinglan Fang

    2013-07-01

    Full Text Available This paper has brought forward an improved non-blind watermarking algorithm based on discrete wavelet transform. Watermarking applies special meaningful color image. Before embedded watermark, the algorithm requires needs the watermarking image and carrier image to separate color and transform discrete wavelet. Then the watermark’s low frequency sub-graph and high low sub-graph are respectively embedded into carrier image using additive watermark embedding rules and iterative mixed method. The experiment results have showed that the algorithm has good concealment and improve the robustness of the algorithm.

  18. Correlation methods of base-level cycle based on wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The authors discussed the method of wavelet neural network (WNN) for correlation of base-level cycle. A new vectored method of well log data was proposed. Through the training with the known data set, the WNN can remenber the cycle pattern characteristic of the well log curves. By the trained WNN to identify the cycle pattern in the vectored log data, the ocrrelation process among the well cycles was completed. The application indicates that it is highly efficient and reliable in base-level cycle correlation.

  19. Surface Electromyography Feature Extraction Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Farzaneh Akhavan Mahdavi

    2012-12-01

    Full Text Available Considering the vast variety of EMG signal applications such as rehabilitation of people suffering from some mobility limitations, scientists have done much research on EMG control system. In this regard, feature extraction of EMG signal has been highly valued as a significant technique to extract the desired information of EMG signal and remove unnecessary parts. In this study, Wavelet Transform (WT has been applied as the main technique to extract Surface EMG (SEMG features because WT is consistent with the nature of EMG as a nonstationary signal. Furthermore, two evaluation criteria, namely, RES index (the ratio of a Euclidean distance to a standard deviation and scatter plot are recruited to investigate the efficiency of wavelet feature extraction. The results illustrated an improvement in class separability of hand movements in feature space. Accordingly, it has been shown that only the SEMG features extracted from first and second level of WT decomposition by second order of Daubechies family (db2 yielded the best class separability.

  20. HSV Color Histogram and Directional Binary Wavelet Patterns for Content Based Image Retrieval

    Directory of Open Access Journals (Sweden)

    P.Vijaya Bhaskar Reddy

    2012-08-01

    Full Text Available This paper presents a new image indexing and retrieval algorithm by integrating color (HSV color histogram and texture (directional binary wavelet patterns (DBWP features. For color feature,first the RGB image is converted to HSV image, and then histograms are constructed from HSV spaces. For texture feature, an 8-bit grayscale image is divided into eight binary bit-planes, and then binary wavelet transform (BWT on each bitplane to extract the multi-resolution binary images. The local binary pattern (LBP features are extracted from the resultant BWT sub-bands. Two experiments have beencarried out for proving the worth of our algorithm. It is further mentioned that the database considered for experiments are Corel 1000 database (DB1, and MIT VisTex database (DB2. The results after beinginvestigated show a significant improvement in terms of their evaluation measures as compared to HSV histogram and DBWP.

  1. Design of New Transformer Protection Device Based on Wavelet Energy Entropy-Neural Network Theory and FPGA

    Directory of Open Access Journals (Sweden)

    Na Wu

    2013-11-01

    Full Text Available Transformer differential protection may be of malfunction at the emergence of inrush current, and it will affect the normal operation of the transformer. So the paper puts forward a new application of wavelet energy spectrum entropy-neural network theory in transformer microcomputer protection, in which the multi-resolution analysis of wavelet transform and information entropy technology are combined firstly and forms a new conception named wavelet energy spectrum entropy, and it will be put into neural network theory as the feature vector, forms the new algorithm in the end. This method decomposes signal through wavelet transform, and  extracts the high frequency part of energy in each scale of wavelet transform from inrush current signal and the short circuit current signal, and calculates the wavelet energy entropy value, which will be as the input feature vector of modified BP neural network. And this feature vector is used as training characteristic value for training in BP neural network. According to the measured data of the system, it has achieved good effect. At the same time, for the large amount of calculation and the high requirements of signal sampling rate in wavelet energy entropy - neural network algorithm, a new idea which uses the high-speed hardware platform of FPGA to realize the algorithm application is put forward, and it will break the bottleneck of traditional microcomputer protection that the MCU would not give consideration to both the speed and the accuracy of the protection at the same time.    

  2. Application of Wavelet-Based Tools to Study the Dynamics of Biological Processes

    DEFF Research Database (Denmark)

    Pavlov, A. N.; Makarov, V. A.; Mosekilde, Erik;

    2006-01-01

    The article makes use of three different examples (sensory information processing in the rat trigeminal complex, intracellular interaction in snail neurons and multimodal dynamics in nephron autoregulation) to demonstrate how modern approaches to time-series analysis based on the wavelet-transfor...

  3. Determination of jumps for functions based on Malvar-Coifman-Meyer conjugate wavelets

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    In this paper we discuss determination of jumps for non-periodic function based on Malvar-Cofiman-Meyer (MCM) conjugate wavelets. We prove the equality of Lukacs type. Furthermore we establish several criteria on concentration factors for functions that satisfy weak-smoothness condition of Dini type.

  4. Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method

    Directory of Open Access Journals (Sweden)

    G.Bharatha Sreeja

    2012-04-01

    Full Text Available Mammography is the most effective procedure for the early detection of breast diseases. Mammogram analysis refers the processing of mammograms with the goal of finding abnormality presented in the mammogram. In this paper, the tumour can be detected by using wavelet based adaptive windowing technique. Coarse segmentation is the first step which can be done by using wavelet based histogram thresholding where, the thereshold value is chosen by performing 1-D wavelet based analysis of PDFs of wavelet transformed images at different channels. Fine segmentation can be done by partitioning the image into fixed number of large and small windows. By calculating the mean, maximum and minimum pixel values for the windows a threshold value has been obtained. Depending upon the threshold values the suspicious areas have been segmented. Intensity adjustment is applied as a preprocessing step to improve the quality of an image before applying the proposed technique. The algorithm is validated with mammograms in Mammographic Image Analysis Society Mini Mammographic database which shows that the proposed technique is capable of detecting lesions of very different sizes

  5. Adaptive control of machining process based on extended entropy square error and wavelet neural network

    Institute of Scientific and Technical Information of China (English)

    LAI Xing-yu; YE Bang-yan; LI Wei-guang; YAN Chun-yan

    2007-01-01

    Combining information entropy and wavelet analysis with neural network, an adaptive control system and an adaptive control algorithm are presented for machining process based on extended entropy square error (EESE) and wavelet neural network (WNN). Extended entropy square error function is defined and its availability is proved theoretically. Replacing the mean square error criterion of BP algorithm with the EESE criterion, the proposed system is then applied to the on-line control of the cutting force with variable cutting parameters by searching adaptively wavelet base function and self adjusting scaling parameter, translating parameter of the wavelet and neural network weights. Simulation results show that the designed system is of fast response,non-overshoot and it is more effective than the conventional adaptive control of machining process based on the neural network. The suggested algorithm can adaptively adjust the feed rate on-line till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus improving the machining efficiency and protecting the tool.

  6. A new method based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling.

    Science.gov (United States)

    Avci, Derya; Leblebicioglu, Mehmet Kemal; Poyraz, Mustafa; Dogantekin, Esin

    2014-02-01

    So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58% recognition succes was obtained.

  7. Nonparametric Transient Classification using Adaptive Wavelets

    CERN Document Server

    Varughese, Melvin M; Stephanou, Michael; Bassett, Bruce A

    2015-01-01

    Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind ...

  8. Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-10-01

    Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

  9. Wavelet-based techniques for the gamma-ray sky

    Science.gov (United States)

    McDermott, Samuel D.; Fox, Patrick J.; Cholis, Ilias; Lee, Samuel K.

    2016-07-01

    We demonstrate how the image analysis technique of wavelet decomposition can be applied to the gamma-ray sky to separate emission on different angular scales. New structures on scales that differ from the scales of the conventional astrophysical foreground and background uncertainties can be robustly extracted, allowing a model-independent characterization with no presumption of exact signal morphology. As a test case, we generate mock gamma-ray data to demonstrate our ability to extract extended signals without assuming a fixed spatial template. For some point source luminosity functions, our technique also allows us to differentiate a diffuse signal in gamma-rays from dark matter annihilation and extended gamma-ray point source populations in a data-driven way.

  10. Electrocardiogram Signal Analysis for Physical Motion Based on Wavelet Approach

    Directory of Open Access Journals (Sweden)

    Guang Lu

    2013-03-01

    Full Text Available In this study, a portable, low-cost system, Portable Motion Analyzer (PMA, is introduced to obtain data from daily physical motions, such as ECG, heart rate signals, as well as kinetic information of motion and free-living gait. It can gather, process and analysis the signals from multiple input channels. To process these signals, digital filtering and wavelet analysis is used for quantitative analysis, which can de-noise, de-composite and reconstruct the signals. Similar to the Fast Fourier Transformation (FFT in the Fourier, Mallat algorithm can realize the decomposition and reconstruction of the signal according to the coefficient. Experiments show that the system can effectively de-noise analysis of the data from MIT-BIH arrhythmia database and analysis the signals of body subjected to the shock of ground. It is proved efficient and stable in the most practical scenarios.

  11. Wavelet-Based Techniques for the Gamma-Ray Sky

    CERN Document Server

    McDermott, Samuel D; Cholis, Ilias; Lee, Samuel K

    2015-01-01

    We demonstrate how the image analysis technique of wavelet decomposition can be applied to the gamma-ray sky to separate emission on different angular scales. New structures on scales that differ from the scales of the conventional astrophysical foreground and background uncertainties can be robustly extracted, allowing a model-independent characterization with no presumption of exact signal morphology. As a test case, we generate mock gamma-ray data to demonstrate our ability to extract extended signals without assuming a fixed spatial template. For some point source luminosity functions, our technique also allows us to differentiate a diffuse signal in gamma-rays from dark matter annihilation and extended gamma-ray point source populations in a data-driven way.

  12. Steganography Based on Integer Wavelet Transform and Bicubic Interpolation

    Directory of Open Access Journals (Sweden)

    N. Ajeeshvali

    2012-11-01

    Full Text Available Steganography is the art and science of hiding information in unremarkable cover media so as not to observe any suspicion. It is an application under information security field, being classified under information security, Steganography will be characterized by having set of measures that rely on strengths and counter attacks that are caused by weaknesses and vulnerabilities. The aim of this paper is to propose a modified high capacity image steganography technique that depends on integer wavelet transform with acceptable levels of imperceptibility and distortion in the cover image as a medium file and high levels of security. Bicubic interpolation causes overshoot, which increases acutance (apparent sharpness. The Bicubic algorithm is frequently used for scaling images and video for display. The algorithm preserves fine details of the image better than the common bilinear algorithm.

  13. 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.

  14. [Drug discrimination by near infrared spectroscopy based on summation wavelet extreme learning machine].

    Science.gov (United States)

    Liu, Zhen-Bing; Jiang, Shu-Jie; Yang, Hui-Hua; Zhang, Xue-Bo

    2014-10-01

    As an effective technique to identify counterfeit drugs, Near Infrared Spectroscopy has been successfully used in the drug management of grass-roots units, with classifier modeling of Pattern Recognition. Due to a major disadvantage of the characteristic overlap and complexity, the wide bandwidth and the weak absorption of the Spectroscopy signals, it seems difficult to give a satisfactory solutions for the modeling problem. To address those problems, in the present paper, a summation wavelet extreme learning machine algorithm (SWELM(CS)) combined with Cuckoo research was adopted for drug discrimination by NIRS. Specifically, Extreme Learning Machine (ELM) was selected as the classifier model because of its properties of fast learning and insensitivity, to improve the accuracy and generalization performances of the classifier model; An inverse hyperbolic sine and a Morlet-wavelet are used as dual activation functions to improve convergence speed, and a combination of activation functions makes the network more adequate to deal with dynamic systems; Due to ELM' s weights and hidden layer threshold generated randomly, it leads to network instability, so Cuckoo Search was adapted to optimize model parameters; SWELM(CS) improves stability of the classifier model. Besides, SWELM(CS) is based on the ELM algorithm for fast learning and insensitivity; the dual activation functions and proper choice of activation functions enhances the capability of the network to face low and high frequency signals simultaneously; it has high stability of classification by Cuckoo Research. This compact structure of the dual activation functions constitutes a kernel framework by extracting signal features and signal simultaneously, which can be generalized to other machine learning fields to obtain a good accuracy and generalization performances. Drug samples of near in- frared spectroscopy produced by Xian-Janssen Pharmaceutical Ltd were adopted as the main objects in this paper

  15. Mouse EEG spike detection based on the adapted continuous wavelet transform

    Science.gov (United States)

    Tieng, Quang M.; Kharatishvili, Irina; Chen, Min; Reutens, David C.

    2016-04-01

    Objective. Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary. Approach. A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set. Main Result. The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity. Significance. Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

  16. An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing.

    Science.gov (United States)

    Hajiaghababa, Fatemeh; Kermani, Saeed; Marateb, Hamid R

    2014-10-01

    A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks. PMID:25426428

  17. ECG Signal Recognition based on Wavelet Transform Using Neural and Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    H. M. Abdul-Ridha

    2008-01-01

    Full Text Available This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.

  18. ECG Signal Recognition based on Wavelet Transform Using Neural and Fuzzy Logic

    OpenAIRE

    H. M. Abdul-Ridha; Abduladhem Abdulkareem Ali

    2008-01-01

    This work presents aneural and fuzzy based ECG signal recognition system based on wavelet transform. The suitable coefficients that can be used as a feature for each fuzzy network or neural network is found using a proposed best basis technique. Using the proposed best bases reduces the dimension of the input vector and hence reduces the complexity of the classifier. The fuzzy network and the neural network parameters are learned using back propagation algorithm.

  19. Extracting ECG signal characteristics based on non-linear transformations and wavelets

    Directory of Open Access Journals (Sweden)

    Victoria Eugenia Montes

    2010-07-01

    Full Text Available Different extraction methods were compared regarding the characteristics of normal ECG signals and those emitted in the presence of events related to ischemic cardiopathy based on diagnosis measurements, wavelet transformation and nonlinear analysis of main components. Methods were developed for automatic recognition between normal and ischemic ECG signals. Two effective feature selection techniques were proposed; one used multivariate statistical methods and the second univariate ones. Linear discriminatory evaluation and vector support machines were used for evaluating the proposed feature extraction techniques, comparing error when classifying different states of cardiac functionality. Nonlinear PCA offered slightly better performance compared to wavelet representation but was much better compared to diagnosis measurement. There was up to 0.22% error compared to 6.78% in the case of wavelets and 24.22% in the case of diagnostic measurements. Support vector machines increased the performance for all analysed feature extraction methods; more discriminating characteristics were obtained when using wavelets applied to heartbeat having up to 0.1% classification precision compared to 0.12% in the case of nonlinear analysis of main components and 5.11% in the case of diagnostic measurements.

  20. A FPGA system for QRS complex detection based on Integer Wavelet Transform

    Science.gov (United States)

    Stojanović, R.; Karadaglić, D.; Mirković, M.; Milošević, D.

    2011-01-01

    Due to complexity of their mathematical computation, many QRS detectors are implemented in software and cannot operate in real time. The paper presents a real-time hardware based solution for this task. To filter ECG signal and to extract QRS complex it employs the Integer Wavelet Transform. The system includes several components and is incorporated in a single FPGA chip what makes it suitable for direct embedding in medical instruments or wearable health care devices. It has sufficient accuracy (about 95%), showing remarkable noise immunity and low cost. Additionally, each system component is composed of several identical blocks/cells what makes the design highly generic. The capacity of today existing FPGAs allows even dozens of detectors to be placed in a single chip. After the theoretical introduction of wavelets and the review of their application in QRS detection, it will be shown how some basic wavelets can be optimized for easy hardware implementation. For this purpose the migration to the integer arithmetic and additional simplifications in calculations has to be done. Further, the system architecture will be presented with the demonstrations in both, software simulation and real testing. At the end, the working performances and preliminary results will be outlined and discussed. The same principle can be applied with other signals where the hardware implementation of wavelet transform can be of benefit.

  1. Centrifugal compressor surge detecting method based on wavelet analysis of unsteady pressure fluctuations in typical stages

    Science.gov (United States)

    Izmaylov, R.; Lebedev, A.

    2015-08-01

    Centrifugal compressors are complex energy equipment. Automotive control and protection system should meet the requirements: of operation reliability and durability. In turbocompressors there are at least two dangerous areas: surge and rotating stall. Antisurge protecting systems usually use parametric or feature methods. As a rule industrial system are parametric. The main disadvantages of anti-surge parametric systems are difficulties in mass flow measurements in natural gas pipeline compressor. The principal idea of feature method is based on the experimental fact: as a rule just before the onset of surge rotating or precursor stall established in compressor. In this case the problem consists in detecting of unsteady pressure or velocity fluctuations characteristic signals. Wavelet analysis is the best method for detecting onset of rotating stall in spite of high level of spurious signals (rotating wakes, turbulence, etc.). This method is compatible with state of the art DSP systems of industrial control. Examples of wavelet analysis application for detecting onset of rotating stall in typical stages centrifugal compressor are presented. Experimental investigations include unsteady pressure measurement and sophisticated data acquisition system. Wavelet transforms used biorthogonal wavelets in Mathlab systems.

  2. TRUFAS, a wavelet based algorithm for the rapid detection of planetary transits

    CERN Document Server

    Regulo, C; Alonso, R; Deeg, H J; Cortes, T Roca

    2007-01-01

    Aims: We describe a fast, robust and automatic detection algorithm, TRUFAS, and apply it to data that are being expected from the CoRoT mission. Methods: The procedure proposed for the detection of planetary transits in light curves works in two steps: 1) a continuous wavelet transformation of the detrended light curve with posterior selection of the optimum scale for transit detection, and 2) a period search in that selected wavelet transformation. The detrending of the light curves are based on Fourier filtering or a discrete wavelet transformation. TRUFAS requires the presence of at least 3 transit events in the data. Results: The proposed algorithm is shown to identify reliably and quickly the transits that had been included in a standard set of 999 light curves that simulate CoRoT data. Variations in the pre-processing of the light curves and in the selection of the scale of the wavelet transform have only little effect on TRUFAS' results. Conclusions: TRUFAS is a robust and quick transit detection algor...

  3. Application of Self-Adaptive Wavelet Ridge Demodulation Method Based on LCD to Incipient Fault Diagnosis

    Directory of Open Access Journals (Sweden)

    Songrong Luo

    2015-01-01

    Full Text Available When a local defect occurs in gearbox, the vibration signals present as the form of multicomponent amplitude modulation and frequency modulation (AM-FM. Demodulation analysis is an effective way for this kind of signal. A self-adaptive wavelet ridge demodulation method based on LCD is proposed in this paper. Firstly, multicomponent AM-FM signal is decomposed into series of intrinsic scale components (ISCs and the special intrinsic scale component is selected in order to decrease the lower frequency background noise. Secondly, the genetic algorithm is employed to optimize wavelet parameters according to the inherent characteristics of signal; thirdly, self-adaptive wavelet ridge demodulation wavelet for the selected ISC component is performed to get instantaneous amplitude (IA or instantaneous frequency (IF. Lastly, the characteristics frequency can be obtained to identify the working state or failure information from its spectrum. By two simulation signals, the proposed method was compared with various existing demodulation methods; the simulation results show that it has higher accuracy and higher noise tolerant performance than others. Furthermore, the proposed method was applied to incipient fault diagnosis for gearbox and the results show that it is simple and effective.

  4. 基于小波熵的 BPSK 信号码元速率估计算法%Symbol rate estimation algorithms based on wavelet entropy

    Institute of Scientific and Technical Information of China (English)

    康健; 林云; 杜浩; 李婧

    2015-01-01

    为了解决在低信噪比下准确地估计BPSK信号的码元速率问题,提出了基于小波能谱熵和小波时间熵的两种码元速率估计算法。通过一维多尺度小波变换重构高频部分,分别计算重构信号的小波能谱熵和小波时间熵。对于小波能谱熵,可以直接求取码元速率;对于小波时间熵,需要根据进一步叠加运算后的峰值来求取码元速率。仿真实验表明这两种方法都可以很好地处理低信噪比下BPSK信号的码元速率估计问题,并且比瞬时自相关法具有更强的抗噪性和适应性。%To estimate the symbol rate of BPSK signal in low signal-noise ratio environment, two methods based on wavelet energy entropy ( WEE ) and wavelet time entropy ( WTE ) were proposed in this paper.After reconstructing the high bandwidth of the signal’ s multi-scale wavelet transformation, the WEE and WTE were calculated, respectively.For wavelet energy entropy, the symbol rate can be estimated directly.For wavelet time entropy, the symbol rate can be estimated based on the peaks processed by superposition operation.The simulation results showed good performance of both two methods.Furthermore, these two al-gorithms were less influenced by the noise and have better adaptability than instantaneous au-tocorrelation method.

  5. Using Multi-input-layer Wavelet Neural Network to Model Product Quality of Continuous Casting Furnace and Hot Rolling Mill

    Institute of Scientific and Technical Information of China (English)

    HuanqinLi; JieCheng; BaiwuWan

    2004-01-01

    A new architecture of wavelet neural network with multi-input-layer is proposed and implemented for modeling a class of large-scale industrial processes. Because the processes are very complicated and the number of technological parameters, which determine the final product quality, is quite large, and these parameters do not make actions at the same time but work in different procedures, the conventional feed-forward neural networks cannot model this set of problems efficiently. The network presented in this paper has several input-layers according to the sequence of work procedure in large-scale industrial production processes. The performance of such networks is analyzed and the network is applied to model the steel plate quality of continuous casting furnace and hot rolling mill. Simulation results indicate that the developed methodology is competent and has well prospects to this set of problems.

  6. Wavelet based mobile video watermarking: spread spectrum vs. informed embedding

    Science.gov (United States)

    Mitrea, M.; Prêteux, F.; Duţă, S.; Petrescu, M.

    2005-11-01

    The cell phone expansion provides an additional direction for digital video content distribution: music clips, news, sport events are more and more transmitted toward mobile users. Consequently, from the watermarking point of view, a new challenge should be taken: very low bitrate contents (e.g. as low as 64 kbit/s) are now to be protected. Within this framework, the paper approaches for the first time the mathematical models for two random processes, namely the original video to be protected and a very harmful attack any watermarking method should face the StirMark attack. By applying an advanced statistical investigation (combining the Chi square, Ro, Fisher and Student tests) in the discrete wavelet domain, it is established that the popular Gaussian assumption can be very restrictively used when describing the former process and has nothing to do with the latter. As these results can a priori determine the performances of several watermarking methods, both of spread spectrum and informed embedding types, they should be considered in the design stage.

  7. Ultrasonic Periodontal Probing Based on the Dynamic Wavelet Fingerprint

    Directory of Open Access Journals (Sweden)

    Rose S Timothy

    2005-01-01

    Full Text Available Manual pocket depth probing has been widely used as a retrospective diagnosis method in periodontics. However, numerous studies have questioned its ability to accurately measure the anatomic pocket depth. In this paper, an ultrasonic periodontal probing method is described, which involves using a hollow water-filled probe to focus a narrow beam of ultrasound energy into and out of the periodontal pocket, followed by automatic processing of pulse-echo signals to obtain the periodontal pocket depth. The signal processing algorithm consists of three steps: peak detection/characterization, peak classification, and peak identification. A dynamic wavelet fingerprint (DWFP technique is first applied to detect suspected scatterers in the A-scan signal and generate a two-dimensional black and white pattern to characterize the local transient signal corresponding to each scatterer. These DWFP patterns are then classified by a two-dimensional FFT procedure and mapped to an inclination index curve. The location of the pocket bottom was identified as the third broad peak in the inclination index curve. The algorithm is tested on full-mouth probing data from two sequential visits of 14 patients. Its performance is evaluated by comparing ultrasonic probing results with that of full-mouth manual probing at the same sites, which is taken as the "gold standard."

  8. Scalable wavelet-based active network detection of stepping stones

    Science.gov (United States)

    Gilbert, Joseph I.; Robinson, David J.; Butts, Jonathan W.; Lacey, Timothy H.

    2012-06-01

    Network intrusions leverage vulnerable hosts as stepping stones to penetrate deeper into a network and mask malicious actions from detection. Identifying stepping stones presents a significant challenge because network sessions appear as legitimate traffic. This research focuses on a novel active watermark technique using discrete wavelet transformations to mark and detect interactive network sessions. This technique is scalable, resilient to network noise, and difficult for attackers to discern that it is in use. Previously captured timestamps from the CAIDA 2009 dataset are sent using live stepping stones in the Amazon Elastic Compute Cloud service. The client system sends watermarked and unmarked packets from California to Virginia using stepping stones in Tokyo, Ireland and Oregon. Five trials are conducted in which the system sends simultaneous watermarked samples and unmarked samples to each target. The live experiment results demonstrate approximately 5% False Positive and 5% False Negative detection rates. Additionally, watermark extraction rates of approximately 92% are identified for a single stepping stone. The live experiment results demonstrate the effectiveness of discerning watermark traffic as applied to identifying stepping stones.

  9. Controlling halo-chaos via wavelet-based feedback

    Directory of Open Access Journals (Sweden)

    Jin-Qing Fang

    2002-01-01

    Full Text Available Halo-chaos in high-current accelerator has become one of the key issues because it can cause excessive radioactivity from the accelerators and significantly limits the applications of the new accelerators in industrial and other fields. Some general engineering methods for chaos control have been developed, but they generally are unsuccessful for halo-chaos suppression due to many technical constraints. In this article, controllability condition for beam halo-chaos is analyzed qualitatively. Then Particles-in-Cell (PIC simulations explore the nature of beam halo-chaos formation. A nonlinear control method and wavelet function feedback controller are proposed for controlling beam halo-chaos. After control of beam halo-chaos for initial proton beam with water bag distributions, the beam halo strength factor H is reduced to zero, and other statistical physical quantities of beam halo-chaos are doubly reduced. The results show that the developed methods in this paper are very effective for proton beam halo-chaos suppression. Potential application of the halo-chaos control method is finally pointed out.

  10. Ultrasonic Periodontal Probing Based on the Dynamic Wavelet Fingerprint

    Science.gov (United States)

    Hou, Jidong; Rose, S. Timothy; Hinders, Mark K.

    2005-12-01

    Manual pocket depth probing has been widely used as a retrospective diagnosis method in periodontics. However, numerous studies have questioned its ability to accurately measure the anatomic pocket depth. In this paper, an ultrasonic periodontal probing method is described, which involves using a hollow water-filled probe to focus a narrow beam of ultrasound energy into and out of the periodontal pocket, followed by automatic processing of pulse-echo signals to obtain the periodontal pocket depth. The signal processing algorithm consists of three steps: peak detection/characterization, peak classification, and peak identification. A dynamic wavelet fingerprint (DWFP) technique is first applied to detect suspected scatterers in the A-scan signal and generate a two-dimensional black and white pattern to characterize the local transient signal corresponding to each scatterer. These DWFP patterns are then classified by a two-dimensional FFT procedure and mapped to an inclination index curve. The location of the pocket bottom was identified as the third broad peak in the inclination index curve. The algorithm is tested on full-mouth probing data from two sequential visits of 14 patients. Its performance is evaluated by comparing ultrasonic probing results with that of full-mouth manual probing at the same sites, which is taken as the "gold standard."

  11. Optical phase extraction algorithm based on the continuous wavelet and the Hilbert transforms

    CERN Document Server

    Bahich, Mustapha; Barj, Elmostafa

    2010-01-01

    In this paper we present an algorithm for optical phase evaluation based on the wavelet transform technique. The main advantage of this method is that it requires only one fringe pattern. This algorithm is based on the use of a second {\\pi}/2 phase shifted fringe pattern where it is calculated via the Hilbert transform. To test its validity, the algorithm was used to demodulate a simulated fringe pattern giving the phase distribution with a good accuracy.

  12. Application of adaptive wavelet networks for vibration control of base isolated structures

    OpenAIRE

    Karimi, Hamid Reza; Zapateiro, Mauricio; Luo, Ningsu

    2010-01-01

    This paper presents an application of wavelet networks (WNs) in identification and control design for a class of structures equipped with a type of semiactive actuators, which are called magnetorheological (MR) dampers. The nonlinear model is identified based on a WN framework. Based on the technique of feedback linearization, supervisory control and H∞ control, an adaptive control strategy is developed to compensate for the nonlinearity in the structure so as to enhance the response of the s...

  13. Wavelet-Based Prediction for Governance, Diversi cation and Value Creation Variables

    OpenAIRE

    Kahloul, Ines; Ben Mabrouk, Anouar; Hallara, Salah-Eddine

    2009-01-01

    We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure / diversification relationship. It consists of a dynamic procedure based on wavelets. A comparison with Neural Networks, the most used method, is provided to prove the efficiency of the here-developed one. The empirical ...

  14. Wavelet-Based Prediction for Governance, Diversification and Value Creation Variables

    OpenAIRE

    Ines Kahloul; Anouar Ben Mabrouk; Slah-Eddine Hallara

    2010-01-01

    We study the possibility of completing data bases of a sample of governance, diversification and value creation variables by providing a well adapted method to reconstruct the missing parts in order to obtain a complete sample to be applied for testing the ownership-structure/diversification relationship. It consists of a dynamic procedure based on wavelets. A comparison with Neural Networks, the most used method, is provided to prove the efficiency of the here-developed one. The empirical te...

  15. 基于小波支持向量回归的遥感多光谱图像分辨率增强算法%Resolution Enhance Algorithm for Remote Sensing Multi-spectral Image Based on Wavelet Support Vector Regression

    Institute of Scientific and Technical Information of China (English)

    胡根生; 张为; 梁栋

    2012-01-01

    利用小波支持向量回归,实现了遥感多光谱图像分辨率的增强。首先采用非下采样Contourlet变换对低分辨率的多光谱图像和高分辨率的全色图像进行多分辨率分解,再利用小波支持向量回归对分解系数进行学习和预测,获得分辨率初步提高的多光谱图像,最后再与传统的插值方法得到的结果进行融合来实现多光谱图像分辨率增强。实验结果表明:此方法借遥感全色图像的辅助获得丰富的高频细节信息,使得分辨率增强结果无论是最小均方误差还是峰值信噪比都要优于仅依靠原图像本身放大的传统方法以及其他的分辨率增强方法。%Wavelet support vector regression is utilized to enhance the resolution for remote sensing multi-spectral image. Firstly, both low resolution multi-spectral image and high resolution panchromatic image are decomposed into multi-resolution by using nonsubsampled contourlet transform. Then, by using wavelet support vector regression, the decomposed coefficients are learned and predicted so as to obtain multi-spectral image with preliminary enhanced resolution. Finally, the above results are further fused with the traditional interpolate one to achieve the resolution enhance of multi-spectral image. Experiment results show that the proposed algorithm utilizes the auxiliary o{ remote sensing panchromatic image to effectively attain a wealth of high-frequency detail information, such that either the minimum mean squared error or the peak signal to noise ratio is superior to these from the traditional methods only depending on the amplification of image itself and other resolution enhance methods.

  16. Reusability of Patterns Using Discrete Wavelet Transformation in Watermarking

    Directory of Open Access Journals (Sweden)

    Gurpreet Kaur

    2014-03-01

    Full Text Available Digital image watermarking is hiding information in any form in original image without degrading its perceptual quality. Watermarking is done for copyright protection of the original data. In this paper, a hybrid and robust watermarking technique for copyright protection based on Discrete Cosine Transform and Discrete Wavelet Transform is proposed. Wavelet transform has been applied widely in watermarking research as its excellent multi-resolution analysis property. The watermark is embedded based on the frequency coefficients of the discrete wavelet transform. The robustness of the technique is tested by applying noise attacks on the host signal and here the host signal is the database set containing satellite images.

  17. Application of Holter ECG Signal Analysis Based on Wavelet and Data Mining Technique

    Institute of Scientific and Technical Information of China (English)

    余辉; 谢远国; 周仲兴; 吕扬生

    2004-01-01

    A new model based on dyadic differential wavelet was developed for detecting the R peak in Holter ECG signal according to the design of data mining. The Mallat recursive filter algorithm was introduced to calculate wavelet and optimize the detection algorithm which is based on the equivalent filter technique. The detection algorithm has been verified by MIT arrhythmia database with a high efficiency of 99%. After optimization,the algorithm was put into clinical experiment and tested in the Air Force Hospital in Tianjin for about two months. After about 108 hearts beating test of more than 100 patients, the total efficient detection rate has reached 97%. Now this algorithm module has been applied in business software and shows perfect performance under the complex conditions such as the inversion of heart beating, the falling off of the electrodes, the excursion of base line and so on.

  18. Medical Image Retrieval Based on Multi-Layer Resampling Template

    Institute of Scientific and Technical Information of China (English)

    WANG Xin-rui; YANG Yun-feng

    2014-01-01

    Medical image application in clinical diagnosis and treatment is becoming more and more widely, How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose. This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition, the image retrieval method consists of two retrieval process which is coarse and fine retrieval. Coarse retrieval process is the medical image retrieval process based on the image contour features. Fine retrieval process is the medical image retrieval process based on multi-layer resampling template, a multi-layer sampling operator is employed to extract image resampling images each layer, then these resampling images are retrieved step by step to finish the process from coarse to fine retrieval.

  19. A multichannel time-frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation.

    Science.gov (United States)

    Batista, Arnaldo G; Najdi, Shirin; Godinho, Daniela M; Martins, Catarina; Serrano, Fátima C; Ortigueira, Manuel D; Rato, Raul T

    2016-09-01

    The uterine electromyogram, also called electrohysterogram (EHG), is an electrical signal generated by the uterine contractile activity. The EHG has been considered a promising biomarker for labour and preterm labour prediction, for which there is a demand for accurate estimation methods. Preterm labour is a significant public health concern and one of the major causes of neonatal mortality and morbidity [1]. Given the non-stationary properties of the EHG signal, time-frequency domain analysis can be used. For real life signals it is not generally possible to determine a priori the suitable quadratic time-frequency kernel or the appropriate wavelet family and relative parameters, regarding, for instance, the adequate detection of the signal frequency variation in time. There has been a lack of a comprehensive software tool for the selection of the appropriate time frequency representation of a multichannel EHG signal and extraction of relevant spectral and temporal information. The presented toolbox (Uterine Explorer) has been specifically designed for the EHG analysis and exploration in view of the characterisation of its components. The starting point is the multichannel scalogram or spectrogram representation from which frequency and time marginals, instantaneous frequency and bandwidth are obtained as EHG features. From this point the detected components undergo parametric and non-parametric spectral estimation and wavelet packet analysis. Intrauterine pressure estimation (IUP) is obtained using the Teager, RMS, wavelet marginal and Hilbert operators over the EHG. This toolbox has been tested to build up a dictionary of 288 EHG components [2], useful for research in preterm labour prediction. PMID:27474810

  20. Heart Rate Variability and Wavelet-based Studies on ECG Signals from Smokers and Non-smokers

    Science.gov (United States)

    Pal, K.; Goel, R.; Champaty, B.; Samantray, S.; Tibarewala, D. N.

    2013-12-01

    The current study deals with the heart rate variability (HRV) and wavelet-based ECG signal analysis of smokers and non-smokers. The results of HRV indicated dominance towards the sympathetic nervous system activity in smokers. The heart rate was found to be higher in case of smokers as compared to non-smokers ( p 90 % was achieved. The wavelet decomposition of the ECG signal was done using the Daubechies (db 6) wavelet family. No difference was observed between the smokers and non-smokers which apparently suggested that smoking does not affect the conduction pathway of heart.

  1. Fault Diagnosis and Classification in Urban Rail Vehicle Auxiliary Inverter Based on Wavelet Packet and Elman Neural Network

    Directory of Open Access Journals (Sweden)

    Dechen Yao

    2013-01-01

    Full Text Available In this paper we present a novel method in fault recognition and classification in urban rail vehicle auxiliary inverter based on wavelet packet and Elman neural network. First, the original fault voltage signals are decomposed by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are used as Elman neural network input parameters to realize intelligent fault diagnosis. The result shows that the Elman neural network is better than BP neural network, it is effective to distinguish the state of the urban rail vehicle auxiliary inverter.

  2. Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

    Science.gov (United States)

    Janusek, D.; Kania, M.; Zaczek, R.; Zavala-Fernandez, H.; Zbieć, A.; Opolski, G.; Maniewski, R.

    2011-01-01

    T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant T-wave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

  3. Bearing fault detection using motor current signal analysis based on wavelet packet decomposition and Hilbert envelope

    Directory of Open Access Journals (Sweden)

    Imaouchen Yacine

    2015-01-01

    Full Text Available To detect rolling element bearing defects, many researches have been focused on Motor Current Signal Analysis (MCSA using spectral analysis and wavelet transform. This paper presents a new approach for rolling element bearings diagnosis without slip estimation, based on the wavelet packet decomposition (WPD and the Hilbert transform. Specifically, the Hilbert transform first extracts the envelope of the motor current signal, which contains bearings fault-related frequency information. Subsequently, the envelope signal is adaptively decomposed into a number of frequency bands by the WPD algorithm. Two criteria based on the energy and correlation analyses have been investigated to automate the frequency band selection. Experimental studies have confirmed that the proposed approach is effective in diagnosing rolling element bearing faults for improved induction motor condition monitoring and damage assessment.

  4. Image Retrieval and Classification Method Based on Euclidian Distance Between Normalized Features Including Wavelet Descriptor

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-10-01

    Full Text Available Image retrieval method based on Euclidian distance between normalized features with their mean and variance in feature space is proposed. Effectiveness of the normalization is evaluated together with a validation of the proposed image retrieval method. The proposed method is applied for discrimination and identifying dangerous red tide species based on wavelet utilized classification methods together with texture and color features. Through experiments, it is found that classification performance with the proposed wavelet derived shape information extracted from the microscopic view of the phytoplankton is effective for identifying dangerous red tide species among the other red tide species rather than the other conventional texture, color information. Moreover, it is also found that the proposed normalization of features is effective to improve identification performance.

  5. JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding

    Directory of Open Access Journals (Sweden)

    André Thomas

    2007-01-01

    Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.

  6. Ultrasonic C-scan Detection for Stainless Steel Spot Welding Based on Wavelet Package Analysis

    Institute of Scientific and Technical Information of China (English)

    LIU Jing; XU Guocheng; XU Desheng; ZHOU Guanghao; FAN Qiuyue

    2015-01-01

    An ultrasonic test of spot welding for stainless steel is conducted. Based on wavelet packet decomposition, the ultrasonic echo signal has been analyzed deeply in time - frequency domain, which can easily distinguish the nugget from the corona bond. The 2D C-scan images produced by ultrasonic C scan which contribute to quantitatively calculate the nugget diameter for the computer are further analyzed. The spot welding nugget diameter can be automatically obtained by image enhancement, edge detection and equivalent diameter algorithm procedure. The ultrasonic detection values in this paper show good agreement with the metallographic measured values. The mean value of normal distribution curve is 0.006 67, and the standard deviation is 0.087 11. Ultrasonic C-scan test based on wavelet packet signal analysis is of high accuracy and stability.

  7. Wavelet-based density estimation for noise reduction in plasma simulations using particles

    CERN Document Server

    van yen, Romain Nguyen; Schneider, Kai; Farge, Marie; Chen, Guangye

    2009-01-01

    For given computational resources, the accuracy of plasma simulations using particles is mainly held back by the noise due to limited statistical sampling in the reconstruction of the particle distribution function. A method based on wavelet analysis is proposed and tested to reduce this noise. The method, known as wavelet based density estimation (WBDE), was previously introduced in the statistical literature to estimate probability densities given a finite number of independent measurements. Its novel application to plasma simulations can be viewed as a natural extension of the finite size particles (FSP) approach, with the advantage of estimating more accurately distribution functions that have localized sharp features. The proposed method preserves the moments of the particle distribution function to a good level of accuracy, has no constraints on the dimensionality of the system, does not require an a priori selection of a global smoothing scale, and its able to adapt locally to the smoothness of the den...

  8. Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI.

    Science.gov (United States)

    Li, Qiyue; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Lai, Zongying; Ye, Jing; Chen, Zhong

    2015-06-01

    Compressed sensing MRI (CS-MRI) is a promising technology to accelerate magnetic resonance imaging. Both improving the image quality and reducing the computation time are important for this technology. Recently, a patch-based directional wavelet (PBDW) has been applied in CS-MRI to improve edge reconstruction. However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform. To accelerate computations of PBDW, we propose a general parallelization of patch-based processing by taking the advantage of multicore processors. Additionally, two pertinent optimizations, excluding smooth patches and pre-arranged insertion sort, that make use of sparsity in MR images are also proposed. Simulation results demonstrate that the acceleration factor with the parallel architecture of PBDW approaches the number of central processing unit cores, and that pertinent optimizations are also effective to make further accelerations. The proposed approaches allow compressed sensing MRI reconstruction to be accomplished within several seconds.

  9. Research on electrocardiogram baseline wandering correction based on wavelet transform, QRS barycenter fitting, and regional method.

    Science.gov (United States)

    Song, Jinzhong; Yan, Hong; Li, Yanjun; Mu, Kaiyu

    2010-09-01

    Baseline wandering in electrocardiogram (ECG) is one of the biggest interferences in visualization and computerized detection of waveforms (especially ST-segment) based on threshold decision. A new method based on wavelet transform, QRS barycenter fitting and regional method was proposed in this paper. Firstly, wavelet transform as a coarse correction was used to remove the baseline wandering, whose frequency bands were non-overlapping with that of ST-segment. Secondly, QRS barycenter fitting was applied as a detailed correction. The third, the regional method was used to transfer baseline to zero. Finally, the method in this paper was proved to perform better than filtering and function fitting methods in baseline wandering correction after the long-term ST database (LTST) verification. In addition, the proposed method is simple and easy to carry out, and in current use. PMID:20882381

  10. Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform

    Science.gov (United States)

    Cao, Guangxi; Xu, Wei

    2016-02-01

    This paper investigates the nonlinear structure between carbon and energy markets by employing the maximum overlap wavelet transform (MODWT) as well as the multifractal detrended cross-correlation analysis based on maximum overlap wavelet transform (MFDCCA-MODWT). Based on the MODWT multiresolution analysis and the statistic Qcc(m) significance, relatively significant cross-correlations are obtained between carbon and energy future markets either on different time scales or on the whole. The result of the Granger causality test indicates bidirectional Granger causality between carbon and electricity future markets, although the Granger causality relationship between the carbon and oil price is not evident. The existence of multifractality for the returns between carbon and energy markets is proven with the MFDCCA-MODWT algorithm. In addition, results of investigating the origin of multifractality demonstrate that both long-range correlations and fat-tailed distributions play important roles in the contributions of multifractality.

  11. A wavelet-based method to remove spatial autocorrelation in the analysis of species distributional data

    Directory of Open Access Journals (Sweden)

    G. Carl

    2008-04-01

    Full Text Available Species distributional data based on lattice data often display spatial autocorrelation. In such cases, the assumption of independently and identically distributed errors can be violated in standard regression models. Based on a recently published review on methods to account for spatial autocorrelation, we describe here a new statistical approach which relies on the theory of wavelets. It provides a powerful tool for removing spatial autocorrelation without any prior knowledge of the underlying correlation structure. Our wavelet-revised model (WRM is applied to artificial datasets of species’ distributions, for both presence/absence (binary response and species abundance data (Poisson or normally distributed response. Making use of these published data enables us to compare WRM to other recently tested models and to recommend it as an attractive option for effective and computationally efficient autocorrelation removal.

  12. Robust digital watermarking algorithm based on continuous hyperchaotic system and discrete wavelet transform

    Institute of Scientific and Technical Information of China (English)

    YIN Hong; CHEN Zeng-qiang; YUAN Zhu-zhi

    2006-01-01

    @@ A hyperchaos-based watermarking algorithm is developed in the wavelet domain for images.The algorithm is based on discrete wavelet transform and combines the communication model with side information.We utilize a suitable scale factor to scale host image,then construct cosets for embedding digital watermarking according to scale version of the host image.Our scheme makes a tradeoff between imperceptibility and robustness,and achieves security.The extraction algorithm is a blind detection algorithm which retrieves the watermark without the original host image.In addition,we propose a new method for watermark encryption with hyperchaotic sequence.This method overcomes the drawback of small key space of chaotic sequence and improves the watermark security.Simulation results indicate that the algorithm is a well-balanced watermarking method that offers good robustness and imperceptibility.

  13. Internal fault current identification based on wavelet transform in power transformers

    Energy Technology Data Exchange (ETDEWEB)

    Monsef, H. [ECE Department, Faculty of Engineering, University of Tehran, Center of Excellence on Applied Electromagnetic Systems, Tehran (Iran); Lotfifard, S. [Department of Electrical and Computer Engineering, University of Tehran, Tehran (Iran)

    2007-10-15

    This paper presents a novel approach for differential protection of power transformers. This method uses wavelet transform (WT) and adaptive network-based fuzzy inference system (ANFIS) to discriminate internal faults from inrush currents. The proposed method has been designed based on the differences between amplitudes of wavelet transform coefficients in a specific frequency band generated by faults and inrush currents. The performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. Also the proposed algorithm is tested off-line using data collected from a prototype laboratory three-phase power transformer. The test results show that the new algorithm is very quick and accurate. (author)

  14. JPEG2000-Compatible Scalable Scheme for Wavelet-Based Video Coding

    Directory of Open Access Journals (Sweden)

    Thomas André

    2007-03-01

    Full Text Available We present a simple yet efficient scalable scheme for wavelet-based video coders, able to provide on-demand spatial, temporal, and SNR scalability, and fully compatible with the still-image coding standard JPEG2000. Whereas hybrid video coders must undergo significant changes in order to support scalability, our coder only requires a specific wavelet filter for temporal analysis, as well as an adapted bit allocation procedure based on models of rate-distortion curves. Our study shows that scalably encoded sequences have the same or almost the same quality than nonscalably encoded ones, without a significant increase in complexity. A full compatibility with Motion JPEG2000, which tends to be a serious candidate for the compression of high-definition video sequences, is ensured.

  15. The Brera Multi-scale Wavelet Chandra Survey. I. Serendipitous source catalogue

    CERN Document Server

    Romano, P; Mignani, R P; Moretti, A; Mottini, M; Panzera, M R; Tagliaferri, G

    2008-01-01

    We present the BMW-Chandra source catalogue drawn from essentially all Chandra ACIS-I pointed observations with an exposure time in excess of 10ks public as of March 2003 (136 observations). Using the wavelet detection algorithm developed by Lazzati et al. (1999) and Campana et al. (1999), which can characterise both point-like and extended sources, we identified 21325 sources. Among them, 16758 are serendipitous, i.e. not associated with the targets of the pointings, and do not require a non-automated analysis. This makes our catalogue the largest compilation of Chandra sources to date. The 0.5--10 keV absorption corrected fluxes of these sources range from ~3E-16 to 9E-12 erg cm^-2 s^-1 with a median of 7E-15 erg cm^-2 s^-1. The catalogue consists of count rates and relative errors in three energy bands (total, 0.5-7keV; soft, 0.5-2keV; and hard, 2-7keV), and source positions relative to the highest signal-to-noise detection among the three bands. The wavelet algorithm also provides an estimate of the exten...

  16. 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.

  17. A Chaos Based Blind Digital Image Watermarking in The Wavelet Transform Domain

    OpenAIRE

    Jila Ayubi; Shahram Mohanna; Farahnaz Mohanna; Mehdi Rezaei

    2011-01-01

    An effective watermarking algorithm based on the chaotic maps and the discrete wavelet transform for gray scale images is proposed. In the algorithm, the chaotic maps are employed to generate a key space with the length of 1040 numbers to increase the degree of security. Additionally, the XOR operator has been replaced by the mutation process for embedding the images. This improved mutation operator has been used to encrypt the watermark logo. Additionally, the upgraded mapping method determi...

  18. Wavelet-based analysis of gastric microcirculation in rats with ulcer bleedings

    Science.gov (United States)

    Pavlov, A. N.; Rodionov, M. A.; Pavlova, O. N.; Semyachkina-Glushkovskaya, O. V.; Berdnikova, V. A.; Kuznetsova, Ya. V.; Semyachkin-Glushkovskij, I. A.

    2012-03-01

    Studying of nitric oxide (NO) dependent mechanisms of regulation of microcirculation in a stomach can provide important diagnostic markers of the development of stress-induced ulcer bleedings. In this work we use a multiscale analysis based on the discrete wavelet-transform to characterize a latent stage of illness formation in rats. A higher sensitivity of stomach vessels to the NO-level in ill rats is discussed.

  19. Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

    Institute of Scientific and Technical Information of China (English)

    LIHe-Sheng; MAOJian-Qin; ZHAOMing-Sheng

    2005-01-01

    In this paper, by applying a group of specific orthogonal wavelet packet to Eykhoff algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPT is provided. In comparison to Eykhoff algorithm, the new algorithm has better practicability and wider application range. Simulation results show that the proposed impulse response identification algorithm can be applied to both deterministic and random systems, and is of higher identification precision, stronger anti-noise interference ability and better system dynamic tracking property.

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

    OpenAIRE

    Bo Chen; Zhi-wei Chen; Gan-jun Wang; Wei-ping Xie

    2014-01-01

    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 de...

  1. Time-Frequency Distribution of Music based on Sparse Wavelet Packet Representations

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft

    We introduce a new method for generating time-frequency distributions, which is particularly useful for the analysis of music signals. The method presented here is based on $\\ell1$ sparse representations of music signals in a redundant wavelet packet dictionary. The representations are found usin......, by masking the energy from less structured music instruments. We present four examples for visualizing structured content, including vocal and single instrument....

  2. ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks

    OpenAIRE

    Iman Moazen; Mohamadreza Ahmadzadeh

    2009-01-01

    In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decom...

  3. Extracting ECG signal characteristics based on non-linear transformations and wavelets

    OpenAIRE

    Victoria Eugenia Montes; Gustavo A Guarín; Germán Castellanos Domínguez

    2010-01-01

    Different extraction methods were compared regarding the characteristics of normal ECG signals and those emitted in the presence of events related to ischemic cardiopathy based on diagnosis measurements, wavelet transformation and nonlinear analysis of main components. Methods were developed for automatic recognition between normal and ischemic ECG signals. Two effective feature selection techniques were proposed; one used multivariate statistical methods and the second univariate ones. Linea...

  4. WAVELET-BASED ESTIMATORS OF MEAN REGRESSION FUNCTION WITH LONG MEMORY DATA

    Institute of Scientific and Technical Information of China (English)

    LI Lin-yuan; XIAO Yi-min

    2006-01-01

    This paper provides an asymptotic expansion for the mean integrated squared error (MISE) of nonlinear wavelet-based mean regression function estimators with long memory data. This MISE expansion, when the underlying mean regression function is only piecewise smooth, is the same as analogous expansion for the kernel estimators.However, for the kernel estimators, this MISE expansion generally fails if the additional smoothness assumption is absent.

  5. Copyright protection in digital museum based on digital holography and discrete wavelet transform

    Institute of Scientific and Technical Information of China (English)

    Zhibin Li; Fei Xia; Gang Zheng; Junyong Zhang

    2008-01-01

    @@ A new method to protect the copyright of digital museum based on digital holography is proposed. The Fresnel hologram of watermark image is embedded in the object to be protected through discrete wavelet transform (DWT). After the watermark detection, the copyright information appears in the reconstructed hologram. With the higher redundancy feature in the hologram, the proposed technique can actually survive several kinds of image processing. Experimental results prove that the presented method has good robustness in image protection.

  6. 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 ...

  7. Analysis of damped tissue vibrations in time-frequency space: a wavelet-based approach.

    Science.gov (United States)

    Enders, Hendrik; von Tscharner, Vinzenz; Nigg, Benno M

    2012-11-15

    There is evidence that vibrations of soft tissue compartments are not appropriately described by a single sinusoidal oscillation for certain types of locomotion such as running or sprinting. This paper discusses a new method to quantify damping of superimposed oscillations using a wavelet-based time-frequency approach. This wavelet-based method was applied to experimental data in order to analyze the decay of the overall power of vibration signals over time. Eight healthy subjects performed sprinting trials on a 30 m runway on a hard surface and a soft surface. Soft tissue vibrations were quantified from the tissue overlaying the muscle belly of the medial gastrocnemius muscle. The new methodology determines damping coefficients with an average error of 2.2% based on a wavelet scaling factor of 0.7. This was sufficient to detect differences in soft tissue compartment damping between the hard and soft surface. On average, the hard surface elicited a 7.02 s(-1) lower damping coefficient than the soft surface (pacceleration trace does not follow a sinusoidal function, as is the case with multiple forms of human locomotion. PMID:22995145

  8. Autocorrelation based denoising of manatee vocalizations using the undecimated discrete wavelet transform.

    Science.gov (United States)

    Gur, Berke M; Niezrecki, Christopher

    2007-07-01

    Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations. PMID:17614478

  9. HIRDLS observations of global gravity wave absolute momentum fluxes: A wavelet based approach

    Science.gov (United States)

    John, Sherine Rachel; Kishore Kumar, Karanam

    2016-02-01

    Using wavelet technique for detection of height varying vertical and horizontal wavelengths of gravity waves, the absolute values of gravity wave momentum fluxes are estimated from High Resolution Dynamics Limb Sounder (HIRDLS) temperature measurements. Two years of temperature measurements (2005 December-2007 November) from HIRDLS onboard EOS-Aura satellite over the globe are used for this purpose. The least square fitting method is employed to extract the 0-6 zonal wavenumber planetary wave amplitudes, which are removed from the instantaneous temperature profiles to extract gravity wave fields. The vertical and horizontal wavelengths of the prominent waves are computed using wavelet and cross correlation techniques respectively. The absolute momentum fluxes are then estimated using prominent gravity wave perturbations and their vertical and horizontal wavelengths. The momentum fluxes obtained from HIRDLS are compared with the fluxes obtained from ground based Rayleigh LIDAR observations over a low latitude station, Gadanki (13.5°N, 79.2°E) and are found to be in good agreement. After validation, the absolute gravity wave momentum fluxes over the entire globe are estimated. It is found that the winter hemisphere has the maximum momentum flux magnitudes over the high latitudes with a secondary maximum over the summer hemispheric low-latitudes. The significance of the present study lies in introducing the wavelet technique for estimating the height varying vertical and horizontal wavelengths of gravity waves and validating space based momentum flux estimations using ground based lidar observations.

  10. 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.

  11. Fluorometric Discrimination Technique of Phytoplankton Population Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Shanshan; SU Rongguo; DUAN Yali; ZHANG Cui; SONG Zhijie; WANG Xiulin

    2012-01-01

    The discrete excitation-emission-matrix fluorescence spectra(EEMS)at 12 excitation wavelengths (400,430,450,460,470,490,500,510,525,550,570,and 590 nm)and emission wavelengths ranging from 600-750 nm were determined for 43 phytoplankton species.A two-rank fluorescence spectra database was established by wavelet analysis and a fluorometric discrimination technique for determining phytoplankton population was developed.For laboratory simulatively mixed samples,the samples mixed from 43 algal species(the algae of one division accounted for 25%,50%,75%,85%,and 100% of the gross biomass,respectively),the average discrimination rates at the level of division were 65.0%,87.5%,98.6%,99.0%,and 99.1%,with average relative contents of 18.9%,44.5%,68.9%,73.4%,and 82.9%,respectively;the samples mixed from 32 red tide algal species(the dominant species accounted for 60%,70%,80%,90%,and 100% of the gross biomass,respectively),the average correct discrimination rates of the dominant species at the level of genus were 63.3%,74.2%,78.8%,83.4%,and 79.4%,respectively.For the 81 laboratory mixed samples with the dominant species accounting for 75% of the gross biomass(chlorophyll),the discrimination rates of the dominant species were 95.1% and 72.8% at the level of division and genus,respectively.For the 12 samples collected from the mesocosm experiment in Maidao Bay of Qingdao in August 2007,the dominant species of the 11 samples were recognized at the division level and the dominant species of four of the five samples in which the dominant species accounted for more than 80% of the gross biomass were discriminated at the genus level;for the 12 samples obtained from Jiaozhou Bay in August 2007,the dominant species of all the 12 samples were recognized at the division level.The technique can be directly applied to fluorescence spectrophotometers and to the developing of an in situ algae fluorescence auto-analyzer for

  12. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

    Directory of Open Access Journals (Sweden)

    Jian Wushuai

    2012-12-01

    Full Text Available Abstract Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC curve and free-response operating characteristic (FROC curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

  13. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

    Science.gov (United States)

    2012-01-01

    Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

  14. Extraction of failure characteristic of rolling element bearing based on wavelet transform under strong noise

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hui; WANG Shu-juan

    2005-01-01

    There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and computing the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearings and pick up the fault characteristics effectively.

  15. A Compressive Sensing SAR Imaging Approach Based on Wavelet Package Algorithm

    Directory of Open Access Journals (Sweden)

    Shi Yan

    2013-06-01

    Full Text Available Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data,but it is essential only in the case where the reflection coefficients of SAR scene are sparse. This paper proposed a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR image can be produced with the proposed method even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.

  16. A Chaos Based Blind Digital Image Watermarking in The Wavelet Transform Domain

    Directory of Open Access Journals (Sweden)

    Jila Ayubi

    2011-07-01

    Full Text Available An effective watermarking algorithm based on the chaotic maps and the discrete wavelet transform for gray scale images is proposed. In the algorithm, the chaotic maps are employed to generate a key space with the length of 1040 numbers to increase the degree of security. Additionally, the XOR operator has been replaced by the mutation process for embedding the images. This improved mutation operator has been used to encrypt the watermark logo. Additionally, the upgraded mapping method determines the location of, DWT, Discrete Wavelet Transform, coefficients where the watermark is embedded. To evaluate the robustness and effectiveness of the proposed method, the effect of several attacks has been simulated on the hidden image. Simulation results indicate that the new algorithm can preserve the hidden information against geometric and non geometric attacks.

  17. Wavelet-Based Color Pathological Image Watermark through Dynamically Adjusting the Embedding Intensity

    Science.gov (United States)

    Liu, Guoyan; Liu, Hongjun; Kadir, Abdurahman

    2012-01-01

    This paper proposes a new dynamic and robust blind watermarking scheme for color pathological image based on discrete wavelet transform (DWT). The binary watermark image is preprocessed before embedding; firstly it is scrambled by Arnold cat map and then encrypted by pseudorandom sequence generated by robust chaotic map. The host image is divided into n × n blocks, and the encrypted watermark is embedded into the higher frequency domain of blue component. The mean and variance of the subbands are calculated, to dynamically modify the wavelet coefficient of a block according to the embedded 0 or 1, so as to generate the detection threshold. We research the relationship between embedding intensity and threshold and give the effective range of the threshold to extract the watermark. Experimental results show that the scheme can resist against common distortions, especially getting advantage over JPEG compression, additive noise, brightening, rotation, and cropping. PMID:23243463

  18. Fusion Based Gaussian noise Removal in the Images using Curvelets and Wavelets with Gaussian Filter

    Directory of Open Access Journals (Sweden)

    Naga Sravanthi Kota, G.Umamaheswara Reddy

    2011-10-01

    Full Text Available Denoising images using Curvelet transform approach has been widely used in many fields for itsability to obtain high quality images. Curvelet transform is superior to wavelet in the expression ofimage edge, such as geometry characteristic of curve, which has been already obtained goodresults in image denoising. However artifacts those appear in the result images of Curveletsapproach prevent its application in some fields such as medical image. This paper puts forward afusion based method using both Wavelets and Curvelet transforms because certain regions of theimage have the ringing and radial stripe after Curvelets transform. The experimental resultsindicate that fusion method has an abroad future for eliminating the noise of images. The resultsof the algorithm applied to ultrasonic medical images indicate that the algorithm can be usedefficiently in medical image fields also.

  19. Wavelet-Based Poisson Solver for Use in Particle-in-Cell Simulations

    CERN Document Server

    Terzic, Balsa; Mihalcea, Daniel; Pogorelov, Ilya V

    2005-01-01

    We report on a successful implementation of a wavelet-based Poisson solver for use in 3D particle-in-cell simulations. One new aspect of our algorithm is its ability to treat the general (inhomogeneous) Dirichlet boundary conditions. The solver harnesses advantages afforded by the wavelet formulation, such as sparsity of operators and data sets, existence of effective preconditioners, and the ability simultaneously to remove numerical noise and further compress relevant data sets. Having tested our method as a stand-alone solver on two model problems, we merged it into IMPACT-T to obtain a fully functional serial PIC code. We present and discuss preliminary results of application of the new code to the modelling of the Fermilab/NICADD and AES/JLab photoinjectors.

  20. Parameter identification of fractional order linear system based on Haar wavelet operational matrix.

    Science.gov (United States)

    Li, Yuanlu; Meng, Xiao; Zheng, Bochao; Ding, Yaqing

    2015-11-01

    Fractional order systems can be more adequate for the description of dynamical systems than integer order models, however, how to obtain fractional order models are still actively exploring. In this paper, an identification method for fractional order linear system was proposed. This is a method based on input-output data in time domain. The input and output signals are represented by Haar wavelet, and then fractional order systems described by fractional order differential equations are transformed into fractional order integral equations. Taking use of the Haar wavelet operational matrix of the fractional order integration, the fractional order linear system can easily be converted into a system of algebraic equation. Finally, the parameters of the fractional order system are determined by minimizing the errors between the output of the real system and that of the identified system. Numerical simulations, involving integral and fractional order systems, confirm the efficiency of the above methodology.

  1. A study of interceptor attitude control based on adaptive wavelet neural networks

    Science.gov (United States)

    Li, Da; Wang, Qing-chao

    2005-12-01

    This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.

  2. Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

    International Nuclear Information System (INIS)

    The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done

  3. Wavelet-based fast time-resolved magnetic sensing with electronic spins in diamond

    Science.gov (United States)

    Xu, Nanyang; Jiang, Fengjian; Tian, Yu; Ye, Jianfeng; Shi, Fazhan; Lv, Haijiang; Wang, Ya; Wrachtrup, Jörg; Du, Jiangfeng

    2016-04-01

    Time-resolved magnetic sensing is of great importance from fundamental studies to applications in physical and biological sciences. Recently, the nitrogen-vacancy defect center in diamond has been developed as a promising sensor of magnetic fields under ambient conditions. However, methods to reconstruct time-resolved magnetic fields with high sensitivity are not yet fully developed. Here, we propose and demonstrate a sensing method based on spin echo and Haar wavelet transformation. Our method is exponentially faster in reconstructing time-resolved magnetic fields with comparable sensitivity than existing methods. It is also easier to implement in experiments. Furthermore, the wavelet's unique features enable our method to extract information from the whole signal with only part of the measuring sequences. We then explore this feature for a fast detection of simulated nerve impulses. These results will be useful to time-resolved magnetic sensing with quantum probes at nanoscale.

  4. Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    González Gómez Dulce, I., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Moreno Barbosa, E., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Hernández, Mario Iván Martínez, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Méndez, José Ramos, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. (Mexico); Silvia, Hidalgo Tobón [Hospital Infantil de México, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Hospital Infantil de México, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Fundación para el Desarrollo Carlos Sigüenza. Puebla, Pue (Mexico)

    2014-11-07

    The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.

  5. Wavelet-based adaptive numerical simulation of unsteady 3D flow around a bluff body

    Science.gov (United States)

    de Stefano, Giuliano; Vasilyev, Oleg

    2012-11-01

    The unsteady three-dimensional flow past a two-dimensional bluff body is numerically simulated using a wavelet-based method. The body is modeled by exploiting the Brinkman volume-penalization method, which results in modifying the governing equations with the addition of an appropriate forcing term inside the spatial region occupied by the obstacle. The volume-penalized incompressible Navier-Stokes equations are numerically solved by means of the adaptive wavelet collocation method, where the non-uniform spatial grid is dynamically adapted to the flow evolution. The combined approach is successfully applied to the simulation of vortex shedding flow behind a stationary prism with square cross-section. The computation is conducted at transitional Reynolds numbers, where fundamental unstable three-dimensional vortical structures exist, by well-predicting the unsteady forces arising from fluid-structure interaction.

  6. A NOVEL METHOD FOR NETWORK WORM DETECTION BASED ON WAVELET PACKET ANALYSIS

    Institute of Scientific and Technical Information of China (English)

    Liao Mingtao; Zhang Deyun; Hou Lin

    2006-01-01

    Objective To detect unknown network worm at its early propagation stage. Methods On the basis of characteristics of network worm attack, the concept of failed connection flow (FCT) was defined. Based on wavelet packet analysis of FCT time series, this method computed the energy associated with each wavelet packet of FCT time series, transformed the FCT time series into a series of energy distribution vector on frequency domain, then a trained K-nearest neighbor (KNN) classifier was applied to identify the worm. Results The experiment showed that the method could identify network worm when the worm started to scan. Compared to theoretic value, the identification error ratio was 5.69%. Conclusion The method can detect unknown network worm at its early propagation stage effectively.

  7. Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

    Science.gov (United States)

    González Gómez, Dulce I.; Moreno Barbosa, E.; Martínez Hernández, Mario Iván; Ramos Méndez, José; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito

    2014-11-01

    The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (pADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.

  8. Wavelet-Based Color Pathological Image Watermark through Dynamically Adjusting the Embedding Intensity

    Directory of Open Access Journals (Sweden)

    Guoyan Liu

    2012-01-01

    Full Text Available This paper proposes a new dynamic and robust blind watermarking scheme for color pathological image based on discrete wavelet transform (DWT. The binary watermark image is preprocessed before embedding; firstly it is scrambled by Arnold cat map and then encrypted by pseudorandom sequence generated by robust chaotic map. The host image is divided into n×n blocks, and the encrypted watermark is embedded into the higher frequency domain of blue component. The mean and variance of the subbands are calculated, to dynamically modify the wavelet coefficient of a block according to the embedded 0 or 1, so as to generate the detection threshold. We research the relationship between embedding intensity and threshold and give the effective range of the threshold to extract the watermark. Experimental results show that the scheme can resist against common distortions, especially getting advantage over JPEG compression, additive noise, brightening, rotation, and cropping.

  9. Image Edge Detection Using Hidden Markov Chain Model Based on the Non-decimated Wavelet

    Directory of Open Access Journals (Sweden)

    Renqi Zhang

    2009-03-01

    Full Text Available Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift invariant, in this paper, we develop a new edge detection technique using Hidden Markov Chain (HMC model. With this proposed model (NWHMC, each wavelet coefficient contains a hidden state, herein, we adopt Laplacian model and Gaussian model to represent the information of the state “big” and the state “small”. The model can be trained by EM algorithm, and then we employ Viterbi algorithm to reveal the hidden state of each coefficient according to MAP estimation. The detecting results of several images are provided to evaluate the algorithm. In addition, the algorithm can be applied to noisy images efficiently.

  10. A wavelet-based approach to the discovery of themes and sections in monophonic melodies

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David

    We present the computational method submitted to the MIREX 2014 Discovery of Repeated Themes & Sections task, and the results on the monophonic version of the JKU Patterns Development Database. In the context of pattern discovery in monophonic music, the idea behind our method is that, with a good...... melodic structure in terms of segments, it should be possible to gather similar segments into clusters and rank their salience within the piece. We present an approach to this problem and how we address it. In general terms, we represent melodies either as raw 1D pitch signals or as these signals filtered...... with the continuous wavelet transform (CWT) using the Haar wavelet. We then segment the signal either into constant duration segments or at the resulting coefficients’ modulus local maxima. Segments are concatenated based on their contiguous city-block distance. The concatenated segments are compared using city...

  11. Audio watermarking robust to geometrical distortions based on dyadic wavelet transform

    Science.gov (United States)

    Wang, Yong; Wu, Shaoquan; Huang, Jiwu

    2007-02-01

    Geometrical transforms such as time-scale modification (TSM), random removal(RR), random duplication(RD), and cropping, are of common operations on audio signals while presents many challenges to robust audio watermarking. The existing algorithms aiming at solving the geometrical distortions have various drawbacks e.g. high false alarm probability, heavy computation load, small data hiding capacity, and low robustness performance. In this paper an audio watermarking algorithm based on dyadic wavelet transform robust to geometrical distortions is proposed. Watermark synchronization is achieved using the geometrical invariant properties of dyadic wavelet transform. A well-designed coding scheme is proposed for lowering the bit error rate of the watermark. The experimental results show that the watermark is robust to geometrical transforms and other common operations. Compared with other existing algorithms the proposed algorithm has several advantages of high robustness, large data hiding capacity and low computation load.

  12. A COMPRESSION ALGORITHM FOR ECG BASED ON INTEGER LIFTING SCHEME WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In view of the shortcomes of conventional ElectroCardioGram (ECG) compression algorithms, such as high complexity of operation and distortion of reconstructed signal, a new ECG compression encoding algorithm based on Set Partitioning In Hierarchical Trees (SPIHT) is brought out after studying the integer lifting scheme wavelet transform in detail. The proposed algorithm modifies zero-tree structure of SPIHT, establishes single dimensional wavelet coefficient tree of ECG signals and enhances the efficiency of SPIHT-encoding by distributing bits rationally, improving zero-tree set and ameliorating classifying method. For this improved algorithm, floating-point computation and storage are left out of consideration and it is easy to be implemented by hardware and software. Experimental results prove that the new algorithm has admirable features of low complexity,high speed and good performance in signal reconstruction. High compression ratio is obtained with high signal fidelity as well.

  13. Robust Wavelet-Based Facial Image Watermarking Against Geometric Attacks Using Coordinate System Recovery

    Institute of Scientific and Technical Information of China (English)

    ZHAO Pei-dong; XIE Jian-ying

    2008-01-01

    A coordinate system of the original image is established using a facial feature point localization technique. After the original image transformed into a new image with the standard coordinate system, a redundant watermark is adaptively embedded in the discrete wavelet transform(DWT) domain based on the statistical characteristics of the wavelet coefficient block. The coordinate system of watermarked image is reestablished as a calibration system. Regardless of the host image rotated, scaled, or translated(RST), all the geometric attacks are eliminated while the watermarked image is transformed into the standard coordinate system. The proposed watermark detection is a blind detection. Experimental results demonstrate the proposed scheme is robust against common and geometric image processing attacks, particularly its robustness against joint geometric attacks.

  14. A Novel VLSI Architecture for Real-Time Line-Based Wavelet Transform Using Lifting Scheme

    Institute of Scientific and Technical Information of China (English)

    Kai Liu; Ke-Yan Wang; Yun-Song Li; Cheng-Ke Wu

    2007-01-01

    In this paper, we propose a VLSI architecture that performs the line-based discrete wavelet transform (DWT) using a lifting scheme. The architecture consists of row processors, column processors, an intermediate buffer and a control module. Row processor and Column processor work as the horizontal and vertical filters respectively.Intermediate buffer is composed of five FIFOs to store temporary results of horizontal filter. Control module schedules the output order to external memory. Compared with existing ones, the presented architecture parallelizes all levels of wavelet transform to compute multilevel DWT within one image transmission time, and uses no external but one intermediate buffer to store several line results of horizontal filtering, which decreases resource required significantly and reduces memory efficiently. This architecture is suitable for various real-time image/video applications.

  15. Enhancement of Non-Air Conducted Speech Based on Wavelet-Packet Adaptive Threshold

    Directory of Open Access Journals (Sweden)

    Xijing Jing

    2013-01-01

    Full Text Available This study developed a new kind of speech detecting method by using millimeter wave. Because of the advantage of the millimeter wave, this speech detecting method has great potential application and may provide some exciting possibility for wide applications. However, the MMW conduct speech is in less intelligible and poor audibility since it is corrupted by additive combined noise. This paper, therefore, also developed an algorithm of wavelet packet threshold by using hard threshold and soft threshold for removing noise based on the good capability of wavelet packet for analyzing time-frequency signal. Comparing to traditional speech enhancement algorithm, the results from both simulation and listening evaluation suggest that the proposed algorithm takes on a better performance on noise removing while the distortion of MMW radar speech remains acceptable, the enhanced speech also sounds more pleasant to human listeners, resulting in improved results over classical speech enhancement algorithms.

  16. Distributed edge detection algorithm based on wavelet transform for wireless video sensor network

    Science.gov (United States)

    Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng

    2011-05-01

    Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.

  17. Wavelet network based predistortion method for wideband RF power amplifiers exhibiting memory effects

    Institute of Scientific and Technical Information of China (English)

    JIN Zhe; SONG Zhi-huan; HE Jia-ming

    2007-01-01

    RF power amplifiers (PAs) are usually considered as memoryless devices in most existing predistortion techniques.Nevertheless, in wideband communication systems, PA memory effects can no longer be ignored and memoryless predistortion cannot linearize PAs effectively. After analyzing PA memory effects, a novel predistortion method based on wavelet networks (WNs) is proposed to linearize wideband RF power amplifiers. A complex wavelet network with tapped delay lines is applied to construct the predistorter and then a complex backpropagation algorithm is developed to train the predistorter parameters. The simulation results show that compared with the previously published feed-forward neural network predistortion method, the proposed method provides faster convergence rate and better performance in reducing out-of-band spectral regrowth.

  18. Feature extraction of induction motor stator fault based on particle swarm optimization and wavelet packet

    Institute of Scientific and Technical Information of China (English)

    WANG Pan-pan; SHI Li-ping; HU Yong-jun; MIAO Chang-xin

    2012-01-01

    To effectively extract the interturn short circuit fault features of induction motor from stator current signal,a novel feature extraction method based on the bare-bones particle swarm optimization (BBPSO) algorithm and wavelet packet was proposed.First,according to the maximum inner product between the current signal and the cosine basis functions,this method could precisely estimate the waveform parameters of the fundamental component using the powerful global search capability of the BBPSO,which can eliminate the fundamental component and not affect other harmonic components.Then,the harmonic components of residual current signal were decomposed to a series of frequency bands by wavelet packet to extract the interturn circuit fault features of the induction motor.Finally,the results of simulation and laboratory tests demonstrated the effectiveness of the proposed method.

  19. An OFDM System Based on Dual Tree Complex Wavelet Transform (DT-CWT

    Directory of Open Access Journals (Sweden)

    Mohamed Hussien Nerma

    2009-05-01

    Full Text Available As demand for higher data rates rises, need to develop more efficient wireless communication systems also rises. The work described in this paper is an effort in this direction. We have proposed a novel orthogonal frequency division multiplexing (OFDM system based on dual – tree complex wavelet transform (DT-CWT. In the proposed scheme, DT-CWT is used in the place of fast Fourier transform (FFT. The proposed scheme achieves excellent improvements in bit error rate (BER over conventional OFDM and wavelet packet modulation (WPM systems. Moreover, the proposed scheme offers the better peak – to – average power ratio (PAPR performance compared to conventional OFDM and WPM systems at the expense of acceptable computational complexity. The complementary cumulative distribution function (CCDF of PAPR for the proposed scheme achieves about 3 dB improvement in PAPR over the traditional OFDM and WPM signals at 0.1% of CCDF.

  20. 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.

  1. WaVPeak: Picking NMR peaks through wavelet-based smoothing and volume-based filtering

    KAUST Repository

    Liu, Zhi

    2012-02-10

    Motivation: Nuclear magnetic resonance (NMR) has been widely used as a powerful tool to determine the 3D structures of proteins in vivo. However, the post-spectra processing stage of NMR structure determination usually involves a tremendous amount of time and expert knowledge, which includes peak picking, chemical shift assignment and structure calculation steps. Detecting accurate peaks from the NMR spectra is a prerequisite for all following steps, and thus remains a key problem in automatic NMR structure determination. Results: We introduce WaVPeak, a fully automatic peak detection method. WaVPeak first smoothes the given NMR spectrum by wavelets. The peaks are then identified as the local maxima. The false positive peaks are filtered out efficiently by considering the volume of the peaks. WaVPeak has two major advantages over the state-of-the-art peak-picking methods. First, through wavelet-based smoothing, WaVPeak does not eliminate any data point in the spectra. Therefore, WaVPeak is able to detect weak peaks that are embedded in the noise level. NMR spectroscopists need the most help isolating these weak peaks. Second, WaVPeak estimates the volume of the peaks to filter the false positives. This is more reliable than intensity-based filters that are widely used in existing methods. We evaluate the performance of WaVPeak on the benchmark set proposed by PICKY (Alipanahi et al., 2009), one of the most accurate methods in the literature. The dataset comprises 32 2D and 3D spectra from eight different proteins. Experimental results demonstrate that WaVPeak achieves an average of 96%, 91%, 88%, 76% and 85% recall on 15N-HSQC, HNCO, HNCA, HNCACB and CBCA(CO)NH, respectively. When the same number of peaks are considered, WaVPeak significantly outperforms PICKY. The Author(s) 2012. Published by Oxford University Press.

  2. ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-Complex Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed Zakaria

    2010-07-01

    Full Text Available In this paper, an Electrocardiogram (ECG signal is compressed based on discrete wavelet transform (DWT and QRS-complex estimation. The ECG signal is preprocessed by normalization and mean removal. Then, an error signal is formed as the difference between the preprocessed ECG signal and the estimated QRS-complex waveform. This error signal is wavelet transformed and the resulting wavelet coefficients are thresholded by setting to zero all coefficients that are smaller than certain threshold levels. The threshold levels of all subbands are calculated based on Energy Packing Efficiency (EPE such that minimum percentage root mean square difference (PRD and maximum compression ratio (CR are obtained. The resulted thresholded DWT coefficients are coded using the coding technique given in [1], [20]. The compression algorithm was implemented and tested upon records selected from the MIT - BIH arrhythmia database [2]. Simulation results show that the proposed algorithm leads to high CR associated with low distortion level relative to previously reported compression algorithms [1], [14] and [18]. For example, the compression of record 100 using the proposed algorithm yields to CR = 25.15 associated with PRD = 0.7% and PSNR = 45 dB. This achieves compression rate of nearly 128 bit/sec. The main features of this compression algorithm are the high efficiency, high speed and simplicity in design.

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

    Science.gov (United States)

    Chouakri, S. A.; Djaafri, O.; Taleb-Ahmed, A.

    2013-08-01

    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.

  4. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions

    Directory of Open Access Journals (Sweden)

    K. Daqrouq

    2016-01-01

    Full Text Available An investigation of the electrocardiogram (ECG signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF obtained from the percentage energy (PE of terminal wavelet packet transform (WPT subsignals. In addition, the average framing percentage energy (AFE technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD, logarithmic difference signal ratio (LDSR, and correlation coefficient (CC. The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN environment, where the recognition rate was 81.48% for 5 dB.

  5. Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions.

    Science.gov (United States)

    Daqrouq, K; Dobaie, A

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB. PMID:26949412

  6. Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Jing Xu

    2016-07-01

    Full Text Available As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

  7. 3D Inversion of Magnetic Data through Wavelet based Regularization Method

    Directory of Open Access Journals (Sweden)

    Maysam Abedi

    2015-06-01

    Full Text Available This study deals with the 3D recovering of magnetic susceptibility model by incorporating the sparsity-based constraints in the inversion algorithm. For this purpose, the area under prospect was divided into a large number of rectangular prisms in a mesh with unknown susceptibilities. Tikhonov cost functions with two sparsity functions were used to recover the smooth parts as well as the sharp boundaries of model parameters. A pre-selected basis namely wavelet can recover the region of smooth behaviour of susceptibility distribution while Haar or finite-difference (FD domains yield a solution with rough boundaries. Therefore, a regularizer function which can benefit from the advantages of both wavelets and Haar/FD operators in representation of the 3D magnetic susceptibility distributionwas chosen as a candidate for modeling magnetic anomalies. The optimum wavelet and parameter β which controls the weight of the two sparsifying operators were also considered. The algorithm assumed that there was no remanent magnetization and observed that magnetometry data represent only induced magnetization effect. The proposed approach is applied to a noise-corrupted synthetic data in order to demonstrate its suitability for 3D inversion of magnetic data. On obtaining satisfactory results, a case study pertaining to the ground based measurement of magnetic anomaly over a porphyry-Cu deposit located in Kerman providence of Iran. Now Chun deposit was presented to be 3D inverted. The low susceptibility in the constructed model coincides with the known location of copper ore mineralization.

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

    Science.gov (United States)

    Yang, Wei; Yang, Yuanhong; Yang, Mingwei

    2014-06-01

    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.

  9. A wavelet space based approach for Doppler ultrasound blood signals separation

    Institute of Scientific and Technical Information of China (English)

    JIN Dawei; WANG Yuanyuan; WANG Weiqi

    2007-01-01

    In medical Doppler ultrasound systems, a high-pass filter which is usually employed to filter wall clutter components, will remove the information of the low velocity blood flow.To extract intact Doppler ultrasound blood signals, a novel approach is proposed based on the spatially selective noise filtration. The wall signals are firstly estimated by the spatially selective noise filtration from wavelet spatial correlation property. Then the wall clutters are exactly obtained by a wavelet threshold de-noising technique which eliminates the residual blood flow signals. Finally the intact blood flow signals are achieved by subtracting the wall signals from the mixed signals. This approach is applied to both computer simulated and in vivo carotid Doppler ultrasound signals. The experiment results show that the wavelet space based approach can exactly extract the blood flow signals, and achieve about 45% lower results in the mean absolute error than that of the high-pass filtering. This approach is expected to be an effective method to remove the wall clutters in Doppler ultrasound systems.

  10. A data-distributed parallel algorithm for wavelet-based fusion of remote sensing images

    Institute of Scientific and Technical Information of China (English)

    YANG Xuejun; WANG Panfeng; DU Yunfei; ZHOU Haifang

    2007-01-01

    With the increasing importance of multiplatform remote sensing missions,the fast integration or fusion of digital images from disparate sources has become critical to the success of these endeavors.In this paper,to speed up the fusion process,a Data-distributed Parallel Algorithm for wavelet-based Fusion (DPAF for short) of remote sensing images which are not geo-registered remote sensing images is presented for the first time.To overcome the limitations on memory space as well as the computing capability of a single processor,data distribution,data-parallel processing and load balancing techniques are integrated into DPAF.To avoid the inherent communication overhead of a wavelet-based fusion method,a special design called redundant partitioning is used,which is inspired by the characteristics of wavelet transform.Finally,DPAF is evaluated in theory and tested on a 32-CPU cluster of workstations.The experimental results show that our algorithm has good parallel performance and scalability.

  11. A Wavelet-based Fast Discrimination of Transformer Magnetizing Inrush Current

    Science.gov (United States)

    Kitayama, Masashi

    Recently customers who need electricity of higher quality have been installing co-generation facilities. They can avoid voltage sags and other distribution system related disturbances by supplying electricity to important load from their generators. For another example, FRIENDS, highly reliable distribution system using semiconductor switches or storage devices based on power electronics technology, is proposed. These examples illustrates that the request for high reliability in distribution system is increasing. In order to realize these systems, fast relaying algorithms are indispensable. The author proposes a new method of detecting magnetizing inrush current using discrete wavelet transform (DWT). DWT provides the function of detecting discontinuity of current waveform. Inrush current occurs when transformer core becomes saturated. The proposed method detects spikes of DWT components derived from the discontinuity of the current waveform at both the beginning and the end of inrush current. Wavelet thresholding, one of the wavelet-based statistical modeling, was applied to detect the DWT component spikes. The proposed method is verified using experimental data using single-phase transformer and the proposed method is proved to be effective.

  12. Comparative Study on Discrimination Methods for Identifying Dangerous Red Tide Species Based on Wavelet Utilized Classification Methods

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-02-01

    Full Text Available Comparative study on discrimination methods for identifying dangerous red tide species based on wavelet utilized classification methods is conducted. Through experiments, it is found that classification performance with the proposed wavelet derived shape information extracted from the microscopic view of the phytoplankton is effective for identifying dangerous red tide species among the other red tide species rather than the other conventional texture, color information.

  13. A wavelets-based analysis of the phillips curve hypothesis for the Brazilian economy, 1980-2011

    OpenAIRE

    Edgard Almeida Pimentel

    2013-01-01

    This paper implements a wavelets-based analysis of the Phillips curve hypothesis — as formulated by Friedman and Phelps — for the Brazilian economy, concerning the last thirty years. We provide an introductory discussion on Phillips curve's main arguments and an exploratory data analysis for the variables under consideration: prices, unemployment and real wages. In the sequel, we estimate variances and correlation structures between these aggregates through wavelets. Our findings reject the P...

  14. Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness

    Science.gov (United States)

    Kim, Won Hwa; Singh, Vikas; Chung, Moo K.; Hinrichs, Chris; Pachauri, Deepti; Okonkwo, Ozioma C.; Johnson, Sterling C.

    2014-01-01

    Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer’s disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer’s Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer’s Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation. PMID:24614060

  15. Analysis of hydrological trend for radioactivity content in bore-hole water samples using wavelet based denoising

    International Nuclear Information System (INIS)

    A wavelet transform based denoising methodology has been applied to detect the presence of any discernable trend in 137Cs and 90Sr activity levels in bore-hole water samples collected four times a year over a period of eight years, from 2002 to 2009, in the vicinity of typical nuclear facilities inside the restricted access zones. The conventional non-parametric methods viz., Mann–Kendall and Spearman rho, along with linear regression when applied for detecting the linear trend in the time series data do not yield results conclusive for trend detection with a confidence of 95% for most of the samples. The stationary wavelet based hard thresholding data pruning method with Haar as the analyzing wavelet was applied to remove the noise present in the same data. Results indicate that confidence interval of the established trend has significantly improved after pre-processing to more than 98% compared to the conventional non-parametric methods when applied to direct measurements. -- Highlights: ► Environmental trend analysis with wavelet pre-processing was carried out. ► Removal of local fluctuations to obtain the trend in a time series with various mother wavelets. ► Theoretical validation of the methodology with model outputs. ► Efficient detection of trend for 137Cs, 90Sr in bore-hole water samples improves the associated confidence interval to more than 98%. ► Wavelet based pre-processing reduces the indecisive nature of the detected trend

  16. The application of modeling and prediction with MRA wavelet network

    Institute of Scientific and Technical Information of China (English)

    LU Shu-ping; YANG Xue-jing; ZHAO Xi-ren

    2004-01-01

    As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was carried out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model.The research indicates that it is feasible to use the MRA wavelet network in the short -time prediction of ship motion.

  17. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Science.gov (United States)

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  18. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    International Nuclear Information System (INIS)

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  19. A New OFDM System Based on Lifting Wavelet Transform for Wireless Channel

    Institute of Scientific and Technical Information of China (English)

    GAO Jian-bo; ZHAO Er-yuan

    2005-01-01

    It is well known that the Orthogonal Frequency Division Multiplexing(OFDM) system has a high ability to overcome the effect of multipath and can obtain high spectral efficiency in the wireless communication channel. However, avoid Interchannel Interference (ICI) and Intersymbol Interference (ISI) in wireless channel, a guard interval longer than channel delay is used in conventional OFDM system, which cause the efficiency of bandwidth usage reduced. Due to the superior spectral containment of wavelets, this paper proposed a new OFDM system based on Lifting Wavelet Transform (LWT-OFDM), which adopts lifting wavelet transform to replace the conventional Fourier transform. This new OFDM system doesn't need the Cyclic Prefix (CP) so its structure is more simply than FFT-OFDM and its algorithm is as simply as FFT-OFDM. The new LWT-OFDM system can mitigates some disadvantages of FFT-OFDM system, such as a relatively large peak-to-average power ratio, more sensitive to carrier frequency offset and phase noise. Simulations show that the LWT-OFDM system is more effective and attractive than conversional FFT-OFDM in wireless channel.

  20. A new approach to pre-processing digital image for wavelet-based watermark

    Science.gov (United States)

    Agreste, Santa; Andaloro, Guido

    2008-11-01

    The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.