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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. Wavelet-Based Adaptive Solvers on Multi-core Architectures for the Simulation of Complex Systems

    Science.gov (United States)

    Rossinelli, Diego; Bergdorf, Michael; Hejazialhosseini, Babak; Koumoutsakos, Petros

    We build wavelet-based adaptive numerical methods for the simulation of advection dominated flows that develop multiple spatial scales, with an emphasis on fluid mechanics problems. Wavelet based adaptivity is inherently sequential and in this work we demonstrate that these numerical methods can be implemented in software that is capable of harnessing the capabilities of multi-core architectures while maintaining their computational efficiency. Recent designs in frameworks for multi-core software development allow us to rethink parallelism as task-based, where parallel tasks are specified and automatically mapped into physical threads. This way of exposing parallelism enables the parallelization of algorithms that were considered inherently sequential, such as wavelet-based adaptive simulations. In this paper we present a framework that combines wavelet-based adaptivity with the task-based parallelism. We demonstrate good scaling performance obtained by simulating diverse physical systems on different multi-core and SMP architectures using up to 16 cores.

  3. Multi-image gradient-based algorithms for motion measurement using wavelet transform

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    A multi-image wavelet transform motion estimation algorithm based on gradient methods is presented by using the characteristic of wavelet transfom.In this algorithm,the accuracy can be improved greatly using data in many images to measure motions between two images.In combination with the reliability measure for constraints function,the reliable data constraints of the images were decomposed with multi-level simultaneous wavelet transform rather than the traditional coarse-to-fine approach.Compared with conventional methods,this motion measurement algorithm based on multi-level simultaneous wavelet transform avoids propagating errors between the decomposed levels.Experimental simulations show that the implementation of this algo rithm is simple,and the measurement accuracy is improved.

  4. DESIGN OF WAVELET PACKET BASED MODEL FOR MULTI CARRIER MODULATION

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    MIHIR NARAYAN MOHANTY

    2012-04-01

    Full Text Available In current scenario Multi-Carrier modulation (MCM is considered an effective technique for both wire and wireless communications. It divides the entire bandwidth into several parallel sub-channels. This splitting is by dividing the transmit data into several parallel low-bit-rate data streams and then to modulate the carrierscorresponding to those sub-channels. Though MCM technique uses Orthogonal Frequency Division Multiplexing (OFDM model, it is very sensitive to Carrier Frequency Offset (CFO, that leads to a severedistortion in subcarrier orthogonality and causes inter channel interference (ICI. In this paper, Wavelet Packet Transform is designed for the model of MCM as a novel alternative to the most exiting Orthogonal Frequency Division Multiplexing (OFDM technique, because of its time frequency representation and lower side lobes intransmitted signals, that reduces inter carrier interference (ICI, and inter symbol interference (ISI. Performance analysis is investigated for such model. Simulation results show a significant enhancement in terms of spectral efficiency.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

    Science.gov (United States)

    Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

    2016-12-01

    This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

  10. Investigation of PAPR in Discrete Wavelet Transform based Multi-carrier Systems

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    Neha S

    2015-10-01

    Full Text Available The objective of the paper is to formulate a measure to reduce PAPR problem in Orthogonal Frequency Division Multiplexing. To mitigate the problem of PAPR, a Discrete Wavelet Transform based system is employed instead of conventional OFDM. For the comparative study, the PAPR in conventional OFDM is analyzed for varying number of subcarriers and for different channel taps. The result of conventional OFDM is compared with wavelet based OFDM, employing wavelets namely - ‘Haar’, ‘Daubechies’, ‘Symlets’ and ‘Biorthogonal’ wavelets. Further the PAPR is analyzed for varying levels and different length of channel impulse response. The simulation results show that wavelet based OFDM has less PAPR than conventional OFDM. With the increase in the number level, the PAPR at the demodulator side decreases in the wavelet based OFDM.

  11. Investigation of PAPR in Discrete Wavelet Transform based Multi-carrier Systems

    OpenAIRE

    Neha S; Thushara S; Ramanathan R

    2015-01-01

    The objective of the paper is to formulate a measure to reduce PAPR problem in Orthogonal Frequency Division Multiplexing. To mitigate the problem of PAPR, a Discrete Wavelet Transform based system is employed instead of conventional OFDM. For the comparative study, the PAPR in conventional OFDM is analyzed for varying number of subcarriers and for different channel taps. The result of conventional OFDM is compared with wavelet based OFDM, employing wavelets namely - ‘Haar’, ‘Daubechies’, ‘Sy...

  12. 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.

  13. Improved Multidimensional Color Image Fusion Based on the Multi-Wavelets

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    T.S. Anand

    2013-06-01

    Full Text Available Image fusion refers to the process of combining the visual information present in two or more images into a single high information content image. This study proposes the concept of fusing the Multi-dimensional images using the YCbCr color model based on the Multi-Wavelet Transform (MWT. Initially the source images namely the visible, Infra Red (IR and Ultra Violet (UV images are transformed from RGB color model to YCbCr color space and then MWT is applied to the Y, Cb and Cr components of the respective images. Finally the transform coefficients obtained are fused using the different fusion techniques. The performance of the color image fusion process is analyzed using the performance measures-Entropy (H, Peak Signal to Noise Ratio (PSNR, Root Mean Square Error (RMSE and Correlation Coefficient (CC.

  14. Wavelet Based Image Denoising Technique

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    Sachin D Ruikar

    2011-03-01

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

  15. A Multi-Watermarking Method Based on Wavelets Combined with the EZW Coder

    OpenAIRE

    Chouchane, Sabrina; Puech, William

    2005-01-01

    International audience; In this paper, we present a new semi-blind watermarking method with secret key by embedding several watermarks in the same image. This embedding is done when the originale image is being coded by the Embedded Zerotree Wavelet (EZW) coder, a compression method based on the wavelets transform, proposed by Shapiro in 1993. Therefore, the detection processus is performed at the time of decoding the compressed watermarked image. Our algorithm, tested on several greyscale im...

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

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    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.

  17. 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

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

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

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    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.

  20. 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 ...

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. The Identification of Internal and External Faults for±800kV UHVDC Transmission Line Using Wavelet based Multi-Resolution Analysis

    Directory of Open Access Journals (Sweden)

    Shu Hongchun

    2011-05-01

    Full Text Available There is a smoothing reactor and DC filter between the inverter and the direct current line to form a boundary in the HVDC transmission system. Since this boundary presents the stop-band characteristic to the high frequency transient voltage signals, the high-frequency transient voltage signal caused by external faults through boundary will be attenuated and the signals caused by internal faults will be unchanged. The wavelet analysis can be used as a tool to extract the feature of the fault to classify the internal fault and the external fault in HVDC transmission system. This paper explores the new method of wavelet based Multi-Resolution Analysis for signal decomposition to classify the difference types fault.

  7. A Sequential, Implicit, Wavelet-Based Solver for Multi-Scale Time-Dependent Partial Differential Equations

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    Donald A. McLaren

    2013-04-01

    Full Text Available This paper describes and tests a wavelet-based implicit numerical method for solving partial differential equations. Intended for problems with localized small-scale interactions, the method exploits the form of the wavelet decomposition to divide the implicit system created by the time-discretization into multiple smaller systems that can be solved sequentially. Included is a test on a basic non-linear problem, with both the results of the test, and the time required to calculate them, compared with control results based on a single system with fine resolution. The method is then tested on a non-trivial problem, its computational time and accuracy checked against control results. In both tests, it was found that the method requires less computational expense than the control. Furthermore, the method showed convergence towards the fine resolution control results.

  8. 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.

  9. 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.

  10. Multi-Classification of IKONOS Images Based on Wavelet Kernel MSVR%基于小波核MSVR的IKONOS影像多分类

    Institute of Scientific and Technical Information of China (English)

    尹凡; 胡根生; 王珏

    2014-01-01

    According to spectral and band characteristics of IKONOS images,combined with the good descrip-tion ability of wavelet function for nonlinear signal and the advantages of multi-output support vector regres-sion(MSVR)in the field of multi-dimensional machine learning,a multi-classification method is proposed for IKONOS images based on wavelet kernel MSVR. IKONOS images of the new campus of Anhui university are used for experimentation. The experimental results show that the proposed multi-classification algorithm is superior to the traditional supervised two-category method and unsupervised K-means classification meth-od,and obtains better classification results.%针对IKONOS影像波段和光谱特点,结合小波函数对非线性信号的良好描述能力和多输出支持向量回归(MSVR)在多维机器学习领域的优势,提出一种基于小波核MSVR的IKONOS影像多分类方法,并以安徽大学新校区的IKONOS影像进行仿真实验。结果表明,提出的多分类算法优于传统的有监督二分类方法和无监督K-Means分类方法,获得较好的分类效果。

  11. 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.

  12. 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.

  13. 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.

  14. Complex Wavelet Transform-Based Face Recognition

    Directory of Open Access Journals (Sweden)

    2009-03-01

    Full Text Available Complex approximately analytic wavelets provide a local multiscale description of images with good directional selectivity and invariance to shifts and in-plane rotations. Similar to Gabor wavelets, they are insensitive to illumination variations and facial expression changes. The complex wavelet transform is, however, less redundant and computationally efficient. In this paper, we first construct complex approximately analytic wavelets in the single-tree context, which possess Gabor-like characteristics. We, then, investigate the recently developed dual-tree complex wavelet transform (DT-CWT and the single-tree complex wavelet transform (ST-CWT for the face recognition problem. Extensive experiments are carried out on standard databases. The resulting complex wavelet-based feature vectors are as discriminating as the Gabor wavelet-derived features and at the same time are of lower dimension when compared with that of Gabor wavelets. In all experiments, on two well-known databases, namely, FERET and ORL databases, complex wavelets equaled or surpassed the performance of Gabor wavelets in recognition rate when equal number of orientations and scales is used. These findings indicate that complex wavelets can provide a successful alternative to Gabor wavelets for face recognition.

  15. 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...

  16. Pedestrian detection based on redundant wavelet transform

    Science.gov (United States)

    Huang, Lin; Ji, Liping; Hu, Ping; Yang, Tiejun

    2016-10-01

    Intelligent video surveillance is to analysis video or image sequences captured by a fixed or mobile surveillance camera, including moving object detection, segmentation and recognition. By using it, we can be notified immediately in an abnormal situation. Pedestrian detection plays an important role in an intelligent video surveillance system, and it is also a key technology in the field of intelligent vehicle. So pedestrian detection has very vital significance in traffic management optimization, security early warn and abnormal behavior detection. Generally, pedestrian detection can be summarized as: first to estimate moving areas; then to extract features of region of interest; finally to classify using a classifier. Redundant wavelet transform (RWT) overcomes the deficiency of shift variant of discrete wavelet transform, and it has better performance in motion estimation when compared to discrete wavelet transform. Addressing the problem of the detection of multi-pedestrian with different speed, we present an algorithm of pedestrian detection based on motion estimation using RWT, combining histogram of oriented gradients (HOG) and support vector machine (SVM). Firstly, three intensities of movement (IoM) are estimated using RWT and the corresponding areas are segmented. According to the different IoM, a region proposal (RP) is generated. Then, the features of a RP is extracted using HOG. Finally, the features are fed into a SVM trained by pedestrian databases and the final detection results are gained. Experiments show that the proposed algorithm can detect pedestrians accurately and efficiently.

  17. 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.

  18. 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.

  19. Discrete directional wavelet bases for image compression

    Science.gov (United States)

    Dragotti, Pier L.; Velisavljevic, Vladan; Vetterli, Martin; Beferull-Lozano, Baltasar

    2003-06-01

    The application of the wavelet transform in image processing is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions. Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show very interesting gains compared to the standard two-dimensional analysis.

  20. 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.

  1. Embedded Zero -Tree Wavelet Based Image Steganography

    OpenAIRE

    Vijendra Rai; Jaishree Jain; Ajay Kr. Yadav; Sheshmani Yadav

    2012-01-01

    Image steganography using Discrete Wavelet Transform can attain very good results as compared to traditional methods, in this paper we discuss a method to embed digital watermark based on modifying frequency coefficient in discrete wavelet transform (DWT) domain. This method uses the embedded zero-tree (EZW) algorithm to insert a watermark in discrete wavelet transform domain. EZW is an effective image compression algorithm, having property that image in the bit stream are generated in order...

  2. Wavelet-based denoising using local Laplace prior

    Science.gov (United States)

    Rabbani, Hossein; Vafadust, Mansur; Selesnick, Ivan

    2007-09-01

    Although wavelet-based image denoising is a powerful tool for image processing applications, relatively few publications have addressed so far wavelet-based video denoising. The main reason is that the standard 3-D data transforms do not provide useful representations with good energy compaction property, for most video data. For example, the multi-dimensional standard separable discrete wavelet transform (M-D DWT) mixes orientations and motions in its subbands, and produces the checkerboard artifacts. So, instead of M-D DWT, usually oriented transforms suchas multi-dimensional complex wavelet transform (M-D DCWT) are proposed for video processing. In this paper we use a Laplace distribution with local variance to model the statistical properties of noise-free wavelet coefficients. This distribution is able to simultaneously model the heavy-tailed and intrascale dependency properties of wavelets. Using this model, simple shrinkage functions are obtained employing maximum a posteriori (MAP) and minimum mean squared error (MMSE) estimators. These shrinkage functions are proposed for video denoising in DCWT domain. The simulation results shows that this simple denoising method has impressive performance visually and quantitatively.

  3. 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.

  4. 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.

  5. 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...

  6. MULTI-RESOLUTION MOTION ESTIMATION AND COMPENSATION BASED ON ADJACENT PREDICTION OF FRAME DIFFERENCE IN WAVELET DOMAIN

    Institute of Scientific and Technical Information of China (English)

    Tang Guowei; Gu Guochang

    2009-01-01

    Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies,a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed.Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region.Then the motion region was extracted to carry out motion estimation and motion compensation on it.The experiment results show that the encoding efficiency of motion vector is promoted,the complexity of motion es timation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Reso lution Motion Estimation (MRME) is improved.

  7. Multi-sensor fusion system using wavelet-based detection algorithm applied to physiological monitoring under high-G environment

    Science.gov (United States)

    Ryoo, Han Chool

    2000-06-01

    A significant problem in physiological state monitoring systems with single data channels is high rates of false alarm. In order to reduce false alarm probability, several data channels can be integrated to enhance system performance. In this work, we have investigated a sensor fusion methodology applicable to physiological state monitoring, which combines local decisions made from dispersed detectors. Difficulties in biophysical signal processing are associated with nonstationary signal patterns and individual characteristics of human physiology resulting in nonidentical observation statistics. Thus a two compartment design, a modified version of well established fusion theory in communication systems, is presented and applied to biological signal processing where we combine discrete wavelet transforms (DWT) with sensor fusion theory. The signals were decomposed in time-frequency domain by discrete wavelet transform (DWT) to capture localized transient features. Local decisions by wavelet power analysis are followed by global decisions at the data fusion center operating under an optimization criterion, i.e., minimum error criterion (MEC). We used three signals acquired from human volunteers exposed to high-G forces at the human centrifuge/dynamic flight simulator facility in Warminster, PA. The subjects performed anti-G straining maneuvers to protect them from the adverse effects of high-G forces. These maneuvers require muscular tensing and altered breathing patterns. We attempted to determine the subject's state by detecting the presence or absence of the voluntary anti-G straining maneuvers (AGSM). During the exposure to high G force the respiratory patterns, blood pressure and electroencephalogram (EEG) were measured to determine changes in the subject's state. Experimental results show that the probability of false alarm under MEC can be significantly reduced by applying the same rule found at local thresholds to all subjects, and MEC can be employed as a

  8. Wavelet spectrum analysis on energy transfer of multi-scale structures in wall turbulence

    Institute of Scientific and Technical Information of China (English)

    Zhen-yan XIA; Yan TIAN; Nan JIANG

    2009-01-01

    The streamwise velocity components at different vertical heights in wall turbulence were measured. Wavelet transform was used to study the turbulent energy spectra, indicating that the global spectrum results from the weighted average of Fourier spectrum based on wavelet scales. Wavelet transform with more vanishing moments can express the declining of turbulent spectrum. The local wavelet spectrum shows that the physical phenomena such as deformation or breakup of eddies are related to the vertical position in the boundary layer, and the energy-containing eddies exist in a multi-scale form. Moreover, the size of these eddies increases with the measured points moving out of the wall. In the buffer region, the small scale energy-containing eddies with higher frequency are excited. In the outer region, the maximal energy is concentrated in the low-frequency large-scale eddies, and the frequency domain of energy-containing eddies becomes narrower.

  9. 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.

  10. Wavelet-based image compression using fixed residual value

    Science.gov (United States)

    Muzaffar, Tanzeem; Choi, Tae-Sun

    2000-12-01

    Wavelet based compression is getting popular due to its promising compaction properties at low bitrate. Zerotree wavelet image coding scheme efficiently exploits multi-level redundancy present in transformed data to minimize coding bits. In this paper, a new technique is proposed to achieve high compression by adding new zerotree and significant symbols to original EZW coder. Contrary to four symbols present in basic EZW scheme, modified algorithm uses eight symbols to generate fewer bits for a given data. Subordinate pass of EZW is eliminated and replaced with fixed residual value transmission for easy implementation. This modification simplifies the coding technique as well and speeds up the process, retaining the property of embeddedness.

  11. Multi-resolution analysis for ear recognition using wavelet features

    Science.gov (United States)

    Shoaib, M.; Basit, A.; Faye, I.

    2016-11-01

    Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.

  12. 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.

  13. Ripples in Communication: Reconfigurable and Adaptive Wireless Communication Systems based on Wavelet Packet Modulators

    NARCIS (Netherlands)

    Lakshmanan, M.K.

    2011-01-01

    Wavelet Packet Modulation (WPM) is a multi-carrier transmission technique that uses orthogonal wavelet packet bases to combine a collection of information bits into a single composite signal. This system can be considered as a viable alternative, for wide-band communication, to the popular Orthogona

  14. Wavelet based Non LSB Steganography

    Directory of Open Access Journals (Sweden)

    H S Manjunatha Reddy

    2011-11-01

    Full Text Available Steganography is the methods of communicating secrete information hidden in the cover object. The messages hidden in a host data are digital image, video or audio files, etc, and then transmitted secretly to the destination. In this paper we propose Wavelet based Non LSB Steganography (WNLS. The cover image is segmented into 4*4 cells and DWT/IWT is applied on each cell. The 2*2 cell of HH band of DWT/IWT are considered and manipulated with payload bit pairs using identity matrix to generate stego image. The key is used to extract payload bit pairs at the destination. It is observed that the PSNR values are better in the case of IWT compare to DWT for all image formats. The algorithm can’t be detected by existing steganalysis techniques such as chi-square and pair of values techniques. The PSNR values are high in the case of raw images compared to formatted images.

  15. WaveJava: Wavelet-based network computing

    Science.gov (United States)

    Ma, Kun; Jiao, Licheng; Shi, Zhuoer

    1997-04-01

    Wavelet is a powerful theory, but its successful application still needs suitable programming tools. Java is a simple, object-oriented, distributed, interpreted, robust, secure, architecture-neutral, portable, high-performance, multi- threaded, dynamic language. This paper addresses the design and development of a cross-platform software environment for experimenting and applying wavelet theory. WaveJava, a wavelet class library designed by the object-orient programming, is developed to take advantage of the wavelets features, such as multi-resolution analysis and parallel processing in the networking computing. A new application architecture is designed for the net-wide distributed client-server environment. The data are transmitted with multi-resolution packets. At the distributed sites around the net, these data packets are done the matching or recognition processing in parallel. The results are fed back to determine the next operation. So, the more robust results can be arrived quickly. The WaveJava is easy to use and expand for special application. This paper gives a solution for the distributed fingerprint information processing system. It also fits for some other net-base multimedia information processing, such as network library, remote teaching and filmless picture archiving and communications.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  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. 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.

  2. Multi-Scale SSA or Data-Adaptive Wavelets

    Science.gov (United States)

    Yiou, P.; Sornette, D.; Sornette, D.; Sornette, D.; Ghil, M.; Ghil, M.

    2001-05-01

    Using multi-scale ideas from wavelet analysis, the singular-spectrum analysis (SSA) is extended to the study of nonstationary time series, including the case where their variance diverges. The wavelet transform is similar to a local Fourier transform within a finite moving window whose width W, proportional to the major period of interest, is varied to explore a broad range of such periods. SSA, on the other hand, relies on the construction of the lag-correlation matrix C on M lagged copies of the time series over a fixed window width W proportional to M to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components. The proposed multi-scale SSA is a local SSA analysis within a moving window of width Mwavelets; successive eigenvectors of C(M) correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency domain, by a suitable localization of the signal's correlation matrix. We present several examples of application to synthetic signals with fractal or power-law behavior which mimic selected features of certain climatic or geophysical time series. The method is applied to the monthly values of the Southern Oscillation index (SOI) which captures major features of the El Niño/Southern Oscillation in the Tropical Pacific. Our methodology highlights an abrupt periodicity shift in the SOI near 1960. This abrupt shift between 5 and 3 years supports the Devil's staircase scenario for the El Niño/Southern Oscillation phenomenon.

  3. 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.

  4. 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...

  5. A multi-resolution wavelet algorithm for hand vein pattern recognition

    Institute of Scientific and Technical Information of China (English)

    Yunxin Wang; Tiegen Liu; Junfeng Jiang

    2008-01-01

    A novel hand vein recognition algorithm is developed based on multi-resolution wavelet analysis. The texture feature of hand vein can be extracted by three-level wavelet decomposition. Furthermore, Knearest neighbor (KNN) with support vector machines (SVM) and minimum distance classifier (MDC) are employed for feature matching. Finally, the experiments are respectively performed in identification and verification modes using Tianjin University (TJU) hand vein image database constructed by our group.The results show the feasibility and effectiveness of the proposed method.

  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. 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.

  8. 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.

  9. Wavelet-based zerotree coding of aerospace images

    Science.gov (United States)

    Franques, Victoria T.; Jain, Vijay K.

    1996-06-01

    This paper presents a wavelet based image coding method achieving high levels of compression. A multi-resolution subband decomposition system is constructed using Quadrature Mirror Filters. Symmetric extension and windowing of the multi-scaled subbands are incorporated to minimize the boundary effects. Next, the Embedded Zerotree Wavelet coding algorithm is used for data compression method. Elimination of the isolated zero symbol, for certain subbands, leads to an improved EZW algorithm. Further compression is obtained with an adaptive arithmetic coder. We achieve a PSNR of 26.91 dB at a bit rate of 0.018, 35.59 dB at a bit rate of 0.149, and 43.05 dB at 0.892 bits/pixel for the aerospace image, Refuel.

  10. Construction of a class of Daubechies type wavelet bases

    Energy Technology Data Exchange (ETDEWEB)

    Li Dengfeng [Institute of Applied Mathematics, School of Mathematics and Information Sciences Henan University, Kaifeng 475001 (China); Wu Guochang [College of Information, Henan University of Finance and Economics, Zhengzhou 450002 (China)], E-mail: archang-0111@163.com

    2009-10-15

    Extensive work has been done in the theory and the construction of compactly supported orthonormal wavelet bases of L{sup 2}(R). Some of the most distinguished work was done by Daubechies, who constructed a whole family of such wavelet bases. In this paper, we construct a class of orthonormal wavelet bases by using the principle of Daubechies, and investigate the length of support and the regularity of these wavelet bases.

  11. 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

  12. Fingerprint verification based on wavelet subbands

    Science.gov (United States)

    Huang, Ke; Aviyente, Selin

    2004-08-01

    Fingerprint verification has been deployed in a variety of security applications. Traditional minutiae detection based verification algorithms do not utilize the rich discriminatory texture structure of fingerprint images. Furthermore, minutiae detection requires substantial improvement of image quality and is thus error-prone. In this paper, we propose an algorithm for fingerprint verification using the statistics of subbands from wavelet analysis. One important feature for each frequency subband is the distribution of the wavelet coefficients, which can be modeled with a Generalized Gaussian Density (GGD) function. A fingerprint verification algorithm that combines the GGD parameters from different subbands is proposed to match two fingerprints. The verification algorithm in this paper is tested on a set of 1,200 fingerprint images. Experimental results indicate that wavelet analysis provides useful features for the task of fingerprint verification.

  13. Scalable still image coding based on wavelet

    Science.gov (United States)

    Yan, Yang; Zhang, Zhengbing

    2005-02-01

    The scalable image coding is an important objective of the future image coding technologies. In this paper, we present a kind of scalable image coding scheme based on wavelet transform. This method uses the famous EZW (Embedded Zero tree Wavelet) algorithm; we give a high-quality encoding to the ROI (region of interest) of the original image and a rough encoding to the rest. This method is applied well in limited memory space condition, and we encode the region of background according to the memory capacity. In this way, we can store the encoded image in limited memory space easily without losing its main information. Simulation results show it is effective.

  14. Signal extrapolation based on wavelet representation

    Science.gov (United States)

    Xia, Xiang-Gen; Kuo, C.-C. Jay; Zhang, Zhen

    1993-11-01

    The Papoulis-Gerchberg (PG) algorithm is well known for band-limited signal extrapolation. We consider the generalization of the PG algorithm to signals in the wavelet subspaces in this research. The uniqueness of the extrapolation for continuous-time signals is examined, and sufficient conditions on signals and wavelet bases for the generalized PG (GPG) algorithm to converge are given. We also propose a discrete GPG algorithm for discrete-time signal extrapolation, and investigate its convergence. Numerical examples are given to illustrate the performance of the discrete GPG algorithm.

  15. Image reconstruction using wavelet multi-resolution technique for time-domain diffuse optical tomography

    Science.gov (United States)

    Yang, Fang; Gao, Feng; Jiao, Yuting; Zhao, Huijuan

    2010-02-01

    It is generally believed that the inverse problem in diffuse optical tomography (DOT) is highly ill-posed and its solution is always under-determined and sensitive to noise, which is the main problem in the application of DOT. In this paper, we propose a method on image reconstruction for time-domain diffuse optical tomography based on panel detection and Finite-Difference Method, and introduce an approach to reduce the number of unknown parameters in the reconstruction process. We propose a multi-level scheme to reduce the number of unknowns by parameterizing the spatial distribution of optical properties via wavelet transform and then reconstruct the coefficients of this transform. Compared with previous traditional uni-level full spatial domain algorithm, this method can efficiently improve the reconstruction quality. Numerical simulations show that wavelet-based multi-level inversion is superior to the uni-level algebraic reconstruction technique.

  16. 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.

  17. 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...

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. Compression of Ultrasonic NDT Image by Wavelet Based Local Quantization

    Science.gov (United States)

    Cheng, W.; Li, L. Q.; Tsukada, K.; Hanasaki, K.

    2004-02-01

    Compression on ultrasonic image that is always corrupted by noise will cause `over-smoothness' or much distortion. To solve this problem to meet the need of real time inspection and tele-inspection, a compression method based on Discrete Wavelet Transform (DWT) that can also suppress the noise without losing much flaw-relevant information, is presented in this work. Exploiting the multi-resolution and interscale correlation property of DWT, a simple way named DWCs classification, is introduced first to classify detail wavelet coefficients (DWCs) as dominated by noise, signal or bi-effected. A better denoising can be realized by selective thresholding DWCs. While in `Local quantization', different quantization strategies are applied to the DWCs according to their classification and the local image property. It allocates the bit rate more efficiently to the DWCs thus achieve a higher compression rate. Meanwhile, the decompressed image shows the effects of noise suppressed and flaw characters preserved.

  3. Ground extraction from airborne laser data based on wavelet analysis

    Science.gov (United States)

    Xu, Liang; Yang, Yan; Jiang, Bowen; Li, Jia

    2007-11-01

    With the advantages of high resolution and accuracy, airborne laser scanning data are widely used in topographic mapping. In order to generate a DTM, measurements from object features such as buildings, vehicles and vegetation have to be classified and removed. However, the automatic extraction of bare earth from point clouds acquired by airborne laser scanning equipment remains a problem in LIDAR data filtering nowadays. In this paper, a filter algorithm based on wavelet analysis is proposed. Relying on the capability of detecting discontinuities of continuous wavelet transform and the feature of multi-resolution analysis, the object points can be removed, while ground data are preserved. In order to evaluate the performance of this approach, we applied it to the data set used in the ISPRS filter test in 2003. 15 samples have been tested by the proposed approach. Results showed that it filtered most of the objects like vegetation and buildings, and extracted a well defined ground model.

  4. SPEECH/MUSIC CLASSIFICATION USING WAVELET BASED FEATURE EXTRACTION TECHNIQUES

    Directory of Open Access Journals (Sweden)

    Thiruvengatanadhan Ramalingam

    2014-01-01

    Full Text Available Audio classification serves as the fundamental step towards the rapid growth in audio data volume. Due to the increasing size of the multimedia sources speech and music classification is one of the most important issues for multimedia information retrieval. In this work a speech/music discrimination system is developed which utilizes the Discrete Wavelet Transform (DWT as the acoustic feature. Multi resolution analysis is the most significant statistical way to extract the features from the input signal and in this study, a method is deployed to model the extracted wavelet feature. Support Vector Machines (SVM are based on the principle of structural risk minimization. SVM is applied to classify audio into their classes namely speech and music, by learning from training data. Then the proposed method extends the application of Gaussian Mixture Models (GMM to estimate the probability density function using maximum likelihood decision methods. The system shows significant results with an accuracy of 94.5%.

  5. The evolution of spillover effects between oil and stock markets across multi-scales using a wavelet-based GARCH-BEKK model

    Science.gov (United States)

    Liu, Xueyong; An, Haizhong; Huang, Shupei; Wen, Shaobo

    2017-01-01

    Aiming to investigate the evolution of mean and volatility spillovers between oil and stock markets in the time and frequency dimensions, we employed WTI crude oil prices, the S&P 500 (USA) index and the MICEX index (Russia) for the period Jan. 2003-Dec. 2014 as sample data. We first applied a wavelet-based GARCH-BEKK method to examine the spillover features in frequency dimension. To consider the evolution of spillover effects in time dimension at multiple-scales, we then divided the full sample period into three sub-periods, pre-crisis period, crisis period, and post-crisis period. The results indicate that spillover effects vary across wavelet scales in terms of strength and direction. By analysis the time-varying linkage, we found the different evolution features of spillover effects between the Oil-US stock market and Oil-Russia stock market. The spillover relationship between oil and US stock market is shifting to short-term while the spillover relationship between oil and Russia stock market is changing to all time scales. That result implies that the linkage between oil and US stock market is weakening in the long-term, and the linkage between oil and Russia stock market is getting close in all time scales. This may explain the phenomenon that the US stock index and the Russia stock index showed the opposite trend with the falling of oil price in the post-crisis period.

  6. Design of wavelet-based ECG detector for implantable cardiac pacemakers.

    Science.gov (United States)

    Min, Young-Jae; Kim, Hoon-Ki; Kang, Yu-Ri; Kim, Gil-Su; Park, Jongsun; Kim, Soo-Won

    2013-08-01

    A wavelet Electrocardiogram (ECG) detector for low-power implantable cardiac pacemakers is presented in this paper. The proposed wavelet-based ECG detector consists of a wavelet decomposer with wavelet filter banks, a QRS complex detector of hypothesis testing with wavelet-demodulated ECG signals, and a noise detector with zero-crossing points. In order to achieve high detection accuracy with low power consumption, a multi-scaled product algorithm and soft-threshold algorithm are efficiently exploited in our ECG detector implementation. Our algorithmic and architectural level approaches have been implemented and fabricated in a standard 0.35 μm CMOS technology. The testchip including a low-power analog-to-digital converter (ADC) shows a low detection error-rate of 0.196% and low power consumption of 19.02 μW with a 3 V supply voltage.

  7. Wavelet-Based Monitoring for Biosurveillance

    Directory of Open Access Journals (Sweden)

    Galit Shmueli

    2013-07-01

    Full Text Available Biosurveillance, focused on the early detection of disease outbreaks, relies on classical statistical control charts for detecting disease outbreaks. However, such methods are not always suitable in this context. Assumptions of normality, independence and stationarity are typically violated in syndromic data. Furthermore, outbreak signatures are typically of unknown patterns and, therefore, call for general detectors. We propose wavelet-based methods, which make less assumptions and are suitable for detecting abnormalities of unknown form. Wavelets have been widely used for data denoising and compression, but little work has been published on using them for monitoring. We discuss monitoring-based issues and illustrate them using data on military clinic visits in the USA.

  8. 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.

  9. 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

  10. 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.

  11. Comparison on Integer Wavelet Transforms in Spherical Wavelet Based Image Based Relighting

    Institute of Scientific and Technical Information of China (English)

    WANGZe; LEEYin; LEUNGChising; WONGTientsin; ZHUYisheng

    2003-01-01

    To provide a good quality rendering in the Image based relighting (IBL) system, tremendous reference images under various illumination conditions are needed. Therefore data compression is essential to enable interactive action. And the rendering speed is another crucial consideration for real applications. Based on Spherical wavelet transform (SWT), this paper presents a quick representation method with Integer wavelet transform (IWT) for the IBL system. It focuses on comparison on different IWTs with the Embedded zerotree wavelet (EZW) used in the IBL system. The whole compression procedure contains two major compression steps. Firstly, SWT is applied to consider the correlation among different reference images. Secondly, the SW transformed images are compressed with IWT based image compression approach. Two IWTs are used and good results are showed in the simulations.

  12. Scale-Dependent Representations of Relief Based on Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    Automatic generalization of geographic information is the core of multi-scale representation of spatial data,but the scale-dependent generalization methods are far from abundant because of its extreme complicacy.This paper puts forward a new consistency model about scale-dependent representations of relief based on wavelet analysis,and discusses the thresholds in the model so as to acquire the continual representations of relief with different details between scales.The model not only meets the need of automatic generalization but also is scale-dependent completely.Some practical examples are given.

  13. 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 ”.

  14. 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.

  15. MULTI-RESOLUTION WAVELET ANALYSES OF TWO DIFFERENT PERFECTİONİSM SCALES: A UNIVERSITY SAMPLE

    Directory of Open Access Journals (Sweden)

    Y. KARACA

    2013-01-01

    Full Text Available Our prior studies indicated that statistical and wavelet analyses of perfectionism inventories were evaluated by SPSS and Wavelet packets. The main aim of the present study is to investigate different scale affects on perfectionism. We proposed that changes of low-frequency Meyer wavelets reflect students' perfectionism levels. We used Wavelet 1D and continuous 1D Wavelet analyses to measure their time dependence. We studied students' questionnaires. Multi-resolution analysis was obtained from continuous and discrete data as a function of cases at different scales. Large scale effects are assumed to play an important role on students with higher others-oriented perfectionism and adaptive perfectionism. Continuous wavelet 1D (Mexh analyses show the similar results and, large scale effects play an important role on students' behavior. In contrast, lower scale effects are assumed to play an important role on students with adaptive perfectionism and self-directed perfectionism

  16. Discrete directional wavelet bases and frames: analysis and applications

    Science.gov (United States)

    Dragotti, Pier Luigi; Velisavljevic, Vladan; Vetterli, Martin; Beferull-Lozano, Baltasar

    2003-11-01

    The application of the wavelet transform in image processing is most frequently based on a separable construction. Lines and columns in an image are treated independently and the basis functions are simply products of the corresponding one dimensional functions. Such method keeps simplicity in design and computation, but is not capable of capturing properly all the properties of an image. In this paper, a new truly separable discrete multi-directional transform is proposed with a subsampling method based on lattice theory. Alternatively, the subsampling can be omitted and this leads to a multi-directional frame. This transform can be applied in many areas like denoising, non-linear approximation and compression. The results on non-linear approximation and denoising show interesting gains compared to the standard two-dimensional analysis.

  17. 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.

  18. Fingerprint spoof detection using wavelet based local binary pattern

    Science.gov (United States)

    Kumpituck, Supawan; Li, Dongju; Kunieda, Hiroaki; Isshiki, Tsuyoshi

    2017-02-01

    In this work, a fingerprint spoof detection method using an extended feature, namely Wavelet-based Local Binary Pattern (Wavelet-LBP) is introduced. Conventional wavelet-based methods calculate wavelet energy of sub-band images as the feature for discrimination while we propose to use Local Binary Pattern (LBP) operation to capture the local appearance of the sub-band images instead. The fingerprint image is firstly decomposed by two-dimensional discrete wavelet transform (2D-DWT), and then LBP is applied on the derived wavelet sub-band images. Furthermore, the extracted features are used to train Support Vector Machine (SVM) classifier to create the model for classifying the fingerprint images into genuine and spoof. Experiments that has been done on Fingerprint Liveness Detection Competition (LivDet) datasets show the improvement of the fingerprint spoof detection by using the proposed feature.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Wavelet Based Semi-blind Channel Estimation For Multiband OFDM

    OpenAIRE

    Sadough, Sajad; Ichir, Mahieddine; Jaffrot, Emmanuel; Duhamel, Pierre

    2007-01-01

    This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the nu...

  4. 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.

  5. Synthesis of Vibration Waves Based on Wavelet Technology

    Directory of Open Access Journals (Sweden)

    L.H. Zou

    2012-01-01

    Full Text Available A novel method to generate time series of vibration waves is proposed in the paper. Considering the frequency band energy as the criterion, synthesis formulas for fluctuating wind pressure and earthquake ground motion are developed in terms of Daubechies wavelet and Harr wavelet respectively. The wavelet reconstruction method is applicable to both stationary and non-stationary process simulation. Theoretically, for non-stationary (such as seismic process synthesis, it has a better non-stationarity in time-frequency domain than the traditional trigonometric series. Influence of wavelet delamination number and wavelet function type is also analyzed. Numerical results show that the synthesis of vibration waves based on wavelet reconstruction method contains main components of vibration, and can reflect the main properties of practical vibrations.

  6. 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.

  7. Soft sensor modeling of sewage disposal process based on multi-scale wavelet least square support vector regression%基于多尺度小波LSSVR的污水处理过程软测量

    Institute of Scientific and Technical Information of China (English)

    王鲜芳; 朱晓霞; 吴瑞红; 郑延斌

    2012-01-01

    To solve the problem that some parameters are difficult to be measured on-line in the process of waste water disposal, a soft measurement modeling method is presented base on multi-scale wavelet least square support vector machine in this Paper. Mexican-hat wavelet function is used as the support vector kernel function, and further the Multi-scale Wavelet Least square Support Vector Regression (MW-LSSVR) algorithm is presented. Build an advanced model with above SVR and characteristics between BOD&COD, predicting BOD&COD of drainage that had been treated. Through using this method in practical sewage disposal process, the result shows that this modeling method has higher precision and faster learning speed of BOD model, can make accurate predictions, can replace online measuring instrument in some expensive, provide control operation basis to the sewage treatment plant workers, and has a certain practical value.%针对污水处理中某些生物参数难以在线测量的情况,本文提出了一种基于小波核的多尺度最小二乘小波支持向量机软测量建模方法.首先,选取墨西哥草帽小波函数作为最小二乘支持向量机的核函数,进而设计出多尺度小波最小二乘支持向量回归机(MW-LSSVR).然后利用该支持向量机和出水水质参数特性建立混合软测量模型,实现对出水BOD浓度、COD浓度在线预测.通过在实际污水处理过程的应用,结果表明本建模方法具有较高的预测精度和较快的模型学习速度,能对BOD的做出准确的预测,一定程度上可以替代某些昂贵的在线测量仪表,给污水处理厂工作人员提供了控制操作依据,具有一定的实际应用价值.

  8. 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.

  9. 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.

  10. 3-D surface profilometry based on modulation measurement by applying wavelet transform method

    Science.gov (United States)

    Zhong, Min; Chen, Feng; Xiao, Chao; Wei, Yongchao

    2017-01-01

    A new analysis of 3-D surface profilometry based on modulation measurement technique by the application of Wavelet Transform method is proposed. As a tool excelling for its multi-resolution and localization in the time and frequency domains, Wavelet Transform method with good localized time-frequency analysis ability and effective de-noizing capacity can extract the modulation distribution more accurately than Fourier Transform method. Especially for the analysis of complex object, more details of the measured object can be well remained. In this paper, the theoretical derivation of Wavelet Transform method that obtains the modulation values from a captured fringe pattern is given. Both computer simulation and elementary experiment are used to show the validity of the proposed method by making a comparison with the results of Fourier Transform method. The results show that the Wavelet Transform method has a better performance than the Fourier Transform method in modulation values retrieval.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  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. An Orthogonal Wavelet Transform Weighted Multi-Modulus Blind Equalization Algorithm Based on SA-GSO%基于SA-GSO的小波加权多模盲均衡算法

    Institute of Scientific and Technical Information of China (English)

    高敏; 郭业才

    2012-01-01

    When MMA(Multi -modulus Algorithm) is used to equalize high -order QAM, it has many disadvantages, such as slow convergence rate, large mean square error, and so on. In order to overcome the problems, an orthogonal wavelet transform weighted multi - modulus blind equalization algorithm based on simulated annealing optimization glowworm swarm algorithm ( SA - GSO - WT - MMA) was proposed. In the proposed algorithm , the weighted item was increased to the traditional multi - modulus blind equalization algorithm (MMA) , and the simulated annealing glowworm swarm optimization algorithm and the wavelet transform were also introduced in. The proposed algorithm can adjust the modulus value of the cost function value by using the weighted item, it can optimize the initial weight vector of the equalizer by using the strong global optimization ability of SA - GSO , and reduce the signal autocorrelation by using the de - correlation ability of WT. The results from computer simulation show that the proposed algorithm was excellence in improving the convergence rate and reducing the steady - state error.%为解决传统多模盲均衡算法(MMA)在均衡高阶QAM信号时存在的收敛速度慢、稳态误差大等问题,提出了一种基于模拟退火萤火虫优化的小波加权多模盲均衡算法(SA-GSO-WT-WMMA).该算法在MMA的基础上增加了加权项,并引入了模拟退火萤火虫优化(SA-GSO)算法和正交小波变换(Wr),利用加权项自适应地调整算法中代价函数的模值,利用SA-GSO算法极强的全局寻优能力来优化均衡器的初始权向量,利用正交小波变换降低信号的自相关性,有效提高了均衡效果.水声信道仿真实验表明,该算法在降低稳态均方误差和加速收敛速度两方面表现卓越.

  17. 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.

  18. Wavelet-based Multiresolution Particle Methods

    Science.gov (United States)

    Bergdorf, Michael; Koumoutsakos, Petros

    2006-03-01

    Particle methods offer a robust numerical tool for solving transport problems across disciplines, such as fluid dynamics, quantitative biology or computer graphics. Their strength lies in their stability, as they do not discretize the convection operator, and appealing numerical properties, such as small dissipation and dispersion errors. Many problems of interest are inherently multiscale, and their efficient solution requires either multiscale modeling approaches or spatially adaptive numerical schemes. We present a hybrid particle method that employs a multiresolution analysis to identify and adapt to small scales in the solution. The method combines the versatility and efficiency of grid-based Wavelet collocation methods while retaining the numerical properties and stability of particle methods. The accuracy and efficiency of this method is then assessed for transport and interface capturing problems in two and three dimensions, illustrating the capabilities and limitations of our approach.

  19. Remote sensing image compression method based on lift scheme wavelet transform

    Science.gov (United States)

    Tao, Hongjiu; Tang, Xinjian; Liu, Jian; Tian, Jinwen

    2003-06-01

    Based on lifting scheme and the construction theorem of the integer Haar wavelet and biorthogonal wavelet, we propose a new integer wavelet transform construct method on the basis of lift scheme after introduciton of constructing specific-demand biorthogonal wavelet transform using Harr wavelet and Lazy wavelet. In this paper, we represent the method and algorithm of the lifting scheme, and we also give mathematical formulation on this method and experimental results as well.

  20. 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.

  1. 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.

  2. Continuous wavelet transform-based feature selection applied to near-infrared spectral diagnosis of cancer.

    Science.gov (United States)

    Chen, Hui; Lin, Zan; Mo, Lin; Wu, Hegang; Wu, Tong; Tan, Chao

    2015-01-01

    Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy.

  3. 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.

  4. Data-Adaptive Wavelets and Multi-Scale Singular Spectrum Analysis

    CERN Document Server

    Yiou, P; Ghil, M

    1998-01-01

    Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series of length $N$ whose intermittency can give rise to the divergence of their variance. SSA relies on the construction of the lag-covariance matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W = M Dt, with Dt the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M 3/4 W 3/4 N. Multi-scale SSA varies W, while keeping a fixed W/M ratio, and uses the eigenvectors of the corresponding lag-covariance matrix C_M as a data-adaptive wavelets; successive eigenvectors of C_M correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency do...

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

    OpenAIRE

    Niya Chen; Zheng Qian; Xiaofeng Meng

    2013-01-01

    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 decomposi...

  6. 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.

  7. 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.

  8. Image denoising based on wavelet cone of influence analysis

    Science.gov (United States)

    Pang, Wei; Li, Yufeng

    2009-11-01

    Donoho et al have proposed a method for denoising by thresholding based on wavelet transform, and indeed, the application of their method to image denoising has been extremely successful. But this method is based on the assumption that the type of noise is only additive Gaussian white noise, which is not efficient to impulse noise. In this paper, a new image denoising algorithm based on wavelet cone of influence (COI) analyzing is proposed, and which can effectively remove the impulse noise and preserve the image edges via undecimated discrete wavelet transform (UDWT). Furthermore, combining with the traditional wavelet thresholding denoising method, it can be also used to restrain more widely type of noise such as Gaussian noise, impulse noise, poisson noise and other mixed noise. Experiment results illustrate the advantages of this method.

  9. 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。

  10. 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

  11. Wavelet Based Semi-blind Channel Estimation For Multiband OFDM

    CERN Document Server

    Sadough, Sajad; Jaffrot, Emmanuel; Duhamel, Pierre

    2007-01-01

    This paper introduces an expectation-maximization (EM) algorithm within a wavelet domain Bayesian framework for semi-blind channel estimation of multiband OFDM based UWB communications. A prior distribution is chosen for the wavelet coefficients of the unknown channel impulse response in order to model a sparseness property of the wavelet representation. This prior yields, in maximum a posteriori estimation, a thresholding rule within the EM algorithm. We particularly focus on reducing the number of estimated parameters by iteratively discarding ``unsignificant'' wavelet coefficients from the estimation process. Simulation results using UWB channels issued from both models and measurements show that under sparsity conditions, the proposed algorithm outperforms pilot based channel estimation in terms of mean square error and bit error rate and enhances the estimation accuracy with less computational complexity than traditional semi-blind methods.

  12. Hand posture recognizer based on separator wavelet networks

    Science.gov (United States)

    Bouchrika, Tahani; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

    2015-12-01

    This paper presents a novel hand posture recognizer based on separator wavelet networks (SWNs). Aiming at creating a robust and rapid hand posture recognizer, we have contributed by proposing a new training algorithm for the wavelet network classifier based on fast wavelet transform (FWN). So, the contribution resides in reducing the number of WNs modeling training data. To make that, inspiring from the adaboost feature selection method, we thought to create SWNs (n-1 WNs for n classes) instead of modeling each training sample by its wavelet network (WN). By proposing the new training algorithm, the recognition phase will be positively influenced. It will be more rapid thanks to the reduction of the number of comparisons between test images WNs and training WNs. Comparisons with other works, employing universal hand posture datasets are presented and discussed. Obtained results have shown that the new hand posture recognizer is comparable to previously established ones.

  13. Classification of Underwater Signals Using Wavelet-Based Decompositions

    Science.gov (United States)

    1998-06-01

    proposed by Learned and Willsky [21], uses the SVD information obtained from the power mapping, the second one selects the most within-a-class...34 SPIE, Vol. 2242, pp. 792-802, Wavelet Applications, 1994 [14] R. Coifman and D. Donoho, "Translation-Invariant Denoising ," Internal Report...J. Barsanti, Jr., Denoising of Ocean Acoustic Signals Using Wavelet-Based Techniques, MSEE Thesis, Naval Postgraduate School, Monterey, California

  14. A Fractional Random Wavelet Transform Based Image Steganography

    OpenAIRE

    G.K. Rajini; RAMACHANDRA REDDY G.

    2015-01-01

    This study presents a novel technique for image steganography based on Fractional Random Wavelet Transform. This transform has all the features of wavelet transform with randomness and fractional order built into it. The randomness and fractional order in the algorithm brings in robustness and additional layers of security to steganography. The stegano image generated by this algorithm contains both cover image and hidden image and image degradation is not observed in it. The steganography st...

  15. Construction of Interval Wavelet Based on Restricted Variational Principle and Its Application for Solving Differential Equations

    Directory of Open Access Journals (Sweden)

    Qin Ma

    2008-05-01

    Full Text Available Based on restricted variational principle, a novel method for interval wavelet construction is proposed. For the excellent local property of quasi-Shannon wavelet, its interval wavelet is constructed, and then applied to solve ordinary differential equations. Parameter choices for the interval wavelet method are discussed and its numerical performance is demonstrated.

  16. 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.  

  17. 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.

  18. 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.

  19. 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 to ex...... 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....

  20. 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...

  1. Wavelet methods in multi-conjugate adaptive optics

    OpenAIRE

    Helin, T; Yudytskiy, M.

    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 m...

  2. A New Multi-direction Adaptive Weighted Pseudo Median Filtering Algorithm Based on Wavelet Domain%一种小波域多方向自适应加权伪中值滤波算法

    Institute of Scientific and Technical Information of China (English)

    郑明言

    2014-01-01

    为了有效地滤除红外图像中的噪声,提出了一种小波域多方向自适应加权伪中值滤波算法。该算法首先对红外噪声图像的各高频分解子图像分别进行噪声点检测和标记;然后根据各子图像中像素点分布特征分别设计出4类具有多方向性的滤波模板进行自适应加权滤波;最后将低频分解子图像与滤波后的各小波高频分解子图像进行重构。分别将中值滤波(MF)、伪中值滤波(PMF)、极值中值滤波(EMF)、加权中值滤波(WMF)、以及本文算法应用于标准测试图像以及红外图像去噪,并引入峰值信噪比(PSNR)、平均绝对误差(MAE)进行去噪效果评定。标准测试图像和红外图像仿真结果表明,该算法性能明显优于PMF,且相对于与其余几类同类型算法而言,也具有一定的优势。%In order to filter the noise in infrared image, a multi-direction adaptive weighted pseudo median filtering algorithm is proposed based on wavelet transform. Firstly, the salt & pepper noise image is conducted by wavelet transform, and the high-frequency sub-images and low-frequency sub-image are obtained. The noise distribution areas of the high-frequency sub-images are detected and labeled effectively. Then, according to the characteristics of the ground objects and the features of the directionality of the high frequency wavelet decomposition sub-images, four kinds of directional filtering templates are respectively designed so as to deal with the noise through adaptive weighted filtering. Finally, low-frequency sub-image and high-frequency sub-images are reconstructed. The median filtering(MF), pseudo median filtering(PMF), extreme median filtering(EMF), weighted median filtering(WMF) and the algorithm in this paper are used to filter the salt & pepper noise in standard test image and infrared image. Peak signal to noise ratio(PSNR) and mean absolute error (MAE)are adopted to evaluate the

  3. 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.

  4. 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.

  5. Detecting Multi-Scale Coherent Eddy Structures and Intermittency in Turbulent Boundary Layer by Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    JIANG Nan; ZHANG Jin

    2005-01-01

    @@ Multi-scale decomposition by wavelet transform has been performed to velocity time sequences obtained by fine measurements of turbulent boundary layer flow. A conditional sampling technique for detecting multi-scale coherent eddy structures in turbulent field is proposed by using multi-scale instantaneous intensity factor and flatness factor of wavelet coefficients. Although the number of coherent eddy structures in the turbulent boundary layer is very small, their energy percentage with respect to the turbulence kinetic energy is high. Especially in buffer layer, the energy percentages of coherent structures are significantly higher than those in the logarithmic layer, indicating that the buffer layer is the most active region in the turbulent boundary layer. These multi-scale coherent eddy structures share some common dynamical characteristics and are responsible for the anomalous scaling law in the turbulent boundary layer.

  6. 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.

  7. Mean shift based log-Gabor wavelet image coding

    Institute of Scientific and Technical Information of China (English)

    LI Ji-liang; FANG Xiang-zhong; HOU Jun

    2007-01-01

    In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.

  8. 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.

  9. 基于多小波变换与QAR编码的信息隐藏算法%Information Hiding Algorithm based on Multi-wavelet Transform and QAR Coding System

    Institute of Scientific and Technical Information of China (English)

    杨涛; 任帅; 索丽; 娄棕棕; 张弢; 慕德俊

    2016-01-01

    Aiming at the contradiction of between invisibility and robustness and at the lack of error-detecting capacity for common information hiding algorithms, a novel algorithm based on multi-wavelet transform and QAR coding system is proposed. The digital image carrier in this scheme is preprocessed with CARDBAL2, GHM transform andlαβ color space translation, and then, the secret information coded by QAR system is embedded into preprocessed carrier for production of a stego image, thus to realize secure communication of the confidential information. The experimental results indicate that the proposed algorithm exhibits clear superiorities in invisibility, robustness and the sensitivity for distortion.%针对信息隐藏算法中常见的不可见性和鲁棒性相矛盾且不具备检错能力的缺点,提出一种新的基于多小波变换与QAR编码的信息隐藏算法.该算法利用CARDBAL2多小波变换、GHM多小波变换和lαβ颜色空间转换等方法对数字图像载体进行预处理,再将经过QAR(Quotient and remainder)编码的秘密信息嵌入到预处理后的载体图像中以生成含秘图像,从而达到将秘密信息安全传输的目的.实验结果显示,算法的优势在于其不可见性、鲁棒性和感知篡改性.

  10. Some Contributions to Wavelet Based Image Coding

    Science.gov (United States)

    2000-07-01

    MSE or PSNR. However, it is noted that JZW does not implement the embedded property as in EZW and SPIHT and that the embedded property can be...achieved by passing the JND quantized wavelet coefficients to EZW or SPIHT. It is noted that the lowest frequency (the coarsest scale) band (LL band) is the...other higher frequency subbands can be efficiently encoded using our zero-tree encoding scheme which is derived from EZW and [improved version of EZW by

  11. Multi fault detection of the roller bearing using the wavelet transformand principal component analysis

    Directory of Open Access Journals (Sweden)

    Jaafar Khalaf Ali, Qusai Talib Abdulwahab, Sajjad Nayyef Abdul kareem

    2016-01-01

    Full Text Available Vibration monitoring and analysis techniques are the key features of successful predictive and proactive maintenance programs. In this work, advanced vibration analysis techniques like Wavelet transform, Principle Component Analysis (PCA and Squared Prediction Error (SPE have been used to detect the faults in bearing. Discrete Wavelet Transforms (DWT decomposes signal to high and low frequencies. PCA is employed to extract important feature and reduce dimension. SPE is used to detect the bearing faults. The experimental data is collected from SpectraQuest's Machine Fault Simulator (MFS-4 apparatus. In this study, four rollers were bearing defects (ball defect, outer race defect, inner race defect and combined defect for 1" and 3/4" bearing. From the results, the suggestion techniques can be used to detect multi-faults in the bearings. The results show that the best wavelet function is Coiflets4 in this method.

  12. Propagation source wavelet phase extraction using multi-taper method coherence estimation

    Science.gov (United States)

    Hariri Naghadeh, Diako; Morley, Christopher Keith

    2017-02-01

    It is possible to use statistical methods to extract the propagation source wavelet phase from seismic data without getting information from a well log. Using kurtosis as a high-order statistics can preserve the phase of the signal but it is highly sensitive to outliers. A new method is introduced here called the multi-taper method coherence estimation. Two steps are required: first, a cosine function that includes the dominant frequency and maximum amplitude of signal is chosen. Secondly, the maximum coherence in the frequency band of the signal, which shows the best phase matching between the time series is determined. To validate this new method real data sets were chosen and the extracted wavelet phases for noise free and noisy data sets were compared with data extracted from a well log. Extracted wavelets using Kurtosis were also generated for comparison, and demonstrate the improved results using the new method.

  13. Wavelet-based LASSO in functional linear regression.

    Science.gov (United States)

    Zhao, Yihong; Ogden, R Todd; Reiss, Philip T

    2012-07-01

    In linear regression with functional predictors and scalar responses, it may be advantageous, particularly if the function is thought to contain features at many scales, to restrict the coefficient function to the span of a wavelet basis, thereby converting the problem into one of variable selection. If the coefficient function is sparsely represented in the wavelet domain, we may employ the well-known LASSO to select a relatively small number of nonzero wavelet coefficients. This is a natural approach to take but to date, the properties of such an estimator have not been studied. In this paper we describe the wavelet-based LASSO approach to regressing scalars on functions and investigate both its asymptotic convergence and its finite-sample performance through both simulation and real-data application. We compare the performance of this approach with existing methods and find that the wavelet-based LASSO performs relatively well, particularly when the true coefficient function is spiky. Source code to implement the method and data sets used in the study are provided as supplemental materials available online.

  14. 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.

  15. 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.

  16. Implementing a global DEM database on the sphere based on spherical wavelets

    Science.gov (United States)

    Zhao, Di; Zhao, Xuesheng; Shan, Shigang; Yao, Liangjun

    2010-11-01

    Wavelets have been proven to be an exceedingly powerful and highly efficient tool for fast computational algorithms in the fields of image data analysis and compression. Traditionally, the classical constructed wavelets are often employed to Euclidean infinite domains (such as the real line R and plane R2). In this paper, a spherical wavelet constructed for discrete DEM data based on the sphere is approached. Firstly, the discrete biorthogonal spherical wavelet with custom properties is constructed with the lifting scheme based on wavelet toolbox in Matlab. Then, the decomposition and reconstruction algorithms are proposed for efficient computation and the related wavelet coefficients are obtained. Finally, different precise images are displayed and analyzed at the different percentage of wavelet coefficients. The efficiency of this spherical wavelet algorithm is tested by using the GTOPO30 DEM data and the results show that at the same precision, the spherical wavelet algorithm consumes smaller storage volume. The results are good and acceptable.

  17. 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.

  18. Natural frequencies and damping estimation based on continuous wavelet transform

    Institute of Scientific and Technical Information of China (English)

    DAI Yu; SUN He-yi; LI Hui-peng; TANG Wen-yan

    2008-01-01

    The continuous wavelet transform (CWT) based method was improved for estimating the natural fre-quencies and damping ratios of a structural system in this paper. The appropriate scale of CWT was selected by means of the least squares method to identify the systems with closely spaced modes. The important issues relat-ed to estimation accuracy such as mode separation and end effect, were also investigated. These issues were as-sociated with the parameter selection of wavelet function based on the fitting error of least squares. The efficien-cy of the method was confirmed by applying it to a simulated 3dof damped system with two close modes.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 基于混沌萤火虫优化的小波多模盲均衡算法%Wavelet Multi-modulus Blind Equalization Algorithm Based on Chaos Glowworm Optimization

    Institute of Scientific and Technical Information of China (English)

    高敏; 郭业才

    2014-01-01

    采用多模盲均衡算法(MMA)处理高阶正交振幅调制 QAM 信号时,存在收敛速度慢、稳态误差大、容易陷入局部最优等问题。为此,提出一种基于混沌萤火虫优化的正交小波多模盲均衡算法(CGSO-WT-MMA)。该算法将具有良好全局搜索能力的萤火虫算法和具有较强局部搜索能力的混沌算法陒结合,用以优化均衡器权向量,并引入正交小波变换降低信号自陒关性,以改善收敛性能。仿真实验结果表明,与MMA算法陒比,该算法均方误差降低近4 dB,收敛速度加快近5000步,稳态性能明显提高。%Multi-Modulus Algorithm(MMA) used to equalize high-order Quadrature Amplitude Modulation(QAM) has many disadvantages, such as slow convergence rate, large mean square error, and easily immerging in partial minimum. In order to overcome the problems, orthogonal Wavelet Transform Multi-modulus blind Equalization Algorithm based on Optimization of Chaos Glowworm Swarm Optimization(CGSO-WT-MMA) is proposed. In the proposed algorithm, MMA is integrated with CGSO and WT, the de-correlation ability of WT is used to reduce the signal autocorrelation, and the global search ability of GSO algorithm integrating with the local search ability of chaos algorithm is used to optimize the equalizer weight vector. Simulation experimental results show that compared with MMA algorithm, mean square error of the algorithm decreases 4 dB, convergence rate speeds up 5 000 step, and its steady state performance has obvious improvement.

  5. 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.

  6. Performance Optimization of Discrete Wavelets Transform Based Image Watermarking Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    A. Al-Haj

    2008-01-01

    Full Text Available The excellent spatial localization, frequency spread and multi-resolution characteristics of the Discrete Wavelets Transform (DWT, which were similar to the theoretical models of the human visual system, facilitated the development of many imperceptible and robust DWT-based watermarking algorithms. There had been extremely few proposed algorithms on optimized DWT-based image watermarking that can simultaneously provide perceptual transparency and robustness since these two watermarking requirements are conflicting, in this study we treat the DWT-based image watermarking problem as an optimization problem and solve it using genetic algorithms. We demonstrate through the experimental results we obtained that optimal DWT-based image watermarking can be achieved only if watermarking has been applied at specific wavelet sub-bands and by using specific watermark-amplification values.

  7. 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.

  8. 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.

  9. Adaptive Dual-Threshold Edge Detection Based on Wavelet Transform

    Institute of Scientific and Technical Information of China (English)

    侯舒娟; 梅文博; 张志明

    2003-01-01

    In order to solve the problems of local-maximum modulus extraction and threshold selection in the edge detection of finite-resolution digital images, a new wavelet transform based adaptive dual-threshold edge detection algorithm is proposed. The local-maximum modulus is extracted by linear interpolation in wavelet domain. With the analysis on histogram, the image is filtered with an adaptive dual-threshold method, which effectively detects the contours of small structures as well as the boundaries of large objects. A wavelet domain's propagation function is used to further select weak edges. Experimental results have shown the self-adaptivity of the threshold to images having the same kind of histogram, and the efficiency even in noise-tampered images.

  10. 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.

  11. 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.

  12. Wavelet based hierarchical coding scheme for radar image compression

    Science.gov (United States)

    Sheng, Wen; Jiao, Xiaoli; He, Jifeng

    2007-12-01

    This paper presents a wavelet based hierarchical coding scheme for radar image compression. Radar signal is firstly quantized to digital signal, and reorganized as raster-scanned image according to radar's repeated period frequency. After reorganization, the reformed image is decomposed to image blocks with different frequency band by 2-D wavelet transformation, each block is quantized and coded by the Huffman coding scheme. A demonstrating system is developed, showing that under the requirement of real time processing, the compression ratio can be very high, while with no significant loss of target signal in restored radar image.

  13. Object-based wavelet compression using coefficient selection

    Science.gov (United States)

    Zhao, Lifeng; Kassim, Ashraf A.

    1998-12-01

    In this paper, we present a novel approach to code image regions of arbitrary shapes. The proposed algorithm combines a coefficient selection scheme with traditional wavelet compression for coding arbitrary regions and uses a shape adaptive embedded zerotree wavelet coding (SA-EZW) to quantize the selected coefficients. Since the shape information is implicitly encoded by the SA-EZW, our decoder can reconstruct the arbitrary region without separate shape coding. This makes the algorithm simple to implement and avoids the problem of contour coding. Our algorithm also provides a sufficient framework to address content-based scalability and improved coding efficiency as described by MPEG-4.

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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...

  19. 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...

  20. 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.

  1. Structure Data Processing and Damage Identification Based on Wavelet and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Zhanfeng Gao

    2011-10-01

    Full Text Available Structural health monitoring is a multi-disciplinary integrated technology, mainly including signal processing and structural damage detection. The aim of the data processing is to obtain the useful information from large volumes of raw data containing noises. In order to obtain the useful information concerned, denoising method and feature extraction technique based on Wavelet analysis is studied. An improved wavelet thresholding algorithm to eliminate the noise for vibration signals is proposed. The results of analysis show that the method based on Wavelet is not only feasible to signal de-noising, but also valuable and effective to detect the health status of bridge structure. In order to detect the damage status of the structure, a multi-layer neural network models based on the BP algorithm is designed. The model is trained with the data from an engineering beam to filter different transfer function, train function and the unit number of hidden layer by contrast to determine the best network model for damage detection. At last, the model is used to detect the damage of cable-stayed bridge with an improved method of data pre-processing using the square rate of change in frequency as input date of network. The structural damage identification results show that the BP neural network model is easy to identify the damage by the changing of vibration modal frequency and effective to reflect the injury status of the existing structure.

  2. Implementation of Wavelet-Based Neural Network for the detection of Very Low Frequency (VLF) Whistlers Transients

    Science.gov (United States)

    Sondhiya, Deepak Kumar; Gwal, Ashok Kumar; Verma, Shivali; Kasde, Satish Kumar

    Abstract: In this paper, a wavelet-based neural network system for the detection and identification of four types of VLF whistler’s transients (i.e. dispersive, diffuse, spiky and multipath) is implemented and tested. The discrete wavelet transform (DWT) technique is integrated with the feed forward neural network (FFNN) model to construct the identifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parseval’s theorem are employed to extract the characteristics features of the transients at different resolution levels. Second, the FFNN identifies these extracted features to identify the transients according to the features extracted. The proposed methodology can reduce a great quantity of the features of transients without losing its original property; less memory space and computing time are required. Various transient events are tested; the results show that the identifier can detect whistler transients efficiently. Keywords: Discrete wavelets transform, Multi-resolution analysis, Parseval’s theorem and Feed forward neural network

  3. 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

  4. Data-adaptive wavelets and multi-scale singular-spectrum analysis

    Science.gov (United States)

    Yiou, Pascal; Sornette, Didier; Ghil, Michael

    2000-08-01

    Using multi-scale ideas from wavelet analysis, we extend singular-spectrum analysis (SSA) to the study of nonstationary time series, including the case where intermittency gives rise to the divergence of their variance. The wavelet transform resembles a local Fourier transform within a finite moving window whose width W, proportional to the major period of interest, is varied to explore a broad range of such periods. SSA, on the other hand, relies on the construction of the lag-correlation matrix C on M lagged copies of the time series over a fixed window width W to detect the regular part of the variability in that window in terms of the minimal number of oscillatory components; here W= MΔ t with Δ t as the time step. The proposed multi-scale SSA is a local SSA analysis within a moving window of width M≤ W≤ N, where N is the length of the time series. Multi-scale SSA varies W, while keeping a fixed W/ M ratio, and uses the eigenvectors of the corresponding lag-correlation matrix C(M) as data-adaptive wavelets; successive eigenvectors of C(M) correspond approximately to successive derivatives of the first mother wavelet in standard wavelet analysis. Multi-scale SSA thus solves objectively the delicate problem of optimizing the analyzing wavelet in the time-frequency domain by a suitable localization of the signal’s correlation matrix. We present several examples of application to synthetic signals with fractal or power-law behavior which mimic selected features of certain climatic or geophysical time series. The method is applied next to the monthly values of the Southern Oscillation Index (SOI) for 1933-1996; the SOI time series is widely believed to capture major features of the El Niño/Southern Oscillation (ENSO) in the Tropical Pacific. Our methodology highlights an abrupt periodicity shift in the SOI near 1960. This abrupt shift between 5 and 3 years supports the Devil’s staircase scenario for the ENSO phenomenon (preliminary results of this study

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. Assessing heart rate variability through wavelet-based statistical measures.

    Science.gov (United States)

    Wachowiak, Mark P; Hay, Dean C; Johnson, Michel J

    2016-10-01

    Because of its utility in the investigation and diagnosis of clinical abnormalities, heart rate variability (HRV) has been quantified with both time and frequency analysis tools. Recently, time-frequency methods, especially wavelet transforms, have been applied to HRV. In the current study, a complementary computational approach is proposed wherein continuous wavelet transforms are applied directly to ECG signals to quantify time-varying frequency changes in the lower bands. Such variations are compared for resting and lower body negative pressure (LBNP) conditions using statistical and information-theoretic measures, and compared with standard HRV metrics. The latter confirm the expected lower variability in the LBNP condition due to sympathetic nerve activity (e.g. RMSSD: p=0.023; SDSD: p=0.023; LF/HF: p=0.018). Conversely, using the standard Morlet wavelet and a new transform based on windowed complex sinusoids, wavelet analysis of the ECG within the observed range of heart rate (0.5-1.25Hz) exhibits significantly higher variability, as measured by frequency band roughness (Morlet CWT: p=0.041), entropy (Morlet CWT: p=0.001), and approximate entropy (Morlet CWT: p=0.004). Consequently, this paper proposes that, when used with well-established HRV approaches, time-frequency analysis of ECG can provide additional insights into the complex phenomenon of heart rate variability.

  14. Bayesian texture segmentation based on wavelet domain hidden markov tree and the SMAP rule

    Institute of Scientific and Technical Information of China (English)

    SUN Jun-xi; ZHANG Su; ZHAO Yong-ming; CHEN Ya-zhu

    2005-01-01

    According to the sequential maximum a posteriori probability (SMAP) rule, this paper proposes a novel multi-scale Bayesian texture segmentation algorithm based on the wavelet domain Hidden Markov Tree (HMT) model. In the proposed scheme, interscale label transition probability is directly defined and resoled by an EM algorithm. In order to smooth out the variations in the homogeneous regions, intrascale context information is considered. A Gaussian mixture model (GMM) in the redundant wavelet domain is also exploited to formulate the pixel-level statistical features of texture pattern so as to avoid the influence of the variance of pixel brightness. The performance of the proposed method is compared with the state-of-the-art HMTSeg method and evaluated by the experiment results.

  15. 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.

  16. A Quaternionic Wavelet Transform-based Approach for Object Recognition

    Directory of Open Access Journals (Sweden)

    R. Ahila Priyadharshini

    2014-07-01

    Full Text Available Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT and dual tree discrete wavelet transform (DTDWT. Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 350-357, DOI:http://dx.doi.org/10.14429/dsj.64.4503

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Topology in galaxy distributions: method for a multi-scale analysis. A use of the wavelet transform.

    Science.gov (United States)

    Escalera, E.; MacGillivray, H. T.

    1995-06-01

    We report the 2D analysis of distributions of galaxies in a search for structures on all scales, from groups up to superclusters (including the identification of voids), based on the use of the wavelet transform. The wavelet method is an objective, multi-scale technique which gives the position, dimension and probability for each individual feature (both structures and voids) detected. We are currently performing the analysis on data from the COSMOS/UKST Southern Sky Galaxy Catalogue. The subsample used in our investigation contains some 2.5x10^6^ galaxies in an area of ~140x45 degrees around the South Galactic Pole. This is the first search for multi-scale objects on such an extended database, allowing us to cover many related topics in present-day Cosmology: realisation of superclusters as large-scale entities in their own right (as opposed to being considered merely as regions of enhanced cluster numbers); improvement in the definition of clusters of galaxies with a new approach to their general behaviour (distribution, typical sizes, state of evolution, etc.); and the objective characterisation of voids, which is exclusive to the wavelet method. In the present paper, we demonstrate the power of the technique by applying it to a selected field covering approximately 3000deg^2^ in the Grus-Sculptor region. In this area, we find 7 large scale structures (of more than 5 degrees in extent) and 26 structures of smaller scales (cluster-sized down to 1 degree, or group-sized down to 0.5 degrees). Sixteen of these small scale aggregates are connected with the large scale structures while ten appear isolated in the field. All these features are significant, having high confidence levels for detection. Voids are also detected in this area, likewise with high significance levels.

  5. Representation of 1/f signal with wavelet bases

    Institute of Scientific and Technical Information of China (English)

    刘峰; 刘贵忠; 张茁生

    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.

  6. 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).

  7. Wavelet-Based Diffusion Approach for DTI Image Restoration

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xiang-fen; CHEN Wu-fan; TIAN Wei-feng; YE Hong

    2008-01-01

    The Rician noise introduced into the diffusion tensor images (DTIs) can bring serious impacts on tensor calculation and fiber tracking. To decrease the effects of the Rician noise, we propose to consider the wavelet-based diffusion method to denoise multichannel typed diffusion weighted (DW) images. The presented smoothing strategy, which utilizes anisotropic nonlinear diffusion in wavelet domain, successfully removes noise while preserving both texture and edges. To evaluate quantitatively the efficiency of the presented method in accounting for the Rician noise introduced into the DW images, the peak-to-peak signal-to-noise ratio (PSNR) and signal-to-mean squared error ratio (SMSE) metrics are adopted. Based on the synthetic and real data, we calculated the apparent diffusion coefficient (ADC) and tracked the fibers. We made comparisons between the presented model,the wave shrinkage and regularized nonlinear diffusion smoothing method. All the experiment results prove quantitatively and visually the better performance of the presented filter.

  8. 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.

  9. 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.

  10. Wavelet-based coding of ultraspectral sounder data

    Science.gov (United States)

    Garcia-Vilchez, Fernando; Serra-Sagrista, Joan; Auli-Llinas, Francesc

    2005-08-01

    In this paper we provide a study concerning the suitability of well-known image coding techniques originally devised for lossy compression of still natural images when applied to lossless compression of ultraspectral sounder data. We present here the experimental results of six wavelet-based widespread coding techniques, namely EZW, IC, SPIHT, JPEG2000, SPECK and CCSDS-IDC. Since the considered techniques are 2-dimensional (2D) in nature but the ultraspectral data are 3D, a pre-processing stage is applied to convert the two spatial dimensions into a single spatial dimension. All the wavelet-based techniques are competitive when compared either to the benchmark prediction-based methods for lossless compression, CALIC and JPEG-LS, or to two common compression utilities, GZIP and BZIP2. EZW, SPIHT, SPECK and CCSDS-IDC provide a very similar performance, while IC and JPEG2000 improve the compression factor when compared to the other wavelet-based methods. Nevertheless, they are not competitive when compared to a fast precomputed vector quantizer. The benefits of applying a pre-processing stage, the Bias Adjusted Reordering, prior to the coding process in order to further exploit the spectral and/or spatial correlation when 2D techniques are employed, are also presented.

  11. 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.

  12. Studies on filtered back-projection imaging reconstruction based on a modified wavelet threshold function

    Science.gov (United States)

    Wang, Zhengzi; Ren, Zhong; Liu, Guodong

    2016-10-01

    In this paper, the wavelet threshold denoising method was used into the filtered back-projection algorithm of imaging reconstruction. To overcome the drawbacks of the traditional soft- and hard-threshold functions, a modified wavelet threshold function was proposed. The modified wavelet threshold function has two threshold values and two variants. To verify the feasibility of the modified wavelet threshold function, the standard test experiments were performed by using the software platform of MATLAB. Experimental results show that the filtered back-projection reconstruction algorithm based on the modified wavelet threshold function has better reconstruction effect because of more flexible advantage.

  13. 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.

  14. Majorization-minimization algorithms for wavelet-based image restoration.

    Science.gov (United States)

    Figueiredo, Mário A T; Bioucas-Dias, José M; Nowak, Robert D

    2007-12-01

    Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.

  15. JND measurements and wavelet-based image coding

    Science.gov (United States)

    Shen, Day-Fann; Yan, Loon-Shan

    1998-06-01

    Two major issues in image coding are the effective incorporation of human visual system (HVS) properties and the effective objective measure for evaluating image quality (OQM). In this paper, we treat the two issues in an integrated fashion. We build a JND model based on the measurements of the JND (Just Noticeable Difference) property of HVS. We found that JND does not only depend on the background intensity but also a function of both spatial frequency and patten direction. Wavelet transform, due to its excellent simultaneous Time (space)/frequency resolution, is the best choice to apply the JND model. We mathematically derive an OQM called JND_PSNR that is based on the JND property and wavelet decomposed subbands. JND_PSNR is more consistent with human perception and is recommended as an alternative to the PSNR or SNR. With the JND_PSNR in mind, we proceed to propose a wavelet and JND based codec called JZW. JZW quantizes coefficients in each subband with proper step size according to the subband's importance to human perception. Many characteristics of JZW are discussed, its performance evaluated and compared with other famous algorithms such as EZW, SPIHT and TCCVQ. Our algorithm has 1 - 1.5 dB gain over SPIHT even when we use simple Huffman coding rather than the more efficient adaptive arithmetic coding.

  16. 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.

  17. Grating geophone signal processing based on wavelet transform

    Science.gov (United States)

    Li, Shuqing; Zhang, Huan; Tao, Zhifei

    2008-12-01

    Grating digital geophone is designed based on grating measurement technique benefiting averaging-error effect and wide dynamic range to improve weak signal detected precision. This paper introduced the principle of grating digital geophone and its post signal processing system. The signal acquisition circuit use Atmega 32 chip as core part and display the waveform on the Labwindows through the RS232 data link. Wavelet transform is adopted this paper to filter the grating digital geophone' output signal since the signal is unstable. This data processing method is compared with the FIR filter that widespread use in current domestic. The result indicates that the wavelet algorithm has more advantages and the SNR of seismic signal improve obviously.

  18. 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.

  19. Wavelet-based Image Enhancement Using Fourth Order PDE

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren

    2011-01-01

    The presence of noise interference signal may cause problems in signal and image analysis; hence signal and image de-noising is often used as a preprocessing stage in many signal processing applications. In this paper, a new method is presented for image de-noising based on fourth order partial...... 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...... indicate superiority of the proposed method over the existing waveletbased image de-noising, anisotropic diffusion, and wiener filtering techniques....

  20. A wavelet watermarking algorithm based on a tree structure

    Science.gov (United States)

    Guitart Pla, Oriol; Lin, Eugene T.; Delp, Edward J., III

    2004-06-01

    We describe a blind watermarking technique for digital images. Our technique constructs an image-dependent watermark in the discrete wavelet transform (DWT) domain and inserts the watermark in the most signifcant coefficients of the image. The watermarked coefficients are determined by using the hierarchical tree structure induced by the DWT, similar in concept to embedded zerotree wavelet (EZW) compression. If the watermarked image is attacked or manipulated such that the set of significant coefficients is changed, the tree structure allows the correlation-based watermark detector to recover synchronization. Our technique also uses a visual adaptive scheme to insert the watermark to minimize watermark perceptibility. The visual adaptive scheme also takes advantage of the tree structure. Finally, a template is inserted into the watermark to provide robustness against geometric attacks. The template detection uses the cross-ratio of four collinear points.

  1. 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.

  2. A Fractional Random Wavelet Transform Based Image Steganography

    Directory of Open Access Journals (Sweden)

    G.K. Rajini

    2015-04-01

    Full Text Available This study presents a novel technique for image steganography based on Fractional Random Wavelet Transform. This transform has all the features of wavelet transform with randomness and fractional order built into it. The randomness and fractional order in the algorithm brings in robustness and additional layers of security to steganography. The stegano image generated by this algorithm contains both cover image and hidden image and image degradation is not observed in it. The steganography strives for security and pay load capacity. The performance measures like PeakSignal to Noise Ratio (PSNR, Mean Square Error (MSE, Structural Similarity Index Measure (SSIM and Universal Image Quality Index (UIQI are computed. In this proposed algorithm, imperceptibility and robustness are verified and it can sustain geometric transformations like rotation, scaling and translation and is compared with some of the existing algorithms. The numerical results show the effectiveness of the proposed algorithm.

  3. 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.

  4. 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).

  5. Wavelet-based neural network analysis of internal carotid arterial Doppler signals.

    Science.gov (United States)

    Ubeyli, Elif Derya; Güler, Inan

    2006-06-01

    In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion.

  6. Export-led growth in Tunisia: A wavelet filtering based analysis

    Directory of Open Access Journals (Sweden)

    Hamrita Mohamed Essaied

    2013-10-01

    Full Text Available In this paper, we use a wavelet filtering based approach to study the econometric relationship between exports, imports, and economic growth for Tunisia, using quarterly data for the period 1961:1-2007:4. GDP is used as a proxy for economic growth. We explore the interactions between these primary macroeconomic inputs in a co-integrating framework. We also study the direction of causality between the three variables, based on the more robust Toda-Yamamoto modified Wald (MWALD test. The much-studied relationship between these three primary indicators of the economy is explored with the help of the wavelet multi-resolution filtering technique. Instead of an analysis at the original series level, as is usually done, we first decompose the variables using wavelet decomposition technique at various scales of resolution and obtain relationship among components of the decomposed series matched to its scale. The analysis reveals interesting aspects of the interrelationship among the three fundamental macroeconomic variables.

  7. Probing Tissue Multifractality Using Wavelet based Multifractal Detrended Fluctuation Analysis: Applications in Precancer Detection

    CERN Document Server

    Soni, Jalpa; Ghosh, Sayantan; Pradhan, Asima; Sengupta, Tapas K; Panigrahi, Prasanta K; Ghosh, Nirmalya

    2011-01-01

    The refractive index fluctuations in the connective tissue layer (stroma) of human cervical tissues having different grades of precancers (dysplasia) was quantified using a wavelet-based multifractal detrended fluctuation analysis model. The results show clear signature of multi-scale self-similarity in the index fluctuations of the tissues. Importantly, the refractive index fluctuations were found to be more anti-correlated at higher grades of precancers. Moreover, the strength of multifractality was also observed to be considerably weaker in higher grades of precancers. These results were further complemented by Fourier domain analysis of the spectral fluctuations.

  8. 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...

  9. Construction of compactly supported biorthogonal wavelet based on Human Visual System

    Science.gov (United States)

    Hu, Haiping; Hou, Weidong; Liu, Hong; Mo, Yu L.

    2000-11-01

    As an important analysis tool, wavelet transform has made a great development in image compression coding, since Daubechies constructed a kind of compact support orthogonal wavelet and Mallat presented a fast pyramid algorithm for wavelet decomposition and reconstruction. In order to raise the compression ratio and improve the visual quality of reconstruction, it becomes very important to find a wavelet basis that fits the human visual system (HVS). Marr wavelet, as it is known, is a kind of wavelet, so it is not suitable for implementation of image compression coding. In this paper, a new method is provided to construct a kind of compactly supported biorthogonal wavelet based on human visual system, we employ the genetic algorithm to construct compactly supported biorthogonal wavelet that can approximate the modulation transform function for HVS. The novel constructed wavelet is applied to image compression coding in our experiments. The experimental results indicate that the visual quality of reconstruction with the new kind of wavelet is equivalent to other compactly biorthogonal wavelets in the condition of the same bit rate. It has good performance of reconstruction, especially used in texture image compression coding.

  10. ECG signal compression by multi-iteration EZW coding for different wavelets and thresholds.

    Science.gov (United States)

    Tohumoglu, Gülay; Sezgin, K Erbil

    2007-02-01

    The modified embedded zero-tree wavelet (MEZW) compression algorithm for the one-dimensional signal was originally derived for image compression based on Shapiro's EZW algorithm. It is revealed that the proposed codec is significantly more efficient in compression and in computation than previously proposed ECG compression schemes. The coder also attains exact bit rate control and generates a bit stream progressive in quality or rate. The EZW and MEZW algorithms apply the chosen threshold values or the expressions in order to specify that the significant transformed coefficients are greatly significant. Thus, two different threshold definitions, namely percentage and dyadic thresholds, are used, and they are applied for different wavelet types in biorthogonal and orthogonal classes. In detail, the MEZW and EZW algorithms results are quantitatively compared in terms of the compression ratio (CR) and percentage root mean square difference (PRD). Experiments are carried out on the selected records from the MIT-BIH arrhythmia database and an original ECG signal. It is observed that the MEZW algorithm shows a clear advantage in the CR achieved for a given PRD over the traditional EZW, and it gives better results for the biorthogonal wavelets than the orthogonal wavelets.

  11. 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.

  12. 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.

  13. Wavelet-based embedded zerotree extension to color coding

    Science.gov (United States)

    Franques, Victoria T.

    1998-03-01

    Recently, a new image compression algorithm was developed which employs wavelet transform and a simple binary linear quantization scheme with an embedded coding technique to perform data compaction. This new family of coder, Embedded Zerotree Wavelet (EZW), provides a better compression performance than the current JPEG coding standard for low bit rates. Since EZW coding algorithm emerged, all of the published coding results related to this coding technique are on monochrome images. In this paper the author has enhanced the original coding algorithm to yield a better compression ratio, and has extended the wavelet-based zerotree coding to color images. Color imagery is often represented by several components, such as RGB, in which each component is generally processed separately. With color coding, each component could be compressed individually in the same manner as a monochrome image, therefore requiring a threefold increase in processing time. Most image coding standards employ de-correlated components, such as YIQ or Y, CB, CR and subsampling of the 'chroma' components, such coding technique is employed here. Results of the coding, including reconstructed images and coding performance, will be presented.

  14. 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.

  15. Redundant Discrete Wavelet Transform Based Super-Resolution Using Sub-Pixel Image Registration

    Science.gov (United States)

    2003-03-01

    AFIT/GE/ENG/03-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED SUPER-RESOLUTION USING SUB-PIXEL IMAGE REGISTRATION THESIS Daniel L. Ward Second...position of the United States Air Force, Department of Defense, or the United States Government. AFIT/GE/ENG/03-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED...O3-18 REDUNDANT DISCRETE WAVELET TRANSFORM BASED SUPER-RESOLUTION USING SUB-PIXEL IMAGE REGISTRATION THESIS Daniel Lee Ward, B.S.E.E. Second

  16. Wavelet transform-based methods for denoising of Coulter counter signals

    Science.gov (United States)

    Jagtiani, Ashish V.; Sawant, Rupesh; Carletta, Joan; Zhe, Jiang

    2008-06-01

    A process based on discrete wavelet transforms is developed for denoising and baseline correction of measured signals from Coulter counters. Given signals from a particular Coulter counting experiment, which detect passage of particles through a fluid-filled microchannel, the process uses a cross-validation procedure to pick appropriate parameters for signal denoising; these parameters include the choice of the particular wavelet, the number of levels of decomposition, the threshold value and the threshold strategy. The process is demonstrated on simulated and experimental single channel data obtained from a particular multi-channel Coulter counter processing. For these example experimental signals from 20 µm polymethacrylate and Cottonwood/Eastern Deltoid pollen particles and the simulated signals, denoising is aimed at removing Gaussian white noise, 60 Hz power line interference and low frequency baseline drift. The process can be easily adapted for other Coulter counters and other sources of noise. Overall, wavelets are presented as a tool to aid in accurate detection of particles in Coulter counters.

  17. Two Novel Space-Time Coding Techniques Designed for UWB MISO Systems Based on Wavelet Transform

    Science.gov (United States)

    Zaki, Amira Ibrahim; El-Khamy, Said E.

    2016-01-01

    In this paper two novel space-time coding multi-input single-output (STC MISO) schemes, designed especially for Ultra-Wideband (UWB) systems, are introduced. The proposed schemes are referred to as wavelet space-time coding (WSTC) schemes. The WSTC schemes are based on two types of multiplexing, spatial and wavelet domain multiplexing. In WSTC schemes, four symbols are transmitted on the same UWB transmission pulse with the same bandwidth, symbol duration, and number of transmitting antennas of the conventional STC MISO scheme. The used mother wavelet (MW) is selected to be highly correlated with transmitted pulse shape and such that the multiplexed signal has almost the same spectral characteristics as those of the original UWB pulse. The two WSTC techniques increase the data rate to four times that of the conventional STC. The first WSTC scheme increases the data rate with a simple combination process. The second scheme achieves the increase in the data rate with a less complex receiver and better performance than the first scheme due to the spatial diversity introduced by the structure of its transmitter and receiver. The two schemes use Rake receivers to collect the energy in the dense multipath channel components. The simulation results show that the proposed WSTC schemes have better performance than the conventional scheme in addition to increasing the data rate to four times that of the conventional STC scheme. PMID:27959939

  18. Fractal coding of wavelet image based on human vision contrast-masking effect

    Science.gov (United States)

    Wei, Hai; Shen, Lansun

    2000-06-01

    In this paper, a fractal-based compression approach of wavelet image is presented. The scheme tries to make full use of the sensitivity features of the human visual system. With the wavelet-based multi-resolution representation of image, detail vectors of each high frequency sub-image are constructed in accordance with its spatial orientation in order to grasp the edge information to which human observer is sensitive. Then a multi-level selection algorithm based on human vision's contrast masking effect is proposed to make the decision whether a detail vector is coded or not. Those vectors below the contrast threshold are discarded without introducing visual artifacts because of the ignorance of human vision. As for the redundancy of the retained vectors, different fractal- based methods are employed to decrease the correlation in single sub-image and between the different resolution sub- images with the same orientation. Experimental results suggest the efficiency of the proposed scheme. With the standard test image, our approach outperforms the EZW algorithm and the JPEG method.

  19. 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.

  20. Remotely sensed image compression based on wavelet transform

    Science.gov (United States)

    Kim, Seong W.; Lee, Heung K.; Kim, Kyung S.; Choi, Soon D.

    1995-01-01

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

  1. Multi-Antenna OFDM System Using Coded Wavelet with Weighted Beamforming

    Directory of Open Access Journals (Sweden)

    K. Anoh

    2014-04-01

    Full Text Available A major drawback in deploying beamforming scheme in orthogonal frequency division multiplexing (OFDM is to obtain the optimal weights that are associated with information beams. Two beam weighting methods, namely co-phasing and singular vector decomposition (SVD, are considered to maximize the signal beams for such beamforming scheme. Initially the system performance with and without interleaving is investigated using coded fast Fourier transform (FFT-OFDM and wavelet-based OFDM. The two beamforming schemes are applied to the wavelet-based OFDM as confirmed to perform better than the FFT-OFDM. It is found that the beam-weight by SVD improves the performance of the system by about 2dB at the expense of the co-phasing method. The capacity performances of the weighting methods are also compared and discussed.

  2. 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 ...

  3. Multi-Antenna OFDM System Using Coded Wavelet with Weighted Beamforming

    OpenAIRE

    K. Anoh; Asif, R.; R. Abd-Alhameed; Rodriguez, J.; J. M. Noras; S.M.R. Jones; Hussaini, A.S.

    2014-01-01

    A major drawback in deploying beamforming scheme in orthogonal frequency division multiplexing (OFDM) is to obtain the optimal weights that are associated with information beams. Two beam weighting methods, namely co-phasing and singular vector decomposition (SVD), are considered to maximize the signal beams for such beamforming scheme. Initially the system performance with and without interleaving is investigated using coded fast Fourier transform (FFT)-OFDM and wavelet-based OFDM. The two...

  4. Phase-preserving speckle reduction based on soft thresholding in quaternion wavelet domain

    Science.gov (United States)

    Liu, Yipeng; Jin, Jing; Wang, Qiang; Shen, Yi

    2012-10-01

    Speckle reduction is a difficult task for ultrasound image processing because of low resolution and contrast. As a novel tool of image analysis, quaternion wavelet (QW) has some superior properties compared to discrete wavelets, such as nearly shift-invariant wavelet coefficients and phase-based texture presentation. We aim to exploit the excellent performance of speckle reduction in quaternion wavelet domain based on the soft thresholding method. First, we exploit the characteristics of magnitude and phases in quaternion wavelet transform (QWT) to the denoising application, and find that the QWT phases of the images are little influenced by the noises. Then we model the QWT magnitude using the Rayleigh distribution, and derive the thresholding criterion. Furthermore, we conduct several experiments on synthetic speckle images and real ultrasound images. The performance of the proposed speckle reduction algorithm, using QWT with soft thresholding, demonstrates superiority to those using discrete wavelet transform and classical algorithms.

  5. Construction of Hilbert Transform Pairs of Wavelet Bases and Gabor-like Transforms

    CERN Document Server

    Chaudhury, Kunal Narayan

    2009-01-01

    We propose a novel method for constructing Hilbert transform (HT) pairs of wavelet bases based on a fundamental approximation-theoretic characterization of scaling functions--the B-spline factorization theorem. In particular, starting from well-localized scaling functions, we construct HT pairs of biorthogonal wavelet bases of L^2(R) by relating the corresponding wavelet filters via a discrete form of the continuous HT filter. As a concrete application of this methodology, we identify HT pairs of spline wavelets of a specific flavor, which are then combined to realize a family of complex wavelets that resemble the optimally-localized Gabor function for sufficiently large orders. Analytic wavelets, derived from the complexification of HT wavelet pairs, exhibit a one-sided spectrum. Based on the tensor-product of such analytic wavelets, and, in effect, by appropriately combining four separable biorthogonal wavelet bases of L^2(R^2), we then discuss a methodology for constructing 2D directional-selective complex...

  6. 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.

  7. Wavelet Transform of Super-Resolutions Based on Radar and Infrared Sensor Fusion

    Science.gov (United States)

    1998-05-01

    0I Q’UAL1 INwPO¶= I VI STATEMB r AApproved for public release; Distribution Unlimited NAVY CASE 77545 WAVELET TRANSFORM OF SUPER-RESOLUTIONS BASED ON...INVENTION It is, therefore, an object of the present invention to provide a structure and method for applying the forward and reverse Wavelet Transform (WT...invention, the noisy super- 10 resolution of infrared imaging is combined with the Wavelet transform for radar corner back-scattering size information

  8. 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.

  9. Solution of wave-like equation based on Haar wavelet

    Directory of Open Access Journals (Sweden)

    Naresh Berwal

    2012-11-01

    Full Text Available Wavelet transform and wavelet analysis are powerful mathematical tools for many problems. Wavelet also can be applied in numerical analysis. In this paper, we apply Haar wavelet method to solve wave-like equation with initial and boundary conditions known. The fundamental idea of Haar wavelet method is to convert the differential equations into a group of algebraic equations, which involves a finite number or variables. The results and graph show that the proposed way is quite reasonable when compared to exact solution.

  10. The Lifting Scheme Based on the Second Generation Wavelets

    Institute of Scientific and Technical Information of China (English)

    FENG Hui; GUO Lanying; XIAO Jinsheng

    2006-01-01

    The lifting scheme is a custom-design construction of Biorthogonal wavelets, a fast and efficient method to realize wavelet transform, which provides a wider range of application and efficiently reduces the computing time with its particular frame. This paper aims at introducing the second generation wavelets, begins with traditional Mallat algorithms, illustrates the lifting scheme and brings out the detail steps in the construction of Biorthogonal wavelets. Because of isolating the degrees of freedom remaining the biorthogonality relations, we can fully control over the lifting operators to design the wavelet for a particular application, such as increasing the number of the vanishing moments.

  11. Target Identification Using Harmonic Wavelet Based ISAR Imaging

    Science.gov (United States)

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

    2006-12-01

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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Denoising method of heart sound signals based on self-construct heart sound wavelet

    Science.gov (United States)

    Cheng, Xiefeng; Zhang, Zheng

    2014-08-01

    In the field of heart sound signal denoising, the wavelet transform has become one of the most effective measures. The selective wavelet basis is based on the well-known orthogonal db series or biorthogonal bior series wavelet. In this paper we present a self-construct wavelet basis which is suitable for the heart sound denoising and analyze its constructor method and features in detail according to the characteristics of heart sound and evaluation criterion of signal denoising. The experimental results show that the heart sound wavelet can effectively filter out the noise of the heart sound signals, reserve the main characteristics of the signal. Compared with the traditional wavelets, it has a higher signal-to-noise ratio, lower mean square error and better denoising effect.

  18. Denoising method of heart sound signals based on self-construct heart sound wavelet

    Directory of Open Access Journals (Sweden)

    Xiefeng Cheng

    2014-08-01

    Full Text Available In the field of heart sound signal denoising, the wavelet transform has become one of the most effective measures. The selective wavelet basis is based on the well-known orthogonal db series or biorthogonal bior series wavelet. In this paper we present a self-construct wavelet basis which is suitable for the heart sound denoising and analyze its constructor method and features in detail according to the characteristics of heart sound and evaluation criterion of signal denoising. The experimental results show that the heart sound wavelet can effectively filter out the noise of the heart sound signals, reserve the main characteristics of the signal. Compared with the traditional wavelets, it has a higher signal-to-noise ratio, lower mean square error and better denoising effect.

  19. Performance Evaluation of Wavelet Based on Human Visual System

    Institute of Scientific and Technical Information of China (English)

    胡海平; 莫玉龙

    2002-01-01

    We have constructed a compactly supported biorthogonal wavelet that approximates the modulation transfer function(MTF) of human visual system in the frequency domain.In this paper,we evaluate performance of the constructed wavelet,and compare it with the widely used Daubechies9-7,Daubechies 9-3 and GBCW-9-7 wavelets.The result shows that coding performance of the constructed wavelet is better than Daubechies9-3,and is competitive with Daubechies 9-7 and GBCW-9-7 wavelets.Like Dauechies 9-3 wavelet,the filter coefficients of the constructed waveklet are all dyadic fractions,and the tap is less than Daubechies 9-7 and GBOW 9-7,It has an attractive feature in the realization of discrete wavelet transform.

  20. 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.

  1. Extracting fingerprint of wireless devices based on phase noise and multiple level wavelet decomposition

    Science.gov (United States)

    Zhao, Weichen; Sun, Zhuo; Kong, Song

    2016-10-01

    Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.

  2. Lossless image compression with projection-based and adaptive reversible integer wavelet transforms.

    Science.gov (United States)

    Deever, Aaron T; Hemami, Sheila S

    2003-01-01

    Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.

  3. 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.

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

    Science.gov (United States)

    Han, Guang; Wang, Jinkuan; Cai, Xi

    2016-03-30

    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.

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

    Science.gov (United States)

    Li, Jian; Zhang, Yunfeng; Zhu, Songye

    2008-02-01

    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.

  6. 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

  7. 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.

  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. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

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

    NARCIS (Netherlands)

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

    1999-01-01

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

  15. Controlling the Beam Halo-Chaos via Wavelet-Based Feedback Periodically

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In our recent work, worth mentioning in particular is the wavelet-based feedback controller, which works much better than the others for controlling the proton beam haio-chaos, where the master wavelet function isor, in a simplified form,and generalized form:(1)(2)(3)where a and b are scaling and translation constants, respectively. C is a selected constant. The

  16. An Adaptive Inpainting Algorithm Based on DCT Induced Wavelet Regularization

    Science.gov (United States)

    2013-01-01

    applications. We view the rows of a discrete cosine transform matrix as the filters associated with a multiresolution analysis. Non-decimated wavelet ...a redundant system which is formed by a set of transforms such as the discrete cosine transform, wavelets , framelets, and curvelets. The missing...vol. 93, pp. 273–299, 1965. [33] Q. Lian, L. Shen, Y. Xu, and L. Yang, “Filters of wavelets on invariant sets for image denoising ,” Applicable

  17. 基于多尺度2D Gab or小波的视网膜血管自动分割%Automatic Segmentation for Retinal Vessel Based on Multi-scale 2D Gabor Wavelet

    Institute of Scientific and Technical Information of China (English)

    王晓红; 赵于前; 廖苗; 邹北骥

    2015-01-01

    眼底视网膜血管分割对临床视网膜疾病诊断具有重要意义。由于视网膜血管结构微小,血管轮廓边界模糊,加上图像采集时噪声的影响,视网膜血管分割非常困难。本文提出一种视网膜血管自动分割新方法。首先,应用对比度受限的自适应直方图均衡法增强视网膜图像;然后,采用不同尺度的2D Gabor 小波对视网膜图像进行变换,并分别应用形态学重构(Morphological reconstruction, MR)和区域生长法(Region growing, RG)对变换后的图像进行分割;最后,对以上两种方法分割的视网膜血管和背景像素点重新标记识别,得到视网膜血管最终分割结果。通过对DRIVE和STARE数据库视网膜图像的分割实验,证明了该算法的有效性。%Segmentation of retinal vessels plays an important role in the diagnostic procedure of retinopathy. Due to the fact that the retinal vessels usually have some tiny structures and blurred boundaries, especially with remarkable noises resulted from retinal image acquisition, it is difficult to segment vessels from retinal images. In this paper, a new automatic segmentation method for retinal vessels is proposed. Firstly, the retinal vessel image is enhanced by the contrast-limited adaptive histogram equalization, and followed by multi-scale 2D Gabor wavelet transformation. Then, the use morphological reconstruction (MR) and region growing (RG) are used respectively to extract retinal vessels. Finally, both the segmented results are combined to achieve the final segmentation by reclassifying the vessel and background pixels. Experiments are conducted on the publicly available DRIVE and STARE databases, which show the effectiveness of the proposed method on retinal vessel segmentation.

  18. Wavelet-based Image Compression using Subband Threshold

    Science.gov (United States)

    Muzaffar, Tanzeem; Choi, Tae-Sun

    2002-11-01

    Wavelet based image compression has been a focus of research in recent days. In this paper, we propose a compression technique based on modification of original EZW coding. In this lossy technique, we try to discard less significant information in the image data in order to achieve further compression with minimal effect on output image quality. The algorithm calculates weight of each subband and finds the subband with minimum weight in every level. This minimum weight subband in each level, that contributes least effect during image reconstruction, undergoes a threshold process to eliminate low-valued data in it. Zerotree coding is done next on the resultant output for compression. Different values of threshold were applied during experiment to see the effect on compression ratio and reconstructed image quality. The proposed method results in further increase in compression ratio with negligible loss in image quality.

  19. 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.

  20. 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.

  1. Region-based image denoising through wavelet and fast discrete curvelet transform

    Science.gov (United States)

    Gu, Yanfeng; Guo, Yan; Liu, Xing; Zhang, Ye

    2008-10-01

    Image denoising always is one of important research topics in the image processing field. In this paper, fast discrete curvelet transform (FDCT) and undecimated wavelet transform (UDWT) are proposed for image denoising. A noisy image is first denoised by FDCT and UDWT separately. The whole image space is then divided into edge region and non-edge regions. After that, wavelet transform is performed on the images denoised by FDCT and UDWT respectively. Finally, the resultant image is fused through using both of edge region wavelet cofficients of the image denoised by FDCT and non-edge region wavelet cofficients of the image denoised by UDWT. The proposed method is validated through numerical experiments conducted on standard test images. The experimental results show that the proposed algorithm outperforms wavelet-based and curvelet-based image denoising methods and preserve linear features well.

  2. 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.

  3. Wavelet and wavelet packet compression of electrocardiograms.

    Science.gov (United States)

    Hilton, M L

    1997-05-01

    Wavelets and wavelet packets have recently emerged as powerful tools for signal compression. Wavelet and wavelet packet-based compression algorithms based on embedded zerotree wavelet (EZW) coding are developed for electrocardiogram (ECG) signals, and eight different wavelets are evaluated for their ability to compress Holter ECG data. Pilot data from a blind evaluation of compressed ECG's by cardiologists suggest that the clinically useful information present in original ECG signals is preserved by 8:1 compression, and in most cases 16:1 compressed ECG's are clinically useful.

  4. 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.

  5. 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.

  6. 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.

  7. Optimization of integer wavelet transforms based on difference correlation structures.

    Science.gov (United States)

    Li, Hongliang; Liu, Guizhong; Zhang, Zhongwei

    2005-11-01

    In this paper, a novel lifting integer wavelet transform based on difference correlation structure (DCCS-LIWT) is proposed. First, we establish a relationship between the performance of a linear predictor and the difference correlations of an image. The obtained results provide a theoretical foundation for the following construction of the optimal lifting filters. Then, the optimal prediction lifting coefficients in the sense of least-square prediction error are derived. DCCS-LIWT puts heavy emphasis on image inherent dependence. A distinct feature of this method is the use of the variance-normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources. The proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations. Experimental evaluation shows that the proposed method produces better results than the other well-known integer transforms for the lossless image compression.

  8. 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.

  9. 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.

  10. 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.

  11. 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 ...

  12. 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.

  13. 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.

  14. Dose calculation using a numerical method based on Haar wavelets integration

    Energy Technology Data Exchange (ETDEWEB)

    Belkadhi, K., E-mail: khaled.belkadhi@ult-tunisie.com [Unité de Recherche de Physique Nucléaire et des Hautes Énergies, Faculté des Sciences de Tunis, Université Tunis El-Manar (Tunisia); Manai, K. [Unité de Recherche de Physique Nucléaire et des Hautes Énergies, Faculté des Sciences de Tunis, Université Tunis El-Manar (Tunisia); College of Science and Arts, University of Bisha, Bisha (Saudi Arabia)

    2016-03-11

    This paper deals with the calculation of the absorbed dose in an irradiation cell of gamma rays. Direct measurement and simulation have shown that they are expensive and time consuming. An alternative to these two operations is numerical methods, a quick and efficient way can furnish an estimation of the absorbed dose by giving an approximation of the photon flux at a specific point of space. To validate the numerical integration method based on the Haar wavelet for absorbed dose estimation, a study with many configurations was performed. The obtained results with the Haar wavelet method showed a very good agreement with the simulation highlighting good efficacy and acceptable accuracy. - Highlights: • A numerical integration method using Haar wavelets is detailed. • Absorbed dose is estimated with Haar wavelets method. • Calculated absorbed dose using Haar wavelets and Monte Carlo simulation using Geant4 are compared.

  15. 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.

  16. 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.

  17. Research of Signal De-noising Technique Based on Wavelet

    Directory of Open Access Journals (Sweden)

    Shigang Hu

    2013-09-01

    Full Text Available During the process of signal testing, often exposed to interference and influence of all kinds of noise signal, such as data collection and transmission and so may introduce noise. So in practical applications, before analysis of the data measured, the need for de-noising processing. The signal de-noising is a method for filtering the high frequency  noise of the signal and makes the signal more precise. This paper deals with the general theory of wavelet transform, the application of wavelet transform in signal de-noising as well as the analysis of the characteristics of noise-polluted signa1. Matlab is used to be carried out the simu1ation where the different wavelet and different threshold of the same wavelet for signal de-noising are applied. An indicator of wavelet de-noising is presented , it is the indicator of smoothness. Through analysis of the experiment , considered MSE , SNR and smoothness , it can be a good way to evaluate the equality of wavelet de-noising. The results show that the wavelet transform can achieve excellent results in signal de-noising.  

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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...

  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. 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.

  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. Music Tune Restoration Based on a Mother Wavelet Construction

    Science.gov (United States)

    Fadeev, A. S.; Konovalov, V. I.; Butakova, T. I.; Sobetsky, A. V.

    2017-01-01

    It is offered to use the mother wavelet function obtained from the local part of an analyzed music signal. Requirements for the constructed function are proposed and the implementation technique and its properties are described. The suggested approach allows construction of mother wavelet families with specified identifying properties. Consequently, this makes possible to identify the basic signal variations of complex music signals including local time-frequency characteristics of the basic one.

  8. 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.

  9. A method of image compression based on lifting wavelet transform and modified SPIHT

    Science.gov (United States)

    Lv, Shiliang; Wang, Xiaoqian; Liu, Jinguo

    2016-11-01

    In order to improve the efficiency of remote sensing image data storage and transmission we present a method of the image compression based on lifting scheme and modified SPIHT(set partitioning in hierarchical trees) by the design of FPGA program, which realized to improve SPIHT and enhance the wavelet transform image compression. The lifting Discrete Wavelet Transform (DWT) architecture has been selected for exploiting the correlation among the image pixels. In addition, we provide a study on what storage elements are required for the wavelet coefficients. We present lena's image using the 3/5 lifting scheme.

  10. 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.

  11. 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).``

  12. 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.

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

    Science.gov (United States)

    Rangel, T.; Caliste, D.; Genovese, L.; Torrent, M.

    2016-11-01

    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.

  14. Adaptive Bayesian-based speck-reduction in SAR images using complex wavelet transform

    Science.gov (United States)

    Ma, Ning; Yan, Wei; Zhang, Peng

    2005-10-01

    In this paper, an improved adaptive speckle reduction method is presented based on dual tree complex wavelet transform (CWT). It combines the characteristics of additive noise reduction of soft thresholding with the CWT's directional selectivity, being its main contribution to adapt the effective threshold to preserve the edge detail. A Bayesian estimator is applied to the decomposed data also to estimate the best value for the noise-free complex wavelet coefficients. This estimation is based on alpha-stable and Gaussian distribution hypotheses for complex wavelet coefficients of the signal and noise, respectively. Experimental results show that the denoising performance is among the state-of-the-art techniques based on real discrete wavelet transform (DWT).

  15. Image Denoising of Wavelet based Compressed Images Corrupted by Additive White Gaussian Noise

    Directory of Open Access Journals (Sweden)

    Shyam Lal

    2012-08-01

    Full Text Available In this study an efficient algorithm is proposed for removal of additive white Gaussian noise from compressed natural images in wavelet based domain. First, the natural image is compressed by discrete wavelet transform and then proposed hybrid filter is applied for image denoising of compressed images corrupted by Additive White Gaussian Noise (AWGN. The proposed hybrid filter (HMCD is combination of non-linear fourth order partial differential equation and bivariate shrinkage function. The proposed hybrid filter provides better results in term of noise suppression with keeping minimum edge blurring as compared to other existing image denoising techniques for wavelet based compressed images. Simulation and experimental results on benchmark test images demonstrate that the proposed hybrid filter attains competitive image denoising performances as compared with other state-of-the-art image denoising algorithms. It is more effective particularly for the highly corrupted images in wavelet based compressed domain.

  16. 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.

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

    Institute of Scientific and Technical Information of China (English)

    CEN Wei; YANG ShiFeng; XUE Rong; XU RiWei; YU DingSheng

    2007-01-01

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

  18. 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.

  19. Property study of integer wavelet transform lossless compression coding based on lifting scheme

    Science.gov (United States)

    Xie, Cheng Jun; Yan, Su; Xiang, Yang

    2006-01-01

    In this paper the algorithms and its improvement of integer wavelet transform combining SPIHT and arithmetic coding in image lossless compression is mainly studied. The experimental result shows that if the order of low-pass filter vanish matrix is fixed, the improvement of compression effect is not evident when invertible integer wavelet transform is satisfied and focusing of energy property monotonic increase with transform scale. For the same wavelet bases, the order of low-pass filter vanish matrix is more important than the order of high-pass filter vanish matrix in improving the property of image compression. Integer wavelet transform lossless compression coding based on lifting scheme has no relation to the entropy of image. The effect of compression is depended on the the focuing of energy property of image transform.

  20. All-optical image processing and compression based on Haar wavelet transform.

    Science.gov (United States)

    Parca, Giorgia; Teixeira, Pedro; Teixeira, Antonio

    2013-04-20

    Fast data processing and compression methods based on wavelet transform are fundamental tools in the area of real-time 2D data/image analysis, enabling high definition applications and redundant data reduction. The need for information processing at high data rates motivates the efforts on exploiting the speed and the parallelism of the light for data analysis and compression. Among several schemes for optical wavelet transform implementation, the Haar transform offers simple design and fast computation, plus it can be easily implemented by optical planar interferometry. We present an all optical scheme based on an asymmetric couplers network for achieving fast image processing and compression in the optical domain. The implementation of Haar wavelet transform through a 3D passive structure is supported by theoretical formulation and simulations results. Asymmetrical coupler 3D network design and optimization are reported and Haar wavelet transform, including compression, was achieved, thus demonstrating the feasibility of our approach.

  1. Dual tree complex wavelet transform based denoising of optical microscopy images.

    Science.gov (United States)

    Bal, Ufuk

    2012-12-01

    Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions.

  2. Wavelet-based integral representation for solutions of the wave equation

    Energy Technology Data Exchange (ETDEWEB)

    Perel, Maria V; Sidorenko, Mikhail S [Department of Mathematical Physics, Physics Faculty, St Petersburg University, Ulyanovskaya 1-1, Petrodvorets, St Petersburg 198904 (Russian Federation)], E-mail: perel@mph.phys.spbu.ru, E-mail: M-Sidorenko@yandex.ru

    2009-09-18

    An integral representation of solutions of the wave equation as a superposition of other solutions of this equation is built. The solutions from a wide class can be used as building blocks for the representation. Considerations are based on mathematical techniques of continuous wavelet analysis. The formulae obtained are justified from the point of view of distribution theory. A comparison of the results with those by G Kaiser is carried out. Methods of obtaining physical wavelets are discussed.

  3. Super-resolution image restoration algorithms based on orthogonal discrete wavelet transform

    Science.gov (United States)

    Liu, Yang-yang; Jin, Wei-qi

    2005-02-01

    Several new super-resolution image restoration algorithms based on orthogonal discrete wavelet transform are proposed, by using orthogonal discrete wavelet transform and generalized cross validation ,and combining with Luck-Richardson super-resolution image restoration algorithm (LR) and Luck-Richardson algorithm based on Poisson-Markov model (MPML). Orthogonal discrete wavelet transform analyzed in both space and frequency domain has the capability of indicating local features of a signal, and concentrating the signal power to a few coefficients in wavelet transform domain. After an original image is "Symlets" orthogonal discrete wavelet transformed, an asymptotically optimal threshold is determined by minimizing generalized cross validation, and high frequency subbands in each decomposition level are denoised with soft threshold processes to converge respectively to those with maximum signal-noise-ratio, when the method is incorporated with existed super-resolution image algorithms, details of original image, especially of those with low signal-noise-ratio, could be well recovered. Single operation wavelet LR algorithm(SWLR),single operation wavelet MPML algorithm(SW-MPML) and MPML algorithm based on single operation and wavelet transform (MPML- SW) are some operative algorithms proposed based on the method. According to the processing results to simulating and practical images , because of the only one operation, under the guarantee of rapid and effective restoration processing, in comparison with LR and MPML, all the proposed algorithms could retain image details better, and be more suitable to low signal-noise-ratio images, They could also reduce operation time for up to hundreds times of iteratives, as well as, avoid the iterative operation of self-adaptive parameters in MPML, improve operating speed and precision. They are practical and instantaneous to some extent in the field of low signal-noise-ratio image restoration.

  4. Image Compression Using Wavelet Transform Based on the Lifting Scheme and its Implementation

    Directory of Open Access Journals (Sweden)

    A Alice Blessie

    2011-05-01

    Full Text Available This paper presents image compression using 9/7 wavelet transform based on the lifting scheme. This is simulated using ISE simulator and implemented in FPGA. The 9/7 wavelet transform performs well for the low frequency components. Implementation in FPGA is since because of its partial reconfiguration. The project mainly aims at retrieving the smooth images without any loss. This design may be used for both lossy and lossless compression.

  5. Model-free stochastic processes studied with q-wavelet-based informational tools

    Energy Technology Data Exchange (ETDEWEB)

    Perez, D.G. [Instituto de Fisica, Pontificia Universidad Catolica de Valparaiso (PUCV), 23-40025 Valparaiso (Chile)]. E-mail: dario.perez@ucv.cl; Zunino, L. [Centro de Investigaciones Opticas, C.C. 124 Correo Central, 1900 La Plata (Argentina) and Departamento de Ciencias Basicas, Facultad de Ingenieria, Universidad Nacional de La Plata (UNLP), 1900 La Plata (Argentina) and Departamento de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, 1900 La Plata (Argentina)]. E-mail: lucianoz@ciop.unlp.edu.ar; Martin, M.T. [Instituto de Fisica (IFLP), Facultad de Ciencias Exactas, Universidad Nacional de La Plata and Argentina' s National Council (CONICET), C.C. 727, 1900 La Plata (Argentina)]. E-mail: mtmartin@venus.unlp.edu.ar; Garavaglia, M. [Centro de Investigaciones Opticas, C.C. 124 Correo Central, 1900 La Plata (Argentina) and Departamento de Fisica, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, 1900 La Plata (Argentina)]. E-mail: garavagliam@ciop.unlp.edu.ar; Plastino, A. [Instituto de Fisica (IFLP), Facultad de Ciencias Exactas, Universidad Nacional de La Plata and Argentina' s National Council (CONICET), C.C. 727, 1900 La Plata (Argentina)]. E-mail: plastino@venus.unlp.edu.ar; Rosso, O.A. [Chaos and Biology Group, Instituto de Calculo, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Pabellon II, Ciudad Universitaria, 1428 Ciudad de Buenos Aires (Argentina)]. E-mail: oarosso@fibertel.com.ar

    2007-04-30

    We undertake a model-free investigation of stochastic processes employing q-wavelet based quantifiers, that constitute a generalization of their Shannon counterparts. It is shown that (i) interesting physical information becomes accessible in such a way (ii) for special q values the quantifiers are more sensitive than the Shannon ones and (iii) there exist an implicit relationship between the Hurst parameter H and q within this wavelet framework.

  6. 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.

  7. The collision-free trajectory planning for the space robot to capture a target based on the wavelet interpolation algorithm

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In the research of path planning for manipulators with many DOF, generally there is a problem in most traditional methods, which is that their computational cost (time and memory space) increases exponentially as DOF or resolution of the discrete configuration space increases. So this paper presents the collision-free trajectory planning for the space robot to capture a target based on the wavelet interpolation algorithm. We made wavelet sample on the desired trajectory of the manipulator' s end-effector to do trajectory planning by use of the proposed wavelet interpolation formula, and then derived joint vectors from the trajectory information of the endeffector based on the fixed-attitude-restrained generalized Jacobian matrix of multi-arm coordinated motion, so as to control the manipulator to capture a static body along the desired collision-free trajectory. The method overcomes the shortcomings of the typical methods, and the desired trajectory of the end-effector can be any kind of complex nonlinear curve. The algorithm is simple and highly effective and the real trajectory is close to the desired trajectory. In simulation, the planar dual-arm three DOF space robot is used to demonstrate the proposed method, and it shows that the algorithm is feasible.

  8. 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.

  9. The Boundary Processing of Wavelet Based Image Compression

    Institute of Scientific and Technical Information of China (English)

    Yu Sheng-sheng; He Xiao-cheng; Zhou Jing-li; Chen Jia-zhong

    2004-01-01

    When an image, which is decomposed by bi-orthogonal wavelet bases, is reconstructed, some information will be lost at the four edges of the image. At the same time,artificial discontinuities will be introduced. We use a method called symmetric extension to solve the problem. We only consider the case of the two-band filter banks, and the results can be applied to M-band filter banks. There are only two types of symmetric extension in analysis phrase, namely the whole-sample symmetry (WS), the half-sample symmetry (HS), while there are four types of symmetric extension in synthesis phrase, namely the WS, HS, the whole-sample an-ti-symmetry (WA), and the half-sample anti-symmetry (HA) respcctively. We can select the exact type according to the image length and the filter length, and we will show how to do these. The image can be perfectly reconstructed without any edge effects in this way. Finally, simulation results are reported.

  10. AN EFFICIENT HILBERT AND INTEGER WAVELET TRANSFORM BASED VIDEO WATERMARKING

    Directory of Open Access Journals (Sweden)

    AGILANDEESWARI L.

    2016-03-01

    Full Text Available In this paper, an efficient, highly imperceptible, robust, and secure digital video watermarking technique for content authentication based on Hilbert transform in the Integer Wavelet Transform (IWT domain has been introduced. The Hilbert coefficients of gray watermark image are embedded into the cover video frames Hilbert coefficients on the 2-level IWT decomposed selected block on sub-bands using Principal Component Analysis (PCA technique. The authentication is achieved by using the digital signature mechanism. This mechanism is used to generate and embed a digital signature after embedding the watermarks. Since, the embedding process is done in Hilbert transform domain, the imperceptibility and the robustness of the watermark is greatly improved. At the receiver end, prior to the extraction of watermark, the originality of the content is verified through the authentication test. If the generated and received signature matches, it proves that the received content is original and performs the extraction process, otherwise deny the extraction process due to unauthenticated received content. The proposed method avoids typical degradations in the imperceptibility level of watermarked video in terms of Average Peak Signal – to – Noise Ratio (PSNR value of about 48db, while it is still providing better robustness against common video distortions such as frame dropping, averaging, and various image processing attacks such as noise addition, median filtering, contrast adjustment, and geometrical attacks such as, rotation and cropping in terms of Normalized Correlation Coefficient (NCC value of about nearly 1.

  11. 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.

  12. Wavelets-based clustering of air quality monitoring sites.

    Science.gov (United States)

    Gouveia, Sónia; Scotto, Manuel G; Monteiro, Alexandra; Alonso, Andres M

    2015-11-01

    This paper aims at providing a variance/covariance profile of a set of 36 monitoring stations measuring ozone (O3) and nitrogen dioxide (NO2) hourly concentrations, collected over the period 2005-2013, in Portugal mainland. The resulting individual profiles are embedded in a wavelet decomposition-based clustering algorithm in order to identify groups of stations exhibiting similar profiles. The results of the cluster analysis identify three groups of stations, namely urban, suburban/urban/rural, and a third group containing all but one rural stations. The results clearly indicate a geographical pattern among urban stations, distinguishing those located in Lisbon area from those located in Oporto/North. Furthermore, for urban stations, intra-diurnal and daily time scales exhibit the highest variance. This is due to the more relevant chemical activity occurring in high NO2 emissions areas which are responsible for high variability on daily profiles. These chemical processes also explain the reason for NO2 and O3 being highly negatively cross-correlated in suburban and urban sites as compared with rural stations. Finally, the clustering analysis also identifies sites which need revision concerning classification according to environment/influence type.

  13. 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.

  14. 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.

  15. 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.)

  16. 基于多尺度小波变换和加权Fisher线性分类器的离线签名认证%An Off-line Signature Verification based on Adaptive Multi-resolution Wavelet and Weighted Fisher Linear Classifier

    Institute of Scientific and Technical Information of China (English)

    曾晓云

    2015-01-01

    采用多尺度小波变换来提取签名图像四个方向投影(水平、垂直、45度和135度)的相关特征。在签名认证阶段,采用加权Fisher线性判别( WFLD)来消除样本个数不平衡的影响,提高了签名认证的效率。%This paper presents an off-line signature verification method based on multi-scale wavelet transform and weighted Fish linear classifier. Firstly,the horizontal,vertical,45 degree direction and the 135 degree direction projections of signature images are calculated,respectively. In the signature verification stage,in order to eliminate the influence of unbalanced number of samples,we use the weighted Fisher linear discriminated analysis( WFLD), experimental results show that WFLD can effectively improve the efficiency of signature verification.

  17. Aircraft target identification based on 2D ISAR images using multiresolution analysis wavelet

    Science.gov (United States)

    Fu, Qiang; Xiao, Huaitie; Hu, Xiangjiang

    2001-09-01

    The formation of 2D ISAR images for radar target identification hold much promise for additional distinguish- ability between targets. Since an image contains important information is a wide range of scales, and this information is often independent from one scale to another, wavelet analysis provides a method of identifying the spatial frequency content of an image and the local regions within the image where those spatial frequencies exist. In this paper, a multiresolution analysis wavelet method based on 2D ISAR images was proposed for use in aircraft radar target identification under the wide band high range resolution radar background. The proposed method was performed in three steps; first, radar backscatter signals were processed in the form of 2D ISAR images, then, Mallat's wavelet algorithm was used in the decomposition of images, finally, a three layer perceptron neural net was used as classifier. The result of experiments demonstrated that the feasibility of using multiresolution analysis wavelet for target identification.

  18. 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.

  19. 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...

  20. Self-similar prior and wavelet bases for hidden incompressible turbulent motion

    CERN Document Server

    Héas, Patrick; Kadri-Harouna, Souleymane

    2013-01-01

    This work is concerned with the ill-posed inverse problem of estimating turbulent flows from the observation of an image sequence. From a Bayesian perspective, a divergence-free isotropic fractional Brownian motion (fBm) is chosen as a prior model for instantaneous turbulent velocity fields. This self-similar prior characterizes accurately second-order statistics of velocity fields in incompressible isotropic turbulence. Nevertheless, the associated maximum a posteriori involves a fractional Laplacian operator which is delicate to implement in practice. To deal with this issue, we propose to decompose the divergent-free fBm on well-chosen wavelet bases. As a first alternative, we propose to design wavelets as whitening filters. We show that these filters are fractional Laplacian wavelets composed with the Leray projector. As a second alternative, we use a divergence-free wavelet basis, which takes implicitly into account the incompressibility constraint arising from physics. Although the latter decomposition ...

  1. 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...

  2. [The noise filtering and baseline correction for harmonic spectrum based on wavelet transform].

    Science.gov (United States)

    Guo, Yuan; Zhao, Xue-Hong; Zhang, Rui; Hu, Ya-Jun; Wang, Yan

    2013-08-01

    The problem of noise and baseline drift is a hot topic in infrared spectral harmonic detection system. This paper presents a new algorithm based on wavelet transform Mallet decomposition to solve the problem of eliminating a variety of complex noise and baseline drift in the harmonic detection. In the algorithm, the appropriate wavelet function and decomposition level were selected to decomposed the noise, baseline drift and useful signal in the harmonic curve into different frequency bands. the bands' information was analysed and a detecting band was set, then the information in useful frequency was reserved by zeroing method of treatment and the coefficient of the threshold. We can just use once transform and reconstruction to remove interference noise and baseline from double-harmonic signal by applying the wavelet transform technique to the harmonic detection spectrum pretreatment. Experiments show that the wavelet transform method can be used to different harmonic detection systems and has universal applicability.

  3. 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.

  4. Evidence of self-organization in brain electrical activity using wavelet-based informational tools

    Science.gov (United States)

    Rosso, O. A.; Martin, M. T.; Plastino, A.

    2005-03-01

    In the present work, we show that appropriate information-theory tools based on the wavelet transform (relative wavelet energy; normalized total wavelet entropy, H; generalized wavelet complexity, CW), when applied to tonic-clonic epileptic EEG data, provide one with valuable insights into the dynamics of neural activity. Twenty tonic-clonic secondary generalized epileptic records pertaining to eight patients have been analyzed. If the electromyographic activity is excluded the difference between the ictal and pre-ictal mean entropic values (ΔH=-) is negative in 95% of the cases (pictal)>-ictal)>) is positive in 85% of the cases (p=0.0002). Thus during the seizure entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus in this kind of seizures triggers a self-organized brain state characterized by both order and maximal complexity.

  5. A wavelet-based quadtree driven stereo image coding

    Science.gov (United States)

    Bensalma, Rafik; Larabi, Mohamed-Chaker

    2009-02-01

    In this work, a new stereo image coding technique is proposed. The new approach integrates the coding of the residual image with the disparity map. The latter computed in the wavelet transform domain. The motivation behind using this transform is that it imitates some properties of the human visual system (HVS), particularly, the decomposition in the perspective canals. Therefore, using the wavelet transform allows for better perceptual image quality preservation. In order to estimate the disparity map, we used a quadtree segmentation in each wavelet frequency band. This segmentation has the advantage of minimizing the entropy. Dyadic squares in the subbands of target image that they are not matched with other in the reference image constitutes the residuals are coded by using an arithmetic codec. The obtained results are evaluated by using the SSIM and PSNR criteria.

  6. 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.

  7. SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

    Science.gov (United States)

    Yan, Jingwen; Chen, Jiazhen

    2007-03-01

    A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are introduced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ), the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.

  8. SFCVQ and EZW coding method based on Karhunen-Loeve transformation and integer wavelet transformation

    Institute of Scientific and Technical Information of China (English)

    Jingwen Yan; Jiazhen Chen

    2007-01-01

    A new hyperspectral image compression method of spectral feature classification vector quantization (SFCVQ) and embedded zero-tree of wavelet (EZW) based on Karhunen-Loeve transformation (KLT) and integer wavelet transformation is represented. In comparison with the other methods, this method not only keeps the characteristics of high compression ratio and easy real-time transmission, but also has the advantage of high computation speed. After lifting based integer wavelet and SFCVQ coding are introduced, a system of nearly lossless compression of hyperspectral images is designed. KLT is used to remove the correlation of spectral redundancy as one-dimensional (1D) linear transform, and SFCVQ coding is applied to enhance compression ratio. The two-dimensional (2D) integer wavelet transformation is adopted for the decorrelation of 2D spatial redundancy. EZW coding method is applied to compress data in wavelet domain. Experimental results show that in comparison with the method of wavelet SFCVQ (WSFCVQ),the method of improved BiBlock zero tree coding (IBBZTC) and the method of feature spectral vector quantization (FSVQ), the peak signal-to-noise ratio (PSNR) of this method can enhance over 9 dB, and the total compression performance is improved greatly.

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. On The Harmonics Reduction Using Wavelet Based Signal Processing

    Directory of Open Access Journals (Sweden)

    Marian GAICEANU

    2000-12-01

    Full Text Available This paper presents a method for calculating the reference currents needed for the command of an active filter. By using the Discrete Wavelet Transform (DWT the high-frequency components of the currents are eliminated. Also, a reference is delivered to the control block of the active filter and a comparison is made between the DWT and the classical Fourier transform.

  18. Piecewise Tensor Product Wavelet Bases by Extensions and Approximation Rates

    NARCIS (Netherlands)

    Chegini, N.G.; Dahlke, S.; Friedrich, U.; Stevenson, R.; Dahlke, S.; Dahmen, W.; Griebel, M.; Hackbusch, W.; Ritter, K.; Schneider, R.; Schwab, C.; Yserentant, H.

    2014-01-01

    DIn this chapter, we present some of the major results that have been achieved in the context of the DFG-SPP project "Adaptive Wavelet Frame Methods for Operator Equations: Sparse Grids, Vector-Valued Spaces and Applications to Nonlinear Inverse Problems". This project has been concerned with (nonli

  19. 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...

  20. 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.

  1. Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

    Science.gov (United States)

    Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

    2010-01-01

    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.

  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. 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.

  4. Mean square error approximation for wavelet-based semiregular mesh compression.

    Science.gov (United States)

    Payan, Frédéric; Antonini, Marc

    2006-01-01

    The objective of this paper is to propose an efficient model-based bit allocation process optimizing the performances of a wavelet coder for semiregular meshes. More precisely, this process should compute the best quantizers for the wavelet coefficient subbands that minimize the reconstructed mean square error for one specific target bitrate. In order to design a fast and low complex allocation process, we propose an approximation of the reconstructed mean square error relative to the coding of semiregular mesh geometry. This error is expressed directly from the quantization errors of each coefficient subband. For that purpose, we have to take into account the influence of the wavelet filters on the quantized coefficients. Furthermore, we propose a specific approximation for wavelet transforms based on lifting schemes. Experimentally, we show that, in comparison with a "naive" approximation (depending on the subband levels), using the proposed approximation as distortion criterion during the model-based allocation process improves the performances of a wavelet-based coder for any model, any bitrate, and any lifting scheme.

  5. 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.

  6. Multi-scale kinetic surface roughening of reactive-sputtered TaN thin films characterized by wavelet transform approach

    Energy Technology Data Exchange (ETDEWEB)

    Yang, J.J., E-mail: jjyang@scu.edu.cn; Miao, F.M.; Tang, J., E-mail: tangjun@scu.edu.cn; Yang, Y.Y.; Liao, J.L.; Liu, N.

    2014-01-01

    Kinetic surface roughening of TaN thin films deposited by reactive sputtering was investigated by using atomic force microscopy. Wavelet transform method incorporating power spectrum density analysis was applied to extract the global and local surface morphologies of the films. Then the dynamical exponents of global and local surface roughening were calculated in terms of dynamic scaling theory. The results show that the kinetic surface roughening of TaN thin films exhibits multi-scale characteristics, where a set of local dynamical exponents (α{sub l} = 0.95, β{sub l} = 0.24) and global dynamical exponents (α{sub g} = 1.56, β{sub g} = 0.71) was obtained. The local surface roughening is dominated by the competition between linear surface diffusion and deposition flux noise, while the global surface roughening displays anomalous rapid-roughening behavior due to the preferred grain growth. - Highlights: • Film surface multi-scale behaviors were characterized by wavelet transform. • Microscopic mechanisms of surface multi-scale behaviors were investigated.

  7. 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.

  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. 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.  

  10. 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.

  11. 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...

  12. A wavelet-based two-stage near-lossless coder.

    Science.gov (United States)

    Yea, Sehoon; Pearlman, William A

    2006-11-01

    In this paper, we present a two-stage near-lossless compression scheme. It belongs to the class of "lossy plus residual coding" and consists of a wavelet-based lossy layer followed by arithmetic coding of the quantized residual to guarantee a given L(infinity) error bound in the pixel domain. We focus on the selection of the optimum bit rate for the lossy layer to achieve the minimum total bit rate. Unlike other similar lossy plus lossless approaches using a wavelet-based lossy layer, the proposed method does not require iteration of decoding and inverse discrete wavelet transform in succession to locate the optimum bit rate. We propose a simple method to estimate the optimal bit rate, with a theoretical justification based on the critical rate argument from the rate-distortion theory and the independence of the residual error.

  13. A wavelet based approach to Solar-Terrestrial Coupling

    Science.gov (United States)

    Katsavrias, Ch.; Hillaris, A.; Preka-Papadema, P.

    2016-05-01

    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 wavelet-coherence (WTC) respectively. In time-scales of ≈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 during the cycle maximum the latter during periods of reduced solar activity. The phase relationship of these modulation is highly non-linear. Only the annual frequency component of the ICMEs is phase-locked with DST and AE. In time-scales of ≈1.3-1.7 years the CIR seem to be the dominant driver for both geomagnetic indices throughout the whole solar cycle 23.

  14. Wavelet-based texture image classification using vector quantization

    Science.gov (United States)

    Lam, Eric P.

    2007-02-01

    Classification of image segments on textures can be helpful for target recognition. Sometimes target cueing is performed before target recognition. Textures are sometimes used to cue an image processor of a potential region of interest. In certain imaging sensors, such as those used in synthetic aperture radar, textures may be abundant. The textures may be caused by the object material or speckle noise. Even speckle noise can create the illusion of texture, which must be compensated in image pre-processing. In this paper, we will discuss how to perform texture classification but constrain the number of wavelet packet node decomposition. The new approach performs a twochannel wavelet decomposition. Comparing the strength of each new subband with others at the same level of the wavelet packet determines when to stop further decomposition. This type of decomposition is performed recursively. Once the decompositions stop, the structure of the packet is stored in a data structure. Using the information from the data structure, dominating channels are extracted. These are defined as paths from the root of the packet to the leaf with the highest strengths. The list of dominating channels are used to train a learning vector quantization neural network.

  15. Wavelet-based pavement image compression and noise reduction

    Science.gov (United States)

    Zhou, Jian; Huang, Peisen S.; Chiang, Fu-Pen

    2005-08-01

    For any automated distress inspection system, typically a huge number of pavement images are collected. Use of an appropriate image compression algorithm can save disk space, reduce the saving time, increase the inspection distance, and increase the processing speed. In this research, a modified EZW (Embedded Zero-tree Wavelet) coding method, which is an improved version of the widely used EZW coding method, is proposed. This method, unlike the two-pass approach used in the original EZW method, uses only one pass to encode both the coordinates and magnitudes of wavelet coefficients. An adaptive arithmetic encoding method is also implemented to encode four symbols assigned by the modified EZW into binary bits. By applying a thresholding technique to terminate the coding process, the modified EZW coding method can compress the image and reduce noise simultaneously. The new method is much simpler and faster. Experimental results also show that the compression ratio was increased one and one-half times compared to the EZW coding method. The compressed and de-noised data can be used to reconstruct wavelet coefficients for off-line pavement image processing such as distress classification and quantification.

  16. Wavelet-based SAR images despeckling using joint hidden Markov model

    Science.gov (United States)

    Li, Qiaoliang; Wang, Guoyou; Liu, Jianguo; Chen, Shaobo

    2007-11-01

    In the past few years, wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the deficiency for taking account of intrascale correlations that exist among neighboring wavelet coefficients. In this paper, we propose to develop a joint hidden Markov model by fusing the wavelet Bayesian denoising technique with an image regularization procedure based on HMT and Markov random field (MRF). The Expectation Maximization algorithm is used to estimate hyperparameters and specify the mixture model. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. It is shown that the joint method outperforms lee filter and standard HMT techniques in terms of the integrative measure of the equivalent number of looks (ENL) and Pratt's figure of merit(FOM), especially when dealing with speckle noise in large variance.

  17. 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.

  18. Classification of melanoma using wavelet-transform-based optimal feature set

    Science.gov (United States)

    Walvick, Ronn P.; Patel, Ketan; Patwardhan, Sachin V.; Dhawan, Atam P.

    2004-05-01

    The features used in the ABCD rule for characterization of skin lesions suggest that the spatial and frequency information in the nevi changes at various stages of melanoma development. To analyze these changes wavelet transform based features have been reported. The classification of melanoma using these features has produced varying results. In this work, all the reported wavelet transform based features are combined to form a single feature set. This feature set is then optimized by removing redundancies using principal component analysis. A feed forward neural network trained with the back propagation algorithm is then used in the classification process to obtain better classification results.

  19. Wavelet packet transform-based optical orthogonal frequency-division multiplexing transmission using direct detection

    Science.gov (United States)

    Zhang, Hongbo; Yi, Xingwen; Chen, Lei; Zhang, Jing; Deng, Mingliang; Qiu, Kun

    2012-10-01

    As an alternate to fast Fourier transform-based orthogonal frequency-division multiplexing (OFDM), wavelet packet transform (WPT)-based OFDM (WPT-OFDM) does not require cyclic prefix to avoid inter-symbol-interference. The wavelet has many varieties and therefore, it can provide more freedom for system design to suit different applications. We propose a real-valued WPT-OFDM that uses intensity modulation/direct detection. We also conduct an experiment to verify its performance through a 75-km standard single-mode fiber.

  20. [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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Phase synchronization based on a Dual-Tree Complex Wavelet Transform

    Science.gov (United States)

    Ferreira, Maria Teodora; Domingues, Margarete Oliveira; Macau, Elbert E. N.

    2016-11-01

    In this work, we show the applicability of our Discrete Complex Wavelet Approach (DCWA) to verify the phenomenon of phase synchronization transition in two coupled chaotic Lorenz systems. DCWA is based on the phase assignment from complex wavelet coefficients obtained by using a Dual-Tree Complex Wavelet Transform (DT-CWT). We analyzed two coupled chaotic Lorenz systems, aiming to detect the transition from non-phase synchronization to phase synchronization. In addition, we check how good is the method in detecting periods of 2π phase-slips. In all experiments, DCWA is compared with classical phase detection methods such as the ones based on arctangent and Hilbert transform showing a much better performance.

  6. 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.

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

    Directory of Open Access Journals (Sweden)

    Liu Zhaohua

    2014-06-01

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

  8. A novel super-resolution image fusion algorithm based on improved PCNN and wavelet transform

    Science.gov (United States)

    Liu, Na; Gao, Kun; Song, Yajun; Ni, Guoqiang

    2009-10-01

    Super-resolution reconstruction technology is to explore new information between the under-sampling image series obtained from the same scene and to achieve the high-resolution picture through image fusion in sub-pixel level. The traditional super-resolution fusion methods for sub-sampling images need motion estimation and motion interpolation and construct multi-resolution pyramid to obtain high-resolution, yet the function of the human beings' visual features are ignored. In this paper, a novel resolution reconstruction for under-sampling images of static scene based on the human vision model is considered by introducing PCNN (Pulse Coupled Neural Network) model, which simplifies and improves the input model, internal behavior and control parameters selection. The proposed super-resolution image fusion algorithm based on PCNN-wavelet is aimed at the down-sampling image series in a static scene. And on the basis of keeping the original features, we introduce Relief Filter(RF) to the control and judge segment to overcome the effect of random factors(such as noise, etc) effectively to achieve the aim that highlighting interested object though the fusion. Numerical simulations show that the new algorithm has the better performance in retaining more details and keeping high resolution.

  9. 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...

  10. Wavelet threshold image denoising algorithm based on MATLAB different wavelet bases%基于MATLAB不同小波基的小波阈值图像去噪算法

    Institute of Scientific and Technical Information of China (English)

    曾敬枫

    2016-01-01

    Through the introduction of wavelet image denoising method and wavelet threshold denoising steps,this paper discusses the role of wavelet bases in wavelet threshold denoising, and describes the characteristics of several common wavelet bases and their correlation properties. Finally, respectively with a db2 and sym4 two kinds of wavelet bases by MATLAB, to denoise wavelet threshold realizes the image filtering and reconstruction of high frequency coefficients, so the conclusion is obtained that using different wavelet bases affects the results of image denoising.%通过介绍小波图像去噪的方法和小波阈值去噪的步骤,讨论小波基在小波阈值去噪中的作用,阐述了常见的几种小波基的特征及其相关性质的比较。最后通过在MATLAB下,分别选择了db2和sym4两种小波基,进行小波阈值去噪实现图像高频系数的滤波并重建,得到采用不同的小波基影响图像去噪效果的结论。

  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. 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.

  13. An automatic sleep spindle detector based on wavelets and the teager energy operator.

    Science.gov (United States)

    Ahmed, Beena; Redissi, Amira; Tafreshi, Reza

    2009-01-01

    Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

  14. 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.

  15. Improving Resolution in k and r Space: A FEFF-based Wavelet for EXAFS Data Analysis

    Science.gov (United States)

    Funke, H.; Chukalina, M.; Voegelin, A.; Scheinost, A. C.

    2007-02-01

    Applying a wavelet analysis based on the Morlet mother function, we previously demonstrated the presence of both Al and Zn atoms in the first metal shell (r ≈ 3 Å from the central Zn atom) of Zn-Al layered double hydroxide (LDH). However, this approach was not suited to resolve the second and third metal shells (r ≈ 5 - 6 Å) in r and k space independently. Therefore, we developed a new FEFF-Morlet wavelet, where the EXAFS function itself, extracted from the FEFF model, is combined with the complex Morlet wavelet. With this method, we were able to distinguish the second metal shell (Zn atoms only) from the third metal shell (Zn and Al atoms), thereby proving a regular, dioctahedral distribution of Zn atoms in the hydroxide layers.

  16. The EM Method in a Probabilistic Wavelet-Based MRI Denoising

    Directory of Open Access Journals (Sweden)

    Marcos Martin-Fernandez

    2015-01-01

    Full Text Available Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak’s, Donoho-Johnstone’s, Awate-Whitaker’s, and nonlocal means filters, in different 2D and 3D images.

  17. 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.

  18. 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.

  19. Automatic Identification of Axis Orbit Based on Both Wavelet Moment Invariants and Neural Network

    Institute of Scientific and Technical Information of China (English)

    FuXiang-qian; LiuGuang-lin; JiangJing; LiYou-ping

    2003-01-01

    Axis orbit is an important characteristic to be used in the condition monitoring and diagnosis system of rotating machine. The wavelet moment has the invariant to the translation, scaling and rotation. A method, which uses a neural network based on Radial Basis Function (RBF) and wavelet moment invariants to identify the orbit of shaft centerline of rotating machine is discussed in this paper. The principle and its application procedure of the method are introduced in detail. It gives simulation results of automatic identification for three typical axis orbits. It is proved that the method is effective and practicable.

  20. Automatic Identification of Axis Orbit Based on Both Wavelet Moment Invariants and Neural Network

    Institute of Scientific and Technical Information of China (English)

    Fu Xiang-qian; Liu Guang-lin; Jiang Jing; Li You-ping

    2003-01-01

    Axis orbit is an important characteristic to be used in the condition monitoring and diagnosis system of rota-ting machine. The wavelet moment has the invariant to the translation, scaling and rotation. A method, which uses a neural network based on Radial Basis Function (RBF) and wavelet moment invariants to identify the orbit of shaft centerline of rotating machine is discussed in this paper. The principle and its application procedure of the method are intro-duced in detail. It gives simulation results of automatic identi-fication for three typical axis orbits. It is proved that the method is effective and practicable.

  1. 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.

  2. A Wavelet-Based Multiresolution Reconstruction Method for Fluorescent Molecular Tomography

    Directory of Open Access Journals (Sweden)

    Wei Zou

    2009-01-01

    Full Text Available Image reconstruction of fluorescent molecular tomography (FMT often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive, especially for the case where there are large deviations in the optical properties between the target and the reference medium. In this paper, a wavelet-based multiresolution reconstruction approach is proposed for the FMT reconstruction in combination with a parallel forward computing strategy, in which both the forward and the inverse problems of FMT are solved in the wavelet domain. Simulation results demonstrate that the proposed approach can significantly speed up the reconstruction process and improve the image quality of FMT.

  3. 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.

  4. Enhancement of islanding-detection of distributed generation systems via wavelet transform-based approaches

    Energy Technology Data Exchange (ETDEWEB)

    Hsieh, Cheng-Tao [Department of Electrical Engineering, Kun Shan University, Tainan 70101 (China); Lin, Jeu-Min [Department of Electrical Engineering, Far Eat University, Tainan 70101 (China); Huang, Shyh-Jier [Department of Electrical Engineering, National Cheng Kung University, Tainan 70101 (China)

    2008-12-15

    In this paper, a wavelet transform-based approach is proposed to detect the occurrence of islanding events in distributed generation systems. Thanks to time-frequency localization capabilities exhibited by wavelet transform, the approach embedded with this transform technique has grasped the appearance of the islanding event in a highly effective manner. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished. To validate the feasibility of this approach, the method has been validated through several scenarios. Test results supported the effectiveness of the method for the application considered. (author)

  5. 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.

  6. An Improved Singularity Computing Algorithm Based on Wavelet Transform Modulus Maxima Method

    Institute of Scientific and Technical Information of China (English)

    ZHAO Jian; XIE Duan; FAN Xun-li

    2006-01-01

    In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.

  7. Source location in plates based on the multiple sensors array method and wavelet analysis

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Hong Jun; Shin, Tae Jin; Lee, Sang Kwon [Inha University, Incheon (Korea, Republic of)

    2014-01-15

    A new method for impact source localization in a plate is proposed based on the multiple signal classification (MUSIC) and wavelet analysis. For source localization, the direction of arrival of the wave caused by an impact on a plate and the distance between impact position and sensor should be estimated. The direction of arrival can be estimated accurately using MUSIC method. The distance can be obtained by using the time delay of arrival and the group velocity of the Lamb wave in a plate. Time delay is experimentally estimated using the continuous wavelet transform for the wave. The elasto dynamic theory is used for the group velocity estimation.

  8. Optimal mother wavelet-based Lamb wave analyses and damage detection for composite structures

    Institute of Scientific and Technical Information of China (English)

    Li Fucai; Meng Guang; Ye Lin

    2007-01-01

    With the purpose of on-line structural health monitoring, a transducer network was embedded into composite structure to minimize the influence of surroundings. The intrinsic dispersion characteristic of Lamb wave makes the wavelet transform an effective signal processing method for guided waves. To get high precision in feature extraction, an information entropy-based optimal mother wavelet selection approach was proposed, which was used to choose the most appropriate basis function for particular Lamb wave analysis. By using the embedded sensor network and extracting time-of-flight, delamination in the composite laminate was identified and located. The results demonstrate the effectiveness of the proposed methods.

  9. 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.

  10. 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.

  11. Wavelet-based Poisson rate estimation using the Skellam distribution

    Science.gov (United States)

    Hirakawa, Keigo; Baqai, Farhan; Wolfe, Patrick J.

    2009-02-01

    Owing to the stochastic nature of discrete processes such as photon counts in imaging, real-world data measurements often exhibit heteroscedastic behavior. In particular, time series components and other measurements may frequently be assumed to be non-iid Poisson random variables, whose rate parameter is proportional to the underlying signal of interest-witness literature in digital communications, signal processing, astronomy, and magnetic resonance imaging applications. In this work, we show that certain wavelet and filterbank transform coefficients corresponding to vector-valued measurements of this type are distributed as sums and differences of independent Poisson counts, taking the so-called Skellam distribution. While exact estimates rarely admit analytical forms, we present Skellam mean estimators under both frequentist and Bayes models, as well as computationally efficient approximations and shrinkage rules, that may be interpreted as Poisson rate estimation method performed in certain wavelet/filterbank transform domains. This indicates a promising potential approach for denoising of Poisson counts in the above-mentioned applications.

  12. Improved successive refinement for wavelet-based embedded image compression

    Science.gov (United States)

    Creusere, Charles D.

    1999-10-01

    In this paper we consider a new form of successive coefficient refinement which can be used in conjunction with embedded compression algorithms like Shapiro's EZW (Embedded Zerotree Wavelet) and Said & Pearlman's SPIHT (Set Partitioning in Hierarchical Trees). Using the conventional refinement process, the approximation of a coefficient that was earlier determined to be significantly is refined by transmitting one of two symbols--an `up' symbol if the actual coefficient value is in the top half of the current uncertainty interval or a `down' symbol if it is the bottom half. In the modified scheme developed here, we transmit one of 3 symbols instead--`up', `down', or `exact'. The new `exact' symbol tells the decoder that its current approximation of a wavelet coefficient is `exact' to the level of precision desired. By applying this scheme in earlier work to lossless embedded compression (also called lossy/lossless compression), we achieved significant reductions in encoder and decoder execution times with no adverse impact on compression efficiency. These excellent results for lossless systems have inspired us to adapt this refinement approach to lossy embedded compression. Unfortunately, the results we have achieved thus far for lossy compression are not as good.

  13. 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.

  14. 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.    

  15. 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.

  16. 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-transform...

  17. 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 ...

  18. 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.

  19. [Retrieval of leaf net photosynthetic rate of moso bamboo forests using hyperspectral remote sen-sing based on wavelet transform].

    Science.gov (United States)

    Sun, Shao-bo; Du, Hua-qiangl; Li, Ping-heng; Zhou, Guo-mo; Xu, Xiao-juni; Gao, Guo-long; Li, Xue-jian

    2016-01-01

    This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    Mrinal Kanti Bhowmik

    2010-05-01

    Full Text Available This paper investigates Quotient based Fusion of thermal and visual images, which were individually passed through level-1 and level-2 multiresolution analyses. In the proposed system, the method-1 namely "Decompose then Quotient Fuse Level-1" and the method-2 namely "Decompose-Reconstruct in level-2 and then Fuse Quotients", both work on wavelet transformations of the visual and thermal face images. The wavelet transform is well-suited to manage different image resolutions 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 tested separately, among them the maximum recognition result for a class is 100% and the minimum recognition rate for a class is 73%.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. The Application of Continuous Wavelet Transform Based Foreground Subtraction Method in 21 cm Sky Surveys

    CERN Document Server

    Gu, Junhua; Wang, Jingying; An, Tao; Chen, Wen

    2013-01-01

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

  12. 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.

  13. Multivariate wavelet frames

    CERN Document Server

    Skopina, Maria; Protasov, Vladimir

    2016-01-01

    This book presents a systematic study of multivariate wavelet frames with matrix dilation, in particular, orthogonal and bi-orthogonal bases, which are a special case of frames. Further, it provides algorithmic methods for the construction of dual and tight wavelet frames with a desirable approximation order, namely compactly supported wavelet frames, which are commonly required by engineers. It particularly focuses on methods of constructing them. Wavelet bases and frames are actively used in numerous applications such as audio and graphic signal processing, compression and transmission of information. They are especially useful in image recovery from incomplete observed data due to the redundancy of frame systems. The construction of multivariate wavelet frames, especially bases, with desirable properties remains a challenging problem as although a general scheme of construction is well known, its practical implementation in the multidimensional setting is difficult. Another important feature of wavelet is ...

  14. 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.

  15. 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."

  16. 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.

  17. 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.

  18. Effective wavelet-based compression method with adaptive quantization threshold and zerotree coding

    Science.gov (United States)

    Przelaskowski, Artur; Kazubek, Marian; Jamrogiewicz, Tomasz

    1997-10-01

    Efficient image compression technique especially for medical applications is presented. Dyadic wavelet decomposition by use of Antonini and Villasenor bank filters is followed by adaptive space-frequency quantization and zerotree-based entropy coding of wavelet coefficients. Threshold selection and uniform quantization is made on a base of spatial variance estimate built on the lowest frequency subband data set. Threshold value for each coefficient is evaluated as linear function of 9-order binary context. After quantization zerotree construction, pruning and arithmetic coding is applied for efficient lossless data coding. Presented compression method is less complex than the most effective EZW-based techniques but allows to achieve comparable compression efficiency. Specifically our method has similar to SPIHT efficiency in MR image compression, slightly better for CT image and significantly better in US image compression. Thus the compression efficiency of presented method is competitive with the best published algorithms in the literature across diverse classes of medical images.

  19. 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.

  20. PERFORMANCE ANALYSIS OF DISCRETE WAVELET TRANSFORM BASED MULTIPLE INPUT MULTIPLE OUTPUT ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING SYSTEM FOR DIFFERENT WAVELETS IN DIFFERENT CHANNEL ENVIRONMENT

    Directory of Open Access Journals (Sweden)

    HariprasadNagarajan

    2013-01-01

    Full Text Available Multiple Input Multiple Output (MIMO and Orthogonal Frequency Division Multiplexing (OFDM are the two assuring technologies that offers high data rate as required for the 4G wireless systems. Conventionally OFDM is Fast Fourier Transform (FFT based system. It uses IFFT (Inverse FFT blocks in the transmitter and FFT blocks in the receiver. OFDM combined with MIMO gives increased throughput and better system performance and hence FFT based MIMO OFDM systems are widely used in 4G wireless schemes. Recent researches shows that replacing the FFT with Discrete Wavelet Transform (DWT the system performance can be further improved. This leads to a new scenario DWT based MIMO OFDM system. In this study one such system is simulated and the Bit Error Rate (BER performance of the system is analysed for the different types of wavelets under different channel environments.

  1. 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...

  2. Recognition of OFDM and Single Carrier Digital Signals Based on Wavelet Ridge%基于小波脊线的OFDM与单载波信号识别

    Institute of Scientific and Technical Information of China (English)

    张传忠; 段田东; 刘世刚; 徐文艳

    2011-01-01

    正交频分复用(OFDM,Orthogonal Frequency Division Multiplexing)与单载波信号广泛应用于短波通信领域.针对低信噪比和多径环境下OFDM与单载波信号识别效率低的问题,本文提出了基于小波脊线的信号识别算法.本文推导了常用信号对应的小波脊线幅度和脊点位置,并分析了小波脊线幅度和脊点形态.通过理论推导和仿真测试证明了OFDM与单载波信号对应小波脊线具有不同特征,对小波脊线差分、中值滤波、并利用其熵作为特征值能够有效的进行OFDM信号与单载波信号的识别.仿真结果证明该算法对输入信号点数要求低,在低信噪比和短波中等信道下识别效果具有稳健性和有效性.%Orthogonal Frequency Division Multiplexing (OFDM) and single carrier digital signals are widely used in High Frequency (HF) communication. Aim at OFDM signals and single carrier digital signals have a low recognition efficiency in lower signal-to-noise ratio (SNR) and multi-path environment in HF communication, a new algorithm based on wavelet ridge is proposed. According to the wavelet ridge amplitude and wavelet ridge patterns characters can reflect instantaneous different characters of different signals types, derivation is provided of the normal used signals' wavelet ridges characters in mathematical formula also. Corresponding to the difference between OFDM signals and single carrier digital signals in wavelet ridge amplitude and wavelet ridge patterns, differential coefficient, median filter are used in this paper, and then calculate entropy of the wavelet ridges after differential coefficient and median filter as a feature value, finally achieved to recognize of OFDM signals and single carrier digital signals. Computer simulation shows that the algorithm is not sensitive to the input signal points and this algorithm is both feasible and effective in low SNR environment.

  3. A Novel Method for Inverter Faults Detection and Diagnosis in PMSM Drives of HEVs based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    AKTAS, M.

    2012-11-01

    Full Text Available The paper proposes a novel method, based on wavelet decomposition, for detection and diagnosis of faults (switch short-circuits and switch open-circuits in the driving systems with Field Oriented Controlled Permanent Magnet Synchro?nous Motors (PMSM of Hybrid Electric Vehicles. The fault behaviour of the analyzed system was simulated by Matlab/SIMULINK R2010a. The stator currents during transients were analysed up to the sixth level detail wavelet decomposition by Symlet2 wavelet. The results prove that the proposed fault diagnosis system have very good capabilities.

  4. 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.

  5. 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.

  6. The segmentation of FMI image based on 2-D dyadic wavelet transform

    Science.gov (United States)

    Liu, Rui-Lin; Wu, Yue-Qi; Liu, Jian-Hua; Ma, Yong

    2005-06-01

    A key aspect in extracting quantitative information from FMI logs is to segment the FMI image to get images of pores, vugs and fractures. A segmentation method based on the dyadic wavelet transform in 2-D is introduced in this paper. The first step is to find all the edge pixels of the FMI image using the 2-D wavelet transform. The second step is to calculate a segmentation threshold based on the average value of the edge pixels. Field data processing examples show that sub-images of vugs and fractures can be correctly separated from original FMI data continuously and automatically along the depth axis. The image segmentation lays the foundation for in-situ parameter calculation.

  7. Texture Segmentation Using Laplace Distribution-Based Wavelet-Domain Hidden Markov Tree Models

    Directory of Open Access Journals (Sweden)

    Yulong Qiao

    2016-11-01

    Full Text Available Multiresolution models such as the wavelet-domain hidden Markov tree (HMT model provide a powerful approach for image modeling and processing because it captures the key features of the wavelet coefficients of real-world data. It is observed that the Laplace distribution is peakier in the center and has heavier tails compared with the Gaussian distribution. Thus we propose a new HMT model based on the two-state, zero-mean Laplace mixture model (LMM, the LMM-HMT, which provides significantly potential for characterizing real-world textures. By using the HMT segmentation framework, we develop LMM-HMT based segmentation methods for image textures and dynamic textures. The experimental results demonstrate the effectiveness of the introduced model and segmentation methods.

  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. 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.

  10. Low-dose computed tomography image denoising based on joint wavelet and sparse representation.

    Science.gov (United States)

    Ghadrdan, Samira; Alirezaie, Javad; Dillenseger, Jean-Louis; Babyn, Paul

    2014-01-01

    Image denoising and signal enhancement are the most challenging issues in low dose computed tomography (CT) imaging. Sparse representational methods have shown initial promise for these applications. In this work we present a wavelet based sparse representation denoising technique utilizing dictionary learning and clustering. By using wavelets we extract the most suitable features in the images to obtain accurate dictionary atoms for the denoising algorithm. To achieve improved results we also lower the number of clusters which reduces computational complexity. In addition, a single image noise level estimation is developed to update the cluster centers in higher PSNRs. Our results along with the computational efficiency of the proposed algorithm clearly demonstrates the improvement of the proposed algorithm over other clustering based sparse representation (CSR) and K-SVD methods.

  11. 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.

  12. 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.

  13. 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.

  14. Weld Defect Extraction Based on Adaptive Morphology Filtering and Edge Detection by Wavelet Analysis

    Institute of Scientific and Technical Information of China (English)

    WANGDonghua; ZHOUYuanhua; GANGTie

    2003-01-01

    One of the most key steps in X-ray au-tomatic inspection and intelligent recognition systems is how to extract defects and detect their edges effectively.In this paper, a novel method of defect extraction based on the adaptive morphology filtering (DEAMF) is pro-posed, whose structuring elements can be changed with the sizes of defects adaptively. By this method, defects in X-ray weld inspection images are extracted with well-kept shapes and high speeds. Then according to the theory of edge detection based on wavelet transform modulus max-ima, a locally supported wavelet with good antisymmetry is developed to extract edges of defects and the results are satisfying.

  15. 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.

  16. 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.

  17. 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.

  18. Discovering Wavelets

    CERN Document Server

    Aboufadel, Edward

    1999-01-01

    An accessible and practical introduction to wavelets. With applications in image processing, audio restoration, seismology, and elsewhere, wavelets have been the subject of growing excitement and interest over the past several years. Unfortunately, most books on wavelets are accessible primarily to research mathematicians. Discovering Wavelets presents basic and advanced concepts of wavelets in a way that is accessible to anyone with only a fundamental knowledge of linear algebra. The basic concepts of wavelet theory are introduced in the context of an explanation of how the FBI uses wavelets

  19. 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.

  20. 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....

  1. A wavelet-based ECG delineation algorithm for 32-bit integer online processing

    OpenAIRE

    Chiari Lorenzo; Di Marco Luigi Y

    2011-01-01

    Abstract Background Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementati...

  2. 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.

  3. 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.

  4. Wavelets in neuroscience

    CERN Document Server

    Hramov, Alexander E; Makarov, Valeri A; Pavlov, Alexey N; Sitnikova, Evgenia

    2015-01-01

    This book examines theoretical and applied aspects of wavelet analysis in neurophysics, describing in detail different practical applications of the wavelet theory in the areas of neurodynamics and neurophysiology and providing a review of fundamental work that has been carried out in these fields over the last decade. Chapters 1 and 2 introduce and review the relevant foundations of neurophysics and wavelet theory, respectively, pointing on one hand to the various current challenges in neuroscience and introducing on the other the mathematical techniques of the wavelet transform in its two variants (discrete and continuous) as a powerful and versatile tool for investigating the relevant neuronal dynamics. Chapter 3 then analyzes results from examining individual neuron dynamics and intracellular processes. The principles for recognizing neuronal spikes from extracellular recordings and the advantages of using wavelets to address these issues are described and combined with approaches based on wavelet neural ...

  5. 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.

  6. 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.

  7. Examination of the wavelet-based approach for measuring self-similarity of epileptic electroencephalogram data

    Institute of Scientific and Technical Information of China (English)

    Suparerk JANJARASJITT

    2014-01-01

    Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram (EEG) signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study, a computational method for estimating the spectral exponent based on wavelet transform was examined. A series of Daubechies wavelet bases with various numbers of vanishing moments were applied to analyze the self-similar characteristics of intracranial EEG data corresponding to different pathological states of the brain, i.e., ictal and interictal states, in patients with epilepsy. The computational results show that the spectral exponents of intracranial EEG signals obtained during epileptic seizure activity tend to be higher than those obtained during non-seizure periods. This suggests that the intracranial EEG signals obtained during epileptic seizure activity tend to be more self-similar than those obtained during non-seizure periods. The computational results obtained using the wavelet-based approach were validated by comparison with results obtained using the power spectrum method.

  8. Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

    Science.gov (United States)

    Li, Zhixiong; Yan, Xinping; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

    2011-10-01

    Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and

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

    Science.gov (United States)

    Hu, Ping; Yang, Tie-jun

    2016-10-01

    The incidence of cutaneous malignant melanoma, a disease of worldwide distribution and is the deadliest form of skin cancer, has been rapidly increasing over the last few decades. Because advanced cutaneous melanoma is still incurable, early detection is an important step toward a reduction in mortality. Dermoscopy photographs are commonly used in melanoma diagnosis and can capture detailed features of a lesion. A great variability exists in the visual appearance of pigmented skin lesions. Therefore, in order to minimize the diagnostic errors that result from the difficulty and subjectivity of visual interpretation, an automatic detection approach is required. The objectives of this paper were to propose a hybrid method using random forest and Gabor wavelet transformation to accurately differentiate which part belong to lesion area and the other is not in a dermoscopy photographs and analyze segmentation accuracy. A random forest classifier consisting of a set of decision trees was used for classification. Gabor wavelets transformation are the mathematical model of visual cortical cells of mammalian brain and an image can be decomposed into multiple scales and multiple orientations by using it. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain. Texture features based on Gabor wavelets transformation are found by the Gabor filtered image. Experiment results indicate the following: (1) the proposed algorithm based on random forest outperformed the-state-of-the-art in pigmented skin lesions detection (2) and the inclusion of Gabor wavelet transformation based texture features improved segmentation accuracy significantly.

  10. 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

  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. 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.

  13. 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.

  14. 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.

  15. Wavelet-based method for image filtering using scale-space continuity

    Science.gov (United States)

    Jung, Claudio R.; Scharcanski, Jacob

    2001-04-01

    This paper proposes a novel technique to reduce noise while preserving edge sharpness during image filtering. This method is based on the image multiresolution decomposition by a discrete wavelet transform, given a proper wavelet basis. In the transform spaces, edges are implicitly located and preserved, at the same time that image noise is filtered out. At each resolution level, geometric continuity is used to preserve edges that are not isolated. Finally, we compare consecutive levels to preserve edges having continuity along scales. As a result, the proposed technique produces a filtered version of the original image, where homogeneous regions appear segmented by well-defined edges. Possible applications include image presegmentation and image denoising.

  16. 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.

  17. 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

    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......). 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...... using a variety of standard images and its performance has been compared against several de-noising algorithms known from the prior art. Experimental results show that the proposed algorithm preserves the edges better and in most cases, improves the measured visual quality of the denoised images...

  18. 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.

  19. 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.

  20. 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.

  1. Wavelet-based correlations of impedance cardiography signals and heart rate variability

    Science.gov (United States)

    Podtaev, Sergey; Dumler, Andrew; Stepanov, Rodion; Frick, Peter; Tziberkin, Kirill

    2010-04-01

    The wavelet-based correlation analysis is employed to study impedance cardiography signals (variation in the impedance of the thorax z(t) and time derivative of the thoracic impedance (- dz/dt)) and heart rate variability (HRV). A method of computer thoracic tetrapolar polyrheocardiography is used for hemodynamic registrations. The modulus of wavelet-correlation function shows the level of correlation, and the phase indicates the mean phase shift of oscillations at the given scale (frequency). Significant correlations essentially exceeding the values obtained for noise signals are defined within two spectral ranges, which correspond to respiratory activity (0.14-0.5 Hz), endothelial related metabolic activity and neuroendocrine rhythms (0.0095-0.02 Hz). Probably, the phase shift of oscillations in all frequency ranges is related to the peculiarities of parasympathetic and neuro-humoral regulation of a cardiovascular system.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Wavelet-based improved Chan-Vese model for image segmentation

    Science.gov (United States)

    Zhao, Xiaoli; Zhou, Pucheng; Xue, Mogen

    2016-10-01

    In this paper, a kind of image segmentation approach which based on improved Chan-Vese (CV) model and wavelet transform was proposed. Firstly, one-level wavelet decomposition was adopted to get the low frequency approximation image. And then, the improved CV model, which contains the global term, local term and the regularization term, was utilized to segment the low frequency approximation image, so as to obtain the coarse image segmentation result. Finally, the coarse segmentation result was interpolated into the fine scale as an initial contour, and the improved CV model was utilized again to get the fine scale segmentation result. Experimental results show that our method can segment low contrast images and/or inhomogeneous intensity images more effectively than traditional level set methods.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Method for low-light-level image compression based on wavelet transform

    Science.gov (United States)

    Sun, Shaoyuan; Zhang, Baomin; Wang, Liping; Bai, Lianfa

    2001-10-01

    Low light level (LLL) image communication has received more and more attentions in the night vision field along with the advance of the importance of image communication. LLL image compression technique is the key of LLL image wireless transmission. LLL image, which is different from the common visible light image, has its special characteristics. As still image compression, we propose in this paper a wavelet-based image compression algorithm suitable for LLL image. Because the information in the LLL image is significant, near lossless data compression is required. The LLL image is compressed based on improved EZW (Embedded Zerotree Wavelet) algorithm. We encode the lowest frequency subband data using DPCM (Differential Pulse Code Modulation). All the information in the lowest frequency is kept. Considering the HVS (Human Visual System) characteristics and the LLL image characteristics, we detect the edge contour in the high frequency subband image first using templet and then encode the high frequency subband data using EZW algorithm. And two guiding matrix is set to avoid redundant scanning and replicate encoding of significant wavelet coefficients in the above coding. The experiment results show that the decoded image quality is good and the encoding time is shorter than that of the original EZW algorithm.

  16. 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.

  17. 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.

  18. Wavelet-based neural network analysis of ophthalmic artery Doppler signals.

    Science.gov (United States)

    Güler, Nihal Fatma; Ubeyli, Elif Derya

    2004-10-01

    In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.

  19. 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.

  20. 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.