WorldWideScience

Sample records for based multi wavelet

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

    Directory of Open Access Journals (Sweden)

    Qiaoning Yang

    2015-10-01

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

  2. Fast wavelet-based pansharpening of multi-spectral images

    OpenAIRE

    Mitianoudis, Nikolaos; Tzimiropoulos, Georgios; Stathaki, Tania

    2010-01-01

    Remote Sensing systems enhance the spatial quality of low-resolution Multi-Spectral (MS) images using information from Pan-chromatic (PAN) images under the pansharpening framework. Most decimated multi-resolution pansharpening approaches upsample the low-resolution MS image to match the resolution of the PAN image. Consequently, a multi-level wavelet decomposition is performed, where the edge information from the PAN image is injected in the MS image. In this paper, the authors propose a pans...

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

    Science.gov (United States)

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

    2016-05-30

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

  4. 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.

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

    OpenAIRE

    Qiaoning Yang; Jianlin Wang

    2015-01-01

    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. Ba...

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

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

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

  8. Compactly supported multi-wavelets

    Directory of Open Access Journals (Sweden)

    Wojciech Banaś

    2012-01-01

    Full Text Available In this paper we show some construction of compactly supported multi-wavelets in \\(L^2(\\mathbb{R}^d\\, \\(d \\geq 2\\ which is based on the one-dimensional case, when \\(d=1\\. We also demonstrate that some methods, which are useful in the construction of wavelets with a compact support at \\(d=1\\, can be adapted to higher-dimensional cases if \\(A \\in M_{d \\times d}(\\mathbb{Z}\\ is an expansive matrix of a special form.

  9. 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.

  10. 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

  11. A gear rattle metric based on the wavelet multi-resolution analysis: Experimental investigation

    Science.gov (United States)

    Brancati, Renato; Rocca, Ernesto; Savino, Sergio

    2015-01-01

    In the article an investigation about the feasibility of a wavelet analysis for gear rattle metric in transmission gears, due to tooth impacts under unloaded conditions, is conducted. The technique adopts the discrete wavelet transform (DWT), following the Multi-resolution analysis, to decompose an experimental signal of the relative angular motion of gears into an approximation and in some detail vectors. The described procedure, previously developed by the authors, permits the qualitative evaluation of the impacts occurring between the teeth by examining in particular the detail vectors coming out from the wavelet decomposition. The technique enables discriminating between the impacts occurring on the two different sides of tooth. This situation is typical of the double-sided gear rattle produced in the automotive gear boxes. This paper considers the influence of oil lubricant, inserted between the teeth, in reducing the impacts. Analysis is performed by comparing three different lubrication conditions, and some of the classical wavelet functions adopted in literature are tested as "mother" wavelet. Moreover, comparisons with a metric based on the harmonic analysis by means of the Fast Fourier Transform (FFT), often adopted in this field, are conducted to put in evidence the advantages of the Wavelet technique with reference to the influence of some fundamental operative parameters. The experimental signals of the relative angular rotation of gear are acquired by two high resolution incremental encoders on a specific test rig for lightly loaded gears. The results of the proposed method appear optimistic also in the detection of defects that could produce little variations in the dynamic behavior of unloaded gears.

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

    Science.gov (United States)

    Huang, Yan; Wang, Zhihui

    2015-12-01

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

  13. 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.

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

    OpenAIRE

    Chien-Ming Chou

    2011-01-01

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

  15. Wavelet Based Image Denoising Technique

    Directory of Open Access Journals (Sweden)

    Sachin D Ruikar

    2011-03-01

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

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

    Directory of Open Access Journals (Sweden)

    S. Sakthivel

    2011-01-01

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

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

    OpenAIRE

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

    2015-01-01

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

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

    OpenAIRE

    Akimov Pavel Alekseevich; Mozgaleva Marina Leonidovna

    2012-01-01

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

  19. 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

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

    Institute of Scientific and Technical Information of China (English)

    GAO Xieping; ZHOU Siwang

    2005-01-01

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

  1. A multi-resolution filtered-x LMS algorithm based on discrete wavelet transform for active noise control

    Science.gov (United States)

    Qiu, Z.; Lee, C.-M.; Xu, Z. H.; Sui, L. N.

    2016-01-01

    We have developed a new active control algorithm based on discrete wavelet transform (DWT) for both stationary and non-stationary noise control. First, the Mallat pyramidal algorithm is introduced to implement the DWT, which can decompose the reference signal into several sub-bands with multi-resolution and provides a perfect reconstruction (PR) procedure. To reduce the extra computational complexity introduced by DWT, an efficient strategy is proposed that updates the adaptive filter coefficients in the frequency domainDeepthi B.B using a fast Fourier transform (FFT). Based on the reference noise source, a 'Haar' wavelet is employed and by decomposing the noise signal into two sub-band (3-band), the proposed DWT-FFT-based FXLMS (DWT-FFT-FXLMS) algorithm has greatly reduced complexity and a better convergence performance compared to a time domain filtered-x least mean square (TD-FXLMS) algorithm. As a result of the outstanding time-frequency characteristics of wavelet analysis, the proposed DWT-FFT-FXLMS algorithm can effectively cancel both stationary and non-stationary noise, whereas the frequency domain FXLMS (FD-FXLMS) algorithm cannot approach this point.

  2. Wavelet-based fluid motion estimation

    OpenAIRE

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

    2011-01-01

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

  3. Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

    Science.gov (United States)

    Abbasion, S.; Rafsanjani, A.; Farshidianfar, A.; Irani, N.

    2007-10-01

    Due to the importance of rolling bearings as one of the most widely used industrial machinery elements, development of proper monitoring and fault diagnosis procedure to prevent malfunctioning and failure of these elements during operation is necessary. For rolling bearing fault detection, it is expected that a desired time-frequency analysis method has good computational efficiency, and has good resolution in both, time and frequency domains. The point of interest of this investigation is the presence of an effective method for multi-fault diagnosis in such systems with optimizing signal decomposition levels by using wavelet analysis and support vector machine (SVM). The system that is under study is an electric motor which has two rolling bearings, one of them is next to the output shaft and the other one is next to the fan and for each of them there is one normal form and three false forms, which make 8 forms for study. The results that we achieved from wavelet analysis and SVM are fully in agreement with empirical result.

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

    Directory of Open Access Journals (Sweden)

    Akimov Pavel Alekseevich

    2012-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-04-01

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

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

    CERN Document Server

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

    2010-01-01

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

  7. 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 ...

  8. Wavelet-Based Volume Visualization

    NARCIS (Netherlands)

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

    1999-01-01

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

  9. 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.

  10. Wavelet-Based Grid Generation

    Science.gov (United States)

    Jameson, Leland

    1996-01-01

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

  11. Leather inspection based on wavelets

    OpenAIRE

    Sobral, João Luís Ferreira

    2005-01-01

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

  12. From cardinal spline wavelet bases to highly coherent dictionaries

    International Nuclear Information System (INIS)

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

  13. From cardinal spline wavelet bases to highly coherent dictionaries

    Energy Technology Data Exchange (ETDEWEB)

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

    2008-05-02

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

  14. 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.

  15. 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.

  16. [A method for obtaining redshifts of quasars based on wavelet multi-scaling feature matching].

    Science.gov (United States)

    Liu, Zhong-Tian; Li, Xiang-Ru; Wu, Fu-Chao; Zhao, Yong-Heng

    2006-09-01

    The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc. PMID:17112059

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

    Directory of Open Access Journals (Sweden)

    K. Vijayarekha

    2012-12-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Zhang Yifan; He Mingyi

    2007-01-01

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

  19. Tree Based Wavelet Transform and DAG SVM for Seizure Detection

    Directory of Open Access Journals (Sweden)

    A.S.Muthanantha Murugavel

    2012-02-01

    Full Text Available In this paper, we have proposed a new tree based wavelet transform (TBWT for feature extraction scheme for epileptic seizure detection. Also this paper uses the Directed Acyclic Graph Support Vector Machine (DAGSVM for the multi-class electroencephalogram (EEG signals classification. The main aim was to determine the effective features for this problem. Wavelets have played an important role in biomedical signal processing for its ability to capture localized spatial-frequency information of EEG signals. The TBWT works well for high dimensional, multi-class data streams. Decision making was performed in two stages: feature extraction by computing the approximate and detailed wavelet coefficients and classification using the classifiers trained on the extracted features. We have compared the TBWT with wavelet based transform by evaluating with the benchmark EEG dataset. Our experimental results show that the TBWT with DAGSVM gives higher classification accuracy such as 97% than the existing classifier.

  20. 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...

  1. Vibrator Data Denoising Based on Fractional Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Zheng Jing

    2015-06-01

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

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

    OpenAIRE

    Lalitha, EM; Satish, L.

    2000-01-01

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

  3. Dual-Tree Complex Wavelet Transform and Image Block Residual-Based Multi-Focus Image Fusion in Visual Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yong Yang

    2014-11-01

    Full Text Available This paper presents a novel framework for the fusion of multi-focus images explicitly designed for visual sensor network (VSN environments. Multi-scale based fusion methods can often obtain fused images with good visual effect. However, because of the defects of the fusion rules, it is almost impossible to completely avoid the loss of useful information in the thus obtained fused images. The proposed fusion scheme can be divided into two processes: initial fusion and final fusion. The initial fusion is based on a dual-tree complex wavelet transform (DTCWT. The Sum-Modified-Laplacian (SML-based visual contrast and SML are employed to fuse the low- and high-frequency coefficients, respectively, and an initial composited image is obtained. In the final fusion process, the image block residuals technique and consistency verification are used to detect the focusing areas and then a decision map is obtained. The map is used to guide how to achieve the final fused image. The performance of the proposed method was extensively tested on a number of multi-focus images, including no-referenced images, referenced images, and images with different noise levels. The experimental results clearly indicate that the proposed method outperformed various state-of-the-art fusion methods, in terms of both subjective and objective evaluations, and is more suitable for VSNs.

  4. Wavelet-based prediction of oil prices

    International Nuclear Information System (INIS)

    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

  5. 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.

  6. Wavelet Image Encryption Algorithm Based on AES

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

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

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

    OpenAIRE

    Kohei Arai

    2013-01-01

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

  8. Tree Based Wavelet Transform and DAG SVM for Seizure Detection

    Directory of Open Access Journals (Sweden)

    A.S.Muthanantha Murugavel

    2012-03-01

    Full Text Available In this paper, we have proposed a new tree based wavelet transform (TBWT for feature extraction scheme for epileptic seizure detection. Also this paper uses the Directed Acyclic Graph Support Vector Machine (DAGSVM for the multi-class electroencephalogram (EEG signals classification. The main aim was to determine the effective features for this problem. Wavelets have played an important role in biomedical signal processing for its ability to capture localized spatial-frequency information of EEG signals. The TBWT works well for high dimensional, multi-class data streams. Decision making was performed in two stages: feature extraction by computing the approximate and detailed wavelet coefficients and classification using the classifiers trained on the extracted features. We have compared the TBWT withwavelet based transform by evaluating with the benchmark EEG dataset. Our experimental results show that the TBWT with DAGSVM gives higher classification accuracy such as 97% than the existing classifier.

  9. Wavelet methods in multi-conjugate adaptive optics

    International Nuclear Information System (INIS)

    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. (paper)

  10. Hydrologic regionalization using wavelet-based multiscale entropy method

    Science.gov (United States)

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

    2016-07-01

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

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

    Science.gov (United States)

    Joglekar, D. M.; Mitra, M.

    2015-12-01

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

  12. Texture Classification based on Gabor Wavelet

    OpenAIRE

    Amandeep Kaur; Savita Gupta

    2012-01-01

    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 Vec...

  13. 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.

  14. 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.

  15. 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.

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

    Institute of Scientific and Technical Information of China (English)

    凌同华; 张胜; 陈倩倩; 李洁

    2015-01-01

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

  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. 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.

  19. 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.

  20. Wavelet Packet based Multicarrier Modulation for Cognitive UWB Systems

    Directory of Open Access Journals (Sweden)

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

    2010-06-01

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

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

    Institute of Scientific and Technical Information of China (English)

    尹冰之; 李勇

    2012-01-01

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

  2. 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.

  3. Complex Wavelet Based Modulation Analysis

    DEFF Research Database (Denmark)

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

    2008-01-01

     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...... spectro-temporal analytic processing of slow frequency and phase varying signals....

  4. Wavelet Based QRS Complex Detection of ECG Signal

    OpenAIRE

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

    2012-01-01

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

  5. Wavelet based detection of manatee vocalizations

    Science.gov (United States)

    Gur, Berke M.; Niezrecki, Christopher

    2005-04-01

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

  6. 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.

  7. A New Wavelet Based Approach to Assess Hydrological Models

    Science.gov (United States)

    Adamowski, J. F.; Rathinasamy, M.; Khosa, R.; Nalley, D.

    2014-12-01

    In this study, a new wavelet based multi-scale performance measure (Multiscale Nash Sutcliffe Criteria, and Multiscale Normalized Root Mean Square Error) for hydrological model comparison was developed and tested. The new measure provides a quantitative measure of model performance across different timescales. Model and observed time series are decomposed using the a trous wavelet transform, and performance measures of the model are obtained at each time scale. The usefulness of the new measure was tested using real as well as synthetic case studies. The real case studies included simulation results from the Soil Water Assessment Tool (SWAT), as well as statistical models (the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods). Data from India and Canada were used. The synthetic case studies included different kinds of errors (e.g., timing error, as well as under and over prediction of high and low flows) in outputs from a hydrologic model. It was found that the proposed wavelet based performance measures (i.e., MNSC and MNRMSE) are a more reliable measure than traditional performance measures such as the Nash Sutcliffe Criteria, Root Mean Square Error, and Normalized Root Mean Square Error. It was shown that the new measure can be used to compare different hydrological models, as well as help in model calibration.

  8. Multi-dimensional medical images compressed and filtered with wavelets

    International Nuclear Information System (INIS)

    Full text: Using the standard wavelet decomposition methods, multi-dimensional medical images can be compressed and filtered by repeating the wavelet-algorithm on 1D-signals in an extra loop per extra dimension. In the non-standard decomposition for multi-dimensional images the areas that must be zero-filled in case of band- or notch-filters are more complex than geometric areas such as rectangles or cubes. Adding an additional dimension in this algorithm until 4D (e.g. a 3D beating heart) increases the geometric complexity of those areas even more. The aim of our study was to calculate the boundaries of the formed complex geometric areas, so we can use the faster non-standard decomposition to compress and filter multi-dimensional medical images. Because a lot of 3D medical images taken by PET- or SPECT-cameras have only a few layers in the Z-dimension and compressing images in a dimension with a few voxels is usually not worthwhile, we provided a solution in which one can choose which dimensions will be compressed or filtered. With the proposal of non-standard decomposition on Daubechies' wavelets D2 to D20 by Steven Gollmer in 1992, 1D data can be compressed and filtered. Each additional level works only on the smoothed data, so the transformation-time halves per extra level. Zero-filling a well-defined area alter the wavelet-transform and then performing the inverse transform will do the filtering. To be capable to compress and filter up to 4D-Images with the faster non-standard wavelet decomposition method, we have investigated a new method for calculating the boundaries of the areas which must be zero-filled in case of filtering. This is especially true for band- and notch filtering. Contrary to the standard decomposition method, the areas are no longer rectangles in 2D or cubes in 3D or a row of cubes in 4D: they are rectangles expanded with a half-sized rectangle in the other direction for 2D, cubes expanded with half cubes in one and quarter cubes in the

  9. Wavelet Based Image Fusion for Detection of Brain Tumor

    Directory of Open Access Journals (Sweden)

    CYN Dwith

    2013-01-01

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

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

    Directory of Open Access Journals (Sweden)

    K. Kannan

    2010-11-01

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

  11. 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.

  12. 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.

  13. 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...

  14. 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.

  15. 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.

  16. 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%.

  17. Soft Sensing Based on Hilbert-Huang Transform and Wavelet Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Qiang Wang

    2013-07-01

    Full Text Available At present, much more soft sensing have been widely used in industrial process control to improve the quality of product and assure safety in production. A novel method using  Hilbert-Huang transform(HHT combined with wavelet support vector machine(WSVM is put forward.Firstly the method analyzes the intrinsic mode function (IMF obtained after the empirical mode decomposition (EMD, then extracts IMF energy feature as the input feature vectors of the wavelet support vector machine. Based on the wavelet analysis and conditions of the support vector kernel function, a novel multi-dimension admissible support vector wavelet kernel function is presented, which is a multidimensional wavelet kernel, thus enhancing the generalization ability of the SVM. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that HHT-WSVM approach has a better prediction and generalization.

  18. A Wavelet-Based Test for Stationarity

    OpenAIRE

    Sachs, Rainer von; Neumann, Michael H.

    1997-01-01

    We develop a test for stationarity of a time series against the alternative of a time-changing covariance structure. Using localized versions of the periodogram, we obtain empirical versions of a reasonable notion of a time-varying spectral density. Coefficients w.r.t. a Haar wavelet series expansion of such a time-varying periodogram are a possible indicator whether there is some deviation from covariance stationarity. We propose a test based on the limit distribution of these empirical coef...

  19. 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.

  20. 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.

  1. Gestures recognition based on wavelet and LLE

    International Nuclear Information System (INIS)

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

  2. 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 ”.

  3. 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 ”.

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

    Directory of Open Access Journals (Sweden)

    Tao ZHANG

    2011-02-01

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

  5. 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.

  6. MRI segmentation study based on wavelet-domain hidden Markov models

    International Nuclear Information System (INIS)

    Full text.The wavelet's transform has emerged as exciting new tool for statistical image processing. The wavelet domain provides a natural setting for many applications in medical imaging and tele medicine area. The interesting properties of wavelet transform have led to a powerful image processing technique based on a simple transformation of individual wavelet coefficient as thought it were dependent of all others. By exploiting the dependencies between wavelet coefficients, a new wavelet domain probability models have been developed based on the hidden Markov probability models. The Wavelet-domain hidden Markov (HMM) models have recently been introduced and successfully applied in image processing area and in particular the Hidden Markov tree (HMT) models. The HMT models can characterize the joint statistics of wavelet coefficients across scales. these models are tree-structured probabilistic graph that captures statistical properties of the coefficient of wavelet transform. Since the HMT is particularly well suited to image containing singularities like edge and ridge, it provides a good classifier for distinguishing between textures of image. Using the inherent tree structure of the wavelet HMT and it fast training and likelihood algorithms, the texture classification at range of different scales. We then fuse these multi scale classifications using Bayesian probabilistic graph to obtain reliable final segmentations. Finally, the compressed image can be segmented directly. In our work, we have applied these models for texture segmenting of compressed MRI images by using the HMT models. By concisely modeling and fusing the statistical behavior of textures at multiple scales, the algorithm developed on HTM models produces an accurate segmentation of texture images yielding a range of segmentation at different scales. One of the most important results is capability of segmenting compressed image without re-expanding, this create a framework for developing joint

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

    Science.gov (United States)

    Zhang, Hai-mo

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

  8. 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.

  9. Wavelet-based verification of the quantitative precipitation forecast

    Science.gov (United States)

    Yano, Jun-Ichi; Jakubiak, Bogumil

    2016-06-01

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

  10. 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.

  11. Wavelet and ANN Based Relaying for Power Transformer Protection

    OpenAIRE

    Sudha, S.; A. E. Jeyakumar

    2007-01-01

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

  12. 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.

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

    Institute of Scientific and Technical Information of China (English)

    陈一民; 姚杰

    2014-01-01

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

  14. 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.

  15. Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation

    Directory of Open Access Journals (Sweden)

    Doroslovački Miloš

    2007-01-01

    Full Text Available The μ-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.

  16. Wavelet-Based MPNLMS Adaptive Algorithm for Network Echo Cancellation

    Directory of Open Access Journals (Sweden)

    Hongyang Deng

    2007-03-01

    Full Text Available The μ-law proportionate normalized least mean square (MPNLMS algorithm has been proposed recently to solve the slow convergence problem of the proportionate normalized least mean square (PNLMS algorithm after its initial fast converging period. But for the color input, it may become slow in the case of the big eigenvalue spread of the input signal's autocorrelation matrix. In this paper, we use the wavelet transform to whiten the input signal. Due to the good time-frequency localization property of the wavelet transform, a sparse impulse response in the time domain is also sparse in the wavelet domain. By applying the MPNLMS technique in the wavelet domain, fast convergence for the color input is observed. Furthermore, we show that some nonsparse impulse responses may become sparse in the wavelet domain. This motivates the usage of the wavelet-based MPNLMS algorithm. Advantages of this approach are documented.

  17. 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.

  18. 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.

  19. Wavelet-based moment invariants for pattern recognition

    Science.gov (United States)

    Chen, Guangyi; Xie, Wenfang

    2011-07-01

    Moment invariants have received a lot of attention as features for identification and inspection of two-dimensional shapes. In this paper, two sets of novel moments are proposed by using the auto-correlation of wavelet functions and the dual-tree complex wavelet functions. It is well known that the wavelet transform lacks the property of shift invariance. A little shift in the input signal will cause very different output wavelet coefficients. The autocorrelation of wavelet functions and the dual-tree complex wavelet functions, on the other hand, are shift-invariant, which is very important in pattern recognition. Rotation invariance is the major concern in this paper, while translation invariance and scale invariance can be achieved by standard normalization techniques. The Gaussian white noise is added to the noise-free images and the noise levels vary with different signal-to-noise ratios. Experimental results conducted in this paper show that the proposed wavelet-based moments outperform Zernike's moments and the Fourier-wavelet descriptor for pattern recognition under different rotation angles and different noise levels. It can be seen that the proposed wavelet-based moments can do an excellent job even when the noise levels are very high.

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

    OpenAIRE

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

    2013-01-01

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

  1. The improvement of the Morlet wavelet for multi-period analysis of climate data

    Science.gov (United States)

    Yi, Hua; Shu, Hong

    2012-10-01

    The multi-level dynamics of an atmosphere system exhibits temporal structures in different types of climate data. This article addresses two issues in multi-period analysis of climate data. Firstly, the advantages of the modified Morlet wavelet transform (MMWT) for analyzing multi-period structure of time series over Morlet wavelet transform (MWT) are emphasized. Secondly, the multi-period issues of temperature data are studied with MMWT through four steps: the four dominant periods of 60 year temperature data are determined with the wavelet variance; by analyzing the real part of MMWT, the warm and cold stages of the temperature data at different scales are determined, and the time intervals of the warm and cold interchange are singled out; the amplitude of each periodic component is quantitatively characterized by the amplitude of wavelet coefficients; the most intensive oscillation time intervals are computed by the squared modulus of the MMWT (MMPS).

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

    Science.gov (United States)

    Zhao, Jing; Li, Zhongke; Liu, Xingmiao

    2010-10-01

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

  3. Directional wavelet based features for colonic polyp classification.

    Science.gov (United States)

    Wimmer, Georg; Tamaki, Toru; Tischendorf, J J W; Häfner, Michael; Yoshida, Shigeto; Tanaka, Shinji; Uhl, Andreas

    2016-07-01

    In this work, various wavelet based methods like the discrete wavelet transform, the dual-tree complex wavelet transform, the Gabor wavelet transform, curvelets, contourlets and shearlets are applied for the automated classification of colonic polyps. The methods are tested on 8 HD-endoscopic image databases, where each database is acquired using different imaging modalities (Pentax's i-Scan technology combined with or without staining the mucosa), 2 NBI high-magnification databases and one database with chromoscopy high-magnification images. To evaluate the suitability of the wavelet based methods with respect to the classification of colonic polyps, the classification performances of 3 wavelet transforms and the more recent curvelets, contourlets and shearlets are compared using a common framework. Wavelet transforms were already often and successfully applied to the classification of colonic polyps, whereas curvelets, contourlets and shearlets have not been used for this purpose so far. We apply different feature extraction techniques to extract the information of the subbands of the wavelet based methods. Most of the in total 25 approaches were already published in different texture classification contexts. Thus, the aim is also to assess and compare their classification performance using a common framework. Three of the 25 approaches are novel. These three approaches extract Weibull features from the subbands of curvelets, contourlets and shearlets. Additionally, 5 state-of-the-art non wavelet based methods are applied to our databases so that we can compare their results with those of the wavelet based methods. It turned out that extracting Weibull distribution parameters from the subband coefficients generally leads to high classification results, especially for the dual-tree complex wavelet transform, the Gabor wavelet transform and the Shearlet transform. These three wavelet based transforms in combination with Weibull features even outperform the state

  4. Discrete wavelet transform based signal stegnography & encryption

    Directory of Open Access Journals (Sweden)

    Mr. Arun Kumar

    2012-05-01

    Full Text Available Stegnography and signal encryption are the most important tools that provide data and information security by hiding the signal under cover signal. It is usually done through mathematical manipulation of the data with on in comprehensible format for unauthorized user. Some time it is essential to transmit Real Time signal through internet with appreciable confidentiality for preventing unauthorized information access, this is prime consideration for growing use of signal stenography. Proposed algorithm based on Discrete WaveletTransform technique for signal stegnography and one stage of encryption; both methods are used for secure communication Cryptograph which deals with data or signal encryption at sender side and decryption at receiver side [3] with help of key or password, stegnography used for secure data transmission.

  5. 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.

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

    Institute of Scientific and Technical Information of China (English)

    姚丽莎; 赵海峰; 罗斌; 朱珍元

    2012-01-01

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

  7. 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.

  8. Wavelet Packet Based Features for Automatic Script Identification

    Directory of Open Access Journals (Sweden)

    M.C. Padma & P. A. Vijaya

    2010-08-01

    Full Text Available In a multi script environment, an archive of documents printed in different scriptsis in practice. For automatic processing of such documents through OpticalCharacter Recognition (OCR, it is necessary to identify the script type of thedocument. In this paper, a novel texture-based approach is presented to identifythe script type of the collection of documents printed in ten Indian scripts -Bangla, Devanagari, Roman (English, Gujarati, Malayalam, Oriya, Tamil,Telugu, Kannada and Urdu. The document images are decomposed through theWavelet Packet Decomposition using the Haar basis function up to level two.Gray level co-occurrence matrix is constructed for the coefficient sub bands ofthe wavelet transform. The Haralick texture features are extracted from the cooccurrencematrix and then used in the identification of the script of a machineprinted document. Experimentation conducted involved 3000 text images forlearning and 2500 text images for testing. Script classification performance isanalyzed using the K-nearest neighbor classifier. The average success rate isfound to be 98.24%.

  9. Developing de-noising methods for ultrasonic NDT based on wavelet transform and adaptive filtering

    International Nuclear Information System (INIS)

    Digital signal processing methods based on the advanced wavelet transform and adaptive filtering were developed to deal with the problem of material's grain noise in ultrasonic Non Destructive Testing applications. The developed methods were implemented in lab View (Laboratory Virtual Instruments Engineering Workbench) programming environment. The experimental ultrasonic signals were obtained by inspecting stainless steel blocks with side-drilled holes, and carbon steel welded plates contain three types of welding flaws: root crack, centerline crack and slag inclusion. The simulations were carried out using CIVA Non Destructive Evaluation modeling software. Wavelet transform has introduced innovative changes in different fields of science and engineering. One of its important applications is in de-noising of signals and images. Wavelet packet is an efficient de-noising method, which has been used for ultrasonic Non Destructive Testing signals de-noising, wavelet packet is generalizations of the discrete wavelet transform. The first part of this research proposes the use of the un decimated wavelet transform in implementing wavelet packets to overcome the limitation of the shift variance encountered in discrete wavelet transform. Simulations and experiments were carried out on flaw's echo signals contaminated with material's grain noise, various wavelet transform processing parameters were investigated, including the number of decomposition levels, analyzing wavelets, and threshold setting. The results showed superior de-noising effect of the developed method over the conventional one. In the second part of the research, improvements are proposed to the multi-stage adaptive filter, which has been reported in a previous study as an advanced adaptive noise cancellation system for ultrasonic None Destructive Testing applications. The multi stage adaptive filter is limited by the slow convergence speed of the least-mean-squares algorithm as well as

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

    OpenAIRE

    Anjali Patil; M. N. Tibdewal

    2015-01-01

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

  11. Seismic data compression based on integer wavelet transform

    Institute of Scientific and Technical Information of China (English)

    王喜珍; 滕云田; 高孟潭; 姜慧

    2004-01-01

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

  12. 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.

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

    Directory of Open Access Journals (Sweden)

    Anjali Patil

    2015-03-01

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

  14. Watermarking ancient documents based on wavelet packets

    Science.gov (United States)

    Maatouk, Med Neji; Jedidi, Ola; Essoukri Ben Amara, Najoua

    2009-01-01

    The ancient documents present an important part of our individual and collective memory. In addition to their preservation, the digitization of these documents may offer users a great number of services like remote look-up and browsing rare documents. However, the documents, digitally formed, are likely to be modified or pirated. Therefore, we need to develop techniques of protecting images stemming from ancient documents. Watermarking figures to be one of the promising solutions. Nevertheless, the performance of watermarking procedure depends on being neither too robust nor too invisible. Thus, choosing the insertion field or mode as well as the carrier points of the signature is decisive. We propose in this work a method of watermarking images stemming from ancient documents based on wavelet packet decomposition. The insertion is carried out into the maximum amplitude ratio being in the best base of decomposition, which is determined beforehand according to a criterion on entropy. This work is part of a project of digitizing ancient documents in cooperation with the National Library of Tunis (BNT).

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

    Science.gov (United States)

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

    2005-12-01

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

  16. 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.

  17. A pseudo wavelet-based method for accurate tagline tracing on tagged MR images of the tongue

    Science.gov (United States)

    Yuan, Xiaohui; Ozturk, Cengizhan; Chi-Fishman, Gloria

    2006-03-01

    In this paper, we present a pseudo wavelet-based tagline detection method. The tagged MR image is transformed to the wavelet domain, and the prominent tagline coefficients are retained while others are eliminated. Significant stripes are extracted via segmentation, which are mixtures of tags and anatomical boundary that resembles line. A refinement step follows such that broken lines or isolated points are grouped or eliminated. Without assumption on tag models, our method extracts taglines automatically regardless their width and spacing. In addition, founded on the multi-resolution wavelet analysis, our method reconstructs taglines precisely and shows great robustness to various types of taglines.

  18. [ECoG classification based on wavelet variance].

    Science.gov (United States)

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

    2013-06-01

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

  19. 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.

  20. [An algorithm of a wavelet-based medical image quantization].

    Science.gov (United States)

    Hou, Wensheng; Wu, Xiaoying; Peng, Chenglin

    2002-12-01

    The compression of medical image is the key to study tele-medicine & PACS. We have studied the statistical distribution of wavelet subimage coefficients and concluded that the distribution of wavelet subimage coefficients is very much similar to that of Laplacian distribution. Based on the statistical properties of image wavelet decomposition, an image quantization algorithm is proposed. In this algorithm, we selected the sample-standard-deviation as the key quantization threshold in every wavelet subimage. The test has proved that, the main advantages of this algorithm are simple computing and the predictability of coefficients in different quantization threshold range. Also, high compression efficiency can be obtained. Therefore, this algorithm can be potentially used in tele-medicine and PACS. PMID:12561372

  1. 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.  

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

    Institute of Scientific and Technical Information of China (English)

    高清河; 刚晶; 王和禹; 刘海英

    2014-01-01

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

  3. Assembling A Multi-Feature EEG Classifier for Left-Right Motor Imagery Data Using Wavelet-Based Fuzzy Approximate Entropy for Improved Accuracy.

    Science.gov (United States)

    Hsu, Wei-Yen

    2015-12-01

    An EEG classifier is proposed for application in the analysis of motor imagery (MI) EEG data from a brain-computer interface (BCI) competition in this study. Applying subject-action-related brainwave data acquired from the sensorimotor cortices, the system primarily consists of artifact and background removal, feature extraction, feature selection and classification. In addition to background noise, the electrooculographic (EOG) artifacts are also automatically removed to further improve the analysis of EEG signals. Several potential features, including amplitude modulation, spectral power and asymmetry ratio, adaptive autoregressive model, and wavelet fuzzy approximate entropy (wfApEn) that can measure and quantify the complexity or irregularity of EEG signals, are then extracted for subsequent classification. Finally, the significant sub-features are selected from feature combination by quantum-behaved particle swarm optimization and then classified by support vector machine (SVM). Compared with feature extraction without wfApEn on MI data from two data sets for nine subjects, the results indicate that the proposed system including wfApEn obtains better performance in average classification accuracy of 88.2% and average number of commands per minute of 12.1, which is promising in the BCI work applications. PMID:26584583

  4. 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...

  5. 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.

  6. 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.

  7. $\\ell_0$ Minimization for Wavelet Frame Based Image Restoration

    CERN Document Server

    Zhang, Yong; Lu, Zhaosong

    2011-01-01

    The theory of (tight) wavelet frames has been extensively studied in the past twenty years and they are currently widely used for image restoration and other image processing and analysis problems. The success of wavelet frame based models, including balanced approach and analysis based approach, is due to their capability of sparsely approximating piecewise smooth functions like images. Motivated by the balanced approach and analysis based approach, we shall propose a wavelet frame based $\\ell_0$ minimization model, where the $\\ell_0$ "norm" of the frame coefficients is penalized. We adapt the penalty decomposition (PD) method to solve the proposed optimization problem. Numerical results showed that the proposed model solved by the PD method can generate images with better quality than those obtained by either analysis based approach or balanced approach in terms of restoring sharp features as well as maintaining smoothness of the recovered images. Some convergence analysis of the PD method will also be prov...

  8. 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.

  9. 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.

  10. Fast Wavelet-Based Visual Classification

    OpenAIRE

    Yu, Guoshen; Slotine, Jean-Jacques

    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 si...

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

    DEFF Research Database (Denmark)

    Stegmann, Mikkel Bille; Forchhammer, Søren

    2002-01-01

    excessive storage and computational requirements. This paper addresses the problem by modelling the appearance of wavelet coefficient subsets contrary to the pixel intensities. We call this Wavelet Enhanced Appearance Modelling (WHAM). Experiments using the orthogonal Haar wavelet and the bi-orthogonal CDF...... 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....

  12. 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.

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

    DEFF Research Database (Denmark)

    Velarde, Gissel; Meredith, David; Weyde, Tillman

    2016-01-01

    We present a computational method for pattern discovery based on the application of the wavelet transform to symbolic representations of melodies or monophonic voices. We model the importance of a discovered pattern in terms of the compression ratio that can be achieved by using it to describe that...... part of the melody covered by its occurrences. The proposed method resembles that of paradigmatic analysis developed by Ruwet (1966) and Nattiez (1975). In our approach, melodies are represented either as ‘raw’ 1-dimensional pitch signals or as these signals filtered with the continuous wavelet...... transform (CWT) at a single scale using the Haar wavelet. These representations are segmented using various approaches and the segments are then concatenated based on their similarity. The concatenated segments are compared, clustered and ranked. The method was evaluated on two musicological tasks...

  14. 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.

  15. Face Recognition System Based on Spectral Graph Wavelet Theory

    Directory of Open Access Journals (Sweden)

    R. Premalatha Kanikannan

    2014-09-01

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

  16. 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.

  17. 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.

  18. FAST TEXT LOCATION BASED ON DISCRETE WAVELET TRANSFORM

    Institute of Scientific and Technical Information of China (English)

    Li Xiaohua; Shen Lansun

    2005-01-01

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

  19. A new Contrast Based Image Fusion using Wavelet Packets

    CERN Document Server

    Balasubramanian, R

    2008-01-01

    Image Fusion, a technique which combines complimentary information from different images of the same scene so that the fused image is more suitable for segmentation, feature extraction, object recognition and Human Visual System. In this paper, a simple yet efficient algorithm is presented based on contrast using wavelet packet decomposition. First, all the source images are decomposed into low and high frequency sub-bands and then fusion of high frequency sub-bands is done by the means of Directive Contrast. Now, inverse wavelet packet transform is performed to reconstruct the fused image. The performance of the algorithm is carried out by the comparison made between proposed and existing algorithm.

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

    OpenAIRE

    Hong He; Yonghong Tan; Yuexia Wang

    2015-01-01

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

  1. Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery

    Science.gov (United States)

    Zavorin, Ilya; LeMoigne, Jacqueline

    2003-01-01

    The problem of image registration, or alignment of two or more images representing the same scene or object, has to be addressed in various disciplines that employ digital imaging. In the area of remote sensing, just like in medical imaging or computer vision, it is necessary to design robust, fast and widely applicable algorithms that would allow automatic registration of images generated by various imaging platforms at the same or different times, and that would provide sub-pixel accuracy. One of the main issues that needs to be addressed when developing a registration algorithm is what type of information should be extracted from the images being registered, to be used in the search for the geometric transformation that best aligns them. The main objective of this paper is to evaluate several wavelet pyramids that may be used both for invariant feature extraction and for representing images at multiple spatial resolutions to accelerate registration. We find that the band-pass wavelets obtained from the Steerable Pyramid due to Simoncelli perform better than two types of low-pass pyramids when the images being registered have relatively small amount of nonlinear radiometric variations between them. Based on these findings, we propose a modification of a gradient-based registration algorithm that has recently been developed for medical data. We test the modified algorithm on several sets of real and synthetic satellite imagery.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2009-05-15

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

  3. 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...

  4. 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 on...

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

    Directory of Open Access Journals (Sweden)

    Park DongSun

    2010-01-01

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

  6. 3D Wavelet-Based Filter and Method

    Science.gov (United States)

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

    2008-08-12

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

  7. Reducing Ultrasonic Signal Noise by Algorithms based on Wavelet Thresholding

    Directory of Open Access Journals (Sweden)

    M. Kreidl

    2002-01-01

    Full Text Available Traditional techniques for reducing ultrasonic signal noise are based on the optimum frequency of an acoustic wave, ultrasonic probe construction and low-noise electronic circuits. This paper describes signal processing methods for noise suppression using a wavelet transform. Computer simulations of the proposed testing algorithms are presented.

  8. 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.

  9. 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.

  10. 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.

  11. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

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

  12. Fast wavelet based 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.

  13. 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.

  14. Multi-resolution Gabor wavelet feature extraction for needle detection in 3D ultrasound

    Science.gov (United States)

    Pourtaherian, Arash; Zinger, Svitlana; Mihajlovic, Nenad; de With, Peter H. N.; Huang, Jinfeng; Ng, Gary C.; Korsten, Hendrikus H. M.

    2015-12-01

    Ultrasound imaging is employed for needle guidance in various minimally invasive procedures such as biopsy guidance, regional anesthesia and brachytherapy. Unfortunately, a needle guidance using 2D ultrasound is very challenging, due to a poor needle visibility and a limited field of view. Nowadays, 3D ultrasound systems are available and more widely used. Consequently, with an appropriate 3D image-based needle detection technique, needle guidance and interventions may significantly be improved and simplified. In this paper, we present a multi-resolution Gabor transformation for an automated and reliable extraction of the needle-like structures in a 3D ultrasound volume. We study and identify the best combination of the Gabor wavelet frequencies. High precision in detecting the needle voxels leads to a robust and accurate localization of the needle for the intervention support. Evaluation in several ex-vivo cases shows that the multi-resolution analysis significantly improves the precision of the needle voxel detection from 0.23 to 0.32 at a high recall rate of 0.75 (gain 40%), where a better robustness and confidence were confirmed in the practical experiments.

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

    International Nuclear Information System (INIS)

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

  16. 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.

  17. A Wavelet-Based Approach to Fall Detection

    Directory of Open Access Journals (Sweden)

    Luca Palmerini

    2015-05-01

    Full Text Available Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases in order to improve the performance of fall detection algorithms.

  18. 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.

  19. Fan Fault Diagnosis Based on Wavelet Packet and Sample Entropy

    Directory of Open Access Journals (Sweden)

    Xiaogang Xu

    2013-06-01

    Full Text Available To accurately diagnose the mechanical failure of the fan, two diagnostic methods based on the wavelet packet energy feature and sample entropy feature are proposed. Vibration signals acquisition of 13 kinds of running states are achieved on the 4-73 No.8D centrifugal fan test bench. The wavelet packet energy feature vector of each vibration signal is rapidly extracted through the wavelet packet denoising, decomposition and reconstruction. The vibration signal wavelet packet energy feature vector of the five measuring points in the same instantaneous running state are fused into the fan fault feature vector. Finally, the fault diagnosis of the fan is achieved by using improved SVM (Support Vector Machine classifier, and the accuracy rate is 94.6%. A new fan fault feature vector is put forward, which is the integration of the vibration signal sample entropy of the five measuring points in the same instantaneous running state, and then the fault diagnosis of the fan is achieved by using improved BP (Back Propagation neural network, and the accuracy rate is 99.23%. The diagnostic results show that these two methods are able to effectively diagnose the category, severity and site of the fan mechanical failures, and suitable for online diagnosis.

  20. 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.

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

    OpenAIRE

    Jarrett, W. O. B.

    2004-01-01

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

  2. 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

  3. Wavelet-based finite element analysis of composites

    International Nuclear Information System (INIS)

    Full text: Wavelet analysis became recently very popular in the area of composite materials modeling since their multiscale and stochastic nature. Most of the people including engineers, scientists and even ordinary people involved in designing, manufacturing and the usage of composites are usually interested in their global behavior rather than the multiphysical phenomena appearing at different scales of their complicated multilevel structure. Therefore, the most important topic is to build the efficient mathematical and numerical algorithm to analyze multiscale heterogeneous materials and structures. As it is known, thanks to the homogenization theory we can follow essentially two different paths to achieve this goal. First, the composite can be directly analyzed using the wavelet-based FEM approach. Concurrently, we can use the wavelet-based homogenization algorithm to determine effective material parameters and next, to carry out classical FEM or another related method based computations. The basic difference between those approaches is that the wavelet decomposition and construction algorithms are incorporated into the matrix FEM computations in the first method; therefore, the additional computer code should be modified. The second method is based on rather symbolic computations necessary for determination of the effective material parameters, while the structural analysis is classical. The computational strategy presented by the author is based on the homogenization method where the dynamics of the linear elastic heterogeneous beam is studied for the following general case: ∂/∂x (E(x)∂u/∂x) = n(x) ∂2u/∂t2; where both Young modulus E(x) and the composite mass density p(x) are defined by some wavelets. First, the effective material parameters of the beam are determined; then, the structural behavior of the homogenized system is determined numerically and compared against the real structure vibrations. Analogous analysis is done for the composite

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

    OpenAIRE

    Clémençon, Stéphan; Depecker, Marine

    2013-01-01

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

  5. Wavelet-Based Mel-Frequency Cepstral Coefficients for Speaker Identification using Hidden Markov Models

    CERN Document Server

    Abdalla, Mahmoud I

    2010-01-01

    To improve the performance of speaker identification systems, an effective and robust method is proposed to extract speech features, capable of operating in noisy environment. Based on the time-frequency multi-resolution property of wavelet transform, the input speech signal is decomposed into various frequency channels. For capturing the characteristic of the signal, the Mel-Frequency Cepstral Coefficients (MFCCs) of the wavelet channels are calculated. Hidden Markov Models (HMMs) were used for the recognition stage as they give better recognition for the speaker's features than Dynamic Time Warping (DTW). Comparison of the proposed approach with the MFCCs conventional feature extraction method shows that the proposed method not only effectively reduces the influence of noise, but also improves recognition. A recognition rate of 99.3% was obtained using the proposed feature extraction technique compared to 98.7% using the MFCCs. When the test patterns were corrupted by additive white Gaussian noise with 20 d...

  6. Wavelet packet transform-based robust video watermarking technique

    Indian Academy of Sciences (India)

    Gaurav Bhatnagar; Balasubrmanian Raman

    2012-06-01

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

  7. 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.

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

    Science.gov (United States)

    Xu, Yonghong; Li, Xingxing; Zhao, Yong

    2013-10-01

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

  9. 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.

  10. Representation of 1/f signal with wavelet bases

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

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

  11. Double-Wavelet Neuron Based on Analytical Activation Functions

    OpenAIRE

    Bodyanskiy, Yevgeniy; Lamonova, Nataliya; Vynokurova, Olena

    2007-01-01

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

  12. 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.

  13. WAVELET-BASED WARPING TECHNIQUE FOR MOBILE DEVICES

    Directory of Open Access Journals (Sweden)

    Ekta Walia

    2014-07-01

    Full Text Available The role of digital images is increasing rapidly in mobile devices. They are used in many applications including virtual tours, virtual reality, e-commerce etc. Such applications synthesize realistic looking novel views of the reference images on mobile devices using the techniques like image-based rendering (IBR. However, with this increasing role of digital images comes the serious issue of processing large images which requires considerable time. Hence, methods to compress these large images are very important. Wavelets are excellent data compression tools that can be used with IBR algorithms to generate the novel views of compressed image data. This paper proposes a framework that uses wavelet-based warping technique to render novel views of compressed images on mobile/ handheld devices. The experiments are performed using Android Development Tools (ADT which shows the proposed framework gives better results for large images in terms of rendering time.

  14. 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).

  15. Face Recognition Approach Based on Wavelet - Curvelet Technique

    Directory of Open Access Journals (Sweden)

    Muzhir Shaban Al-Ani

    2012-04-01

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

  16. Fast wavelet based algorithms for linear evolution equations

    Science.gov (United States)

    Engquist, Bjorn; Osher, Stanley; Zhong, Sifen

    1992-01-01

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

  17. Embedded wavelet-based face recognition under variable position

    Science.gov (United States)

    Cotret, Pascal; Chevobbe, Stéphane; Darouich, Mehdi

    2015-02-01

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

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

    OpenAIRE

    Beegan, Andrew Peter

    2001-01-01

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

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

    OpenAIRE

    Mousa Kadhim Wali; Murugappan Murugappan; Badlishah Ahmmad

    2013-01-01

    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 ba...

  20. Wavelet Based Resolution Enhancement for Low Resolution Satellite Images

    OpenAIRE

    Garg, Akansha; Vardhan Naidu, Sashi; Yahia, Hussein; Singh, Darmendra

    2014-01-01

    Satellite images play a major role in the analysis of land cover, topographic analysis, geosciences etc. There has always existed a tradeoff between the image resolution and the image cost. In this paper, a modified discrete wavelet transform and interpolation based technique is proposed for enhancing the resolution of satellite images having low resolution in such a way that a highly resolved satellite image can be obtained without losing any image information. The advent of DWT has given a ...

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

    International Nuclear Information System (INIS)

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

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

    Institute of Scientific and Technical Information of China (English)

    HUANG Wei; LU Xiaobo; LING Xiaojing

    2005-01-01

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

  3. A 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.

  4. Generating Optimized Decision Tree Based on Discrete Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Kiran Kumar Reddi

    2010-03-01

    Full Text Available Increasing growth of functionality in current IT trends proved the decision making operations through mass data mining techniques. There is still a requirement for further efficiency and optimization. The problem of constructing the optimization decision tree is now an active research area. Generating an efficient and optimized decision tree with multi-attribute data source is considered as one of the shortcomings. This paper emphasizes to propose a multivariate statistical method Discrete Wavelet Transform on multi-attribute data for reducing dimensionality and to transform traditional decision tree algorithm to form a new algorithmic model. The experimental results described that this method can not only optimizes the structure of the decision tree, but also improves the problems existing in pruning and to mine the better rule set without effecting the purpose of prediction accuracy altogether.

  5. 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.

  6. 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.

  7. 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.

  8. Wavelet-based gray-level digital image watermarking

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

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

  9. 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.

  10. An automatic system for Turkish word recognition using Discrete Wavelet Neural Network based on adaptive entropy

    International Nuclear Information System (INIS)

    In this paper, an automatic system is presented for word recognition using real Turkish word signals. This paper especially deals with combination of the feature extraction and classification from real Turkish word signals. A Discrete Wavelet Neural Network (DWNN) model is used, which consists of two layers: discrete wavelet layer and multi-layer perceptron. The discrete wavelet layer is used for adaptive feature extraction in the time-frequency domain and is composed of Discrete Wavelet Transform (DWT) and wavelet entropy. The multi-layer perceptron used for classification is a feed-forward neural network. The performance of the used system is evaluated by using noisy Turkish word signals. Test results showing the effectiveness of the proposed automatic system are presented in this paper. The rate of correct recognition is about 92.5% for the sample speech signals. (author)

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

    Science.gov (United States)

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

    2016-05-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-07-01

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

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

    Science.gov (United States)

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

    2009-01-01

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

  14. 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.

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

    OpenAIRE

    Bhowmik, D.; Abhayaratne, C.

    2009-01-01

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

  16. Optimal Wavelets for Speech Signal Representations

    Directory of Open Access Journals (Sweden)

    Shonda L. Walker

    2003-08-01

    Full Text Available It is well known that in many speech processing applications, speech signals are characterized by their voiced and unvoiced components. Voiced speech components contain dense frequency spectrum with many harmonics. The periodic or semi-periodic nature of voiced signals lends itself to Fourier Processing. Unvoiced speech contains many high frequency components and thus resembles random noise. Several methods for voiced and unvoiced speech representations that utilize wavelet processing have been developed. These methods seek to improve the accuracy of wavelet-based speech signal representations using adaptive wavelet techniques, superwavelets, which uses a linear combination of adaptive wavelets, gaussian methods and a multi-resolution sinusoidal transform approach to mention a few. This paper addresses the relative performance of these wavelet methods and evaluates the usefulness of wavelet processing in speech signal representations. In addition, this paper will also address some of the hardware considerations for the wavelet methods presented.

  17. 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...

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

    Institute of Scientific and Technical Information of China (English)

    郑亚强

    2014-01-01

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

  19. A Regression Analysis Model Based on Wavelet Networks

    Institute of Scientific and Technical Information of China (English)

    XIONG Zheng-feng

    2002-01-01

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

  20. 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.

  1. MRA-based wavelet frames and applications: image segmentation and surface reconstruction

    Science.gov (United States)

    Dong, Bin; Shen, Zuowei

    2012-06-01

    Theory of wavelet frames and their applications to image restoration problems have been extensively studied for the past two decades. The success of wavelet frames in solving image restoration problems, which includes denoising, deblurring, inpainting, computed tomography, etc., is mainly due to their capability of sparsely approximating piecewise smooth functions such as images. However, in contrast to the wide applications of wavelet frame based approaches to image restoration problems, they are rarely used for some image/data analysis tasks, such as image segmentation, registration and surface reconstruction from unorganized point clouds. The main reason for this is the lack of geometric interpretations of wavelet frames and their associated transforms. Recently, geometric meanings of wavelet frames have been discovered and connections between the wavelet frame based approach and the differential operator based variational model were established.1 Such discovery enabled us to extend the wavelet frame based approach to some image/data analysis tasks that have not yet been studied before. In this paper, we will provide a unified survey of the wavelet frame based models for image segmentation and surface reconstruction from unorganized point clouds. Advantages of the wavelet frame based approach are illustrated by numerical experiments.

  2. 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.

  3. Wavelet-based detection of transients in biological signals

    Science.gov (United States)

    Mzaik, Tahsin; Jagadeesh, Jogikal M.

    1994-10-01

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

  4. 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.

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

    OpenAIRE

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

    2014-01-01

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

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

    OpenAIRE

    K. Daqrouq; A. Dobaie

    2016-01-01

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

  7. An ECG signal compressor based on the selection of optimal threshold levels of discrete wavelet transform coefficients.

    Science.gov (United States)

    Al-Ajlouni, A F; Abo-Zahhad, M; Ahmed, S M; Schilling, R J

    2008-01-01

    Compression of electrocardiography (ECG) is necessary for efficient storage and transmission of the digitized ECG signals. Discrete wavelet transform (DWT) has recently emerged as a powerful technique for ECG signal compression due to its multi-resolution signal decomposition and locality properties. This paper presents an ECG compressor based on the selection of optimum threshold levels of DWT coefficients in different subbands that achieve maximum data volume reduction while preserving the significant signal morphology features upon reconstruction. First, the ECG is wavelet transformed into m subbands and the wavelet coefficients of each subband are thresholded using an optimal threshold level. Thresholding removes excessively small features and replaces them with zeroes. The threshold levels are defined for each signal so that the bit rate is minimized for a target distortion or, alternatively, the distortion is minimized for a target compression ratio. After thresholding, the resulting significant wavelet coefficients are coded using multi embedded zero tree (MEZW) coding technique. In order to assess the performance of the proposed compressor, records from the MIT-BIH Arrhythmia Database were compressed at different distortion levels, measured by the percentage rms difference (PRD), and compression ratios (CR). The method achieves good CR values with excellent reconstruction quality that compares favourably with various classical and state-of-the-art ECG compressors. Finally, it should be noted that the proposed method is flexible in controlling the quality of the reconstructed signals and the volume of the compressed signals by establishing a target PRD and a target CR a priori, respectively. PMID:19005960

  8. 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.

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

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

    Science.gov (United States)

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

    2012-08-01

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

  15. 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 ...

  16. 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.

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

    OpenAIRE

    Gang Chen; Dawei Zhao

    2013-01-01

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

  18. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-01

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

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

    International Nuclear Information System (INIS)

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

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

    Institute of Scientific and Technical Information of China (English)

    辛芳芳; 焦李成; 王桂婷; 万红林

    2012-01-01

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

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

    Science.gov (United States)

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

    2016-08-01

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

  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

    2016-01-01

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

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

    International Nuclear Information System (INIS)

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

  6. 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.

  7. An image adaptive, wavelet-based watermarking of digital images

    Science.gov (United States)

    Agreste, Santa; Andaloro, Guido; Prestipino, Daniela; Puccio, Luigia

    2007-12-01

    In digital management, multimedia content and data can easily be used in an illegal way--being copied, modified and distributed again. Copyright protection, intellectual and material rights protection for authors, owners, buyers, distributors and the authenticity of content are crucial factors in solving an urgent and real problem. In such scenario digital watermark techniques are emerging as a valid solution. In this paper, we describe an algorithm--called WM2.0--for an invisible watermark: private, strong, wavelet-based and developed for digital images protection and authenticity. Using discrete wavelet transform (DWT) is motivated by good time-frequency features and well-matching with human visual system directives. These two combined elements are important in building an invisible and robust watermark. WM2.0 works on a dual scheme: watermark embedding and watermark detection. The watermark is embedded into high frequency DWT components of a specific sub-image and it is calculated in correlation with the image features and statistic properties. Watermark detection applies a re-synchronization between the original and watermarked image. The correlation between the watermarked DWT coefficients and the watermark signal is calculated according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has shown to be resistant against geometric, filtering and StirMark attacks with a low rate of false alarm.

  8. 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.

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

    OpenAIRE

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

    2010-01-01

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

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

    Science.gov (United States)

    Padma, S; Hariharan, G

    2016-06-01

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

  11. 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.

  12. An FPGA-based rapid prototyping platform for wavelet coprocessors

    Science.gov (United States)

    Vera, Alonzo; Meyer-Baese, Uwe; Pattichis, Marios

    2007-04-01

    MatLab/Simulink-based design flows are being used by DSP designers to improve time-to-market of FPGA implementations. 1 Commonly, digital signal processing cores are integrated in an embedded system as coprocessors. Existing CAD tools do not fully address the integration of a DSP coprocessor into an embedded system design. This integration might prove to be time consuming and error prone. It also requires that the DSP designer has an excellent knowledge of embedded systems and computer architecture details. We present a prototyping platform and design flow that allows rapid integration of embedded systems with a wavelet coprocessor. The platform comprises of software and hardware modules that allow a DSP designer a painless integration of a coprocessor with a PowerPC-based embedded system. The platform has a wide range of applications, from industrial to educational environments.

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

    Science.gov (United States)

    Mahdavi, Seyed Hossein; Razak, Hashim Abdul

    2016-06-01

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

  14. 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 ...

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

    Institute of Scientific and Technical Information of China (English)

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

    2009-01-01

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

  16. Experimental wavelet based denoising for indoor infrared wireless communications.

    Science.gov (United States)

    Rajbhandari, Sujan; Ghassemlooy, Zabih; Angelova, Maia

    2013-06-01

    This paper reports the experimental wavelet denoising techniques carried out for the first time for a number of modulation schemes for indoor optical wireless communications in the presence of fluorescent light interference. The experimental results are verified using computer simulations, clearly illustrating the advantage of the wavelet denoising technique in comparison to the high pass filtering for all baseband modulation schemes. PMID:23736631

  17. 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.

  18. 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.

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

    Institute of Scientific and Technical Information of China (English)

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

    2004-01-01

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

  20. 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.

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

    Institute of Scientific and Technical Information of China (English)

    YIN Ming; LIU Wei; KONG Ranran

    2012-01-01

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

  2. Certain problems concerning wavelets and wavelets packets

    International Nuclear Information System (INIS)

    Wavelets is the outcome of the synthesis of ideas that have emerged in different branches of science and technology, mainly in the last decade. The concept of wavelet packets, which are superpositions of wavelets, has been introduced a couple of years ago. They form bases which retain many properties of wavelets like orthogonality, smoothness and localization. The Walsh orthornomal system is a special case of wavelet packet. The wavelet packets provide at our disposal a library of orthonormal bases, each of which can be used to analyze a given signal of finite energy. The optimal choice is decided by the entropy criterion. In the present paper we discuss results concerning convergence, coefficients, and approximation of wavelet packets series in general and wavelets series in particular. Wavelet packet techniques for solutions of differential equations are also mentioned. (author). 117 refs

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

    Institute of Scientific and Technical Information of China (English)

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

    2012-01-01

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

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

    Science.gov (United States)

    Ganjefar, Soheil; Tofighi, Morteza; Karami, Hamidreza

    2015-11-01

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

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

    Science.gov (United States)

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

    2016-05-01

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

  6. 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.

  7. Phase space reconstruction of chaotic dynamical system based on wavelet decomposition

    International Nuclear Information System (INIS)

    In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system. (general)

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

    CERN Document Server

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

    2010-01-01

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

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

    OpenAIRE

    Na Wu; Yinjing Guo; Yongqin Wei; Shuxian Fan; Xuehua Li

    2013-01-01

    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 ...

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

    Science.gov (United States)

    Cifter, Atilla

    2011-06-01

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

  11. Wavelet based time-frequency and multifractal analysis of the glow discharge plasma instabilities

    CERN Document Server

    Nurujjaman, Md; Iyengar, A N Sekar

    2009-01-01

    Wavelet based ridge patterns and multifractal analysis have been carried out of the anode glow related floating potential fluctuations of a glow discharge plasma. Wavelet based ridge plots and multifractal spectrum were constructed for the fluctuations for different discharge voltages. Presence of the strong and weak chaos has been explained qualitatively from the ridge plots and from the multifractal spectrum different quantities like correlation and fractal dimension, degree of multifractality, etc., have been estimated at different discharge voltages. Wavelet based analysis is very much consistent with nonlinear time series analysis.

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Wu Xiang

    2014-01-01

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

  14. 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.

  15. Wavelet-based Image Enhancement Using Fourth Order PDE

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren

    2011-01-01

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

  16. 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.

  17. 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.

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

    International Nuclear Information System (INIS)

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

  19. 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

  20. Analysis of Efficient Wavelet Based Volumetric Image Compression

    Directory of Open Access Journals (Sweden)

    Krishna Kumar

    2012-04-01

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

  1. 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.

  2. 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...

  3. 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...

  4. 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.

  5. Wavelet-based acoustic emission detection method with adaptive thresholding

    Science.gov (United States)

    Menon, Sunil; Schoess, Jeffrey N.; Hamza, Rida; Busch, Darryl

    2000-06-01

    Reductions in Navy maintenance budgets and available personnel have dictated the need to transition from time-based to 'condition-based' maintenance. Achieving this will require new enabling diagnostic technologies. One such technology, the use of acoustic emission for the early detection of helicopter rotor head dynamic component faults, has been investigated by Honeywell Technology Center for its rotor acoustic monitoring system (RAMS). This ambitious, 38-month, proof-of-concept effort, which was a part of the Naval Surface Warfare Center Air Vehicle Diagnostics System program, culminated in a successful three-week flight test of the RAMS system at Patuxent River Flight Test Center in September 1997. The flight test results demonstrated that stress-wave acoustic emission technology can detect signals equivalent to small fatigue cracks in rotor head components and can do so across the rotating articulated rotor head joints and in the presence of other background acoustic noise generated during flight operation. This paper presents the results of stress wave data analysis of the flight-test dataset using wavelet-based techniques to assess background operational noise vs. machinery failure detection results.

  6. A Wavelet Based Algorithm for the Identification of Oscillatory Event-Related Potential Components

    OpenAIRE

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

    2014-01-01

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

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

    OpenAIRE

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

    2003-01-01

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

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

    Science.gov (United States)

    Leblanc, George E.; Ferguson, Stephen

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

  9. ICER-3D: A Progressive Wavelet-Based Compressor for Hyperspectral Images

    Science.gov (United States)

    Kiely, A.; Klimesh, M.; Xie, H.; Aranki, N.

    2005-01-01

    ICER-3D is a progressive, wavelet-based compressor for hyperspectral images. ICER-3D is derived from the ICER image compressor. ICER-3D can provide lossless and lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The three-dimensional wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of hyperspectral data sets, while facilitating elimination of spectral ringing artifacts. Correlation is further exploited by a context modeler that effectively exploits spectral dependencies in the wavelet-transformed hyperspectral data. Performance results illustrating the benefits of these features are presented.

  10. 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.

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

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

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

  13. 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.

  14. Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization

    Science.gov (United States)

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

    2016-08-01

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

  15. Classification of Histological Images Based on the Stationary Wavelet Transform

    International Nuclear Information System (INIS)

    Non-Hodgkin lymphomas are of many distinct types, and different classification systems make it difficult to diagnose them correctly. Many of these systems classify lymphomas only based on what they look like under a microscope. In 2008 the World Health Organisation (WHO) introduced the most recent system, which also considers the chromosome features of the lymphoma cells and the presence of certain proteins on their surface. The WHO system is the one that we apply in this work. Herewith we present an automatic method to classify histological images of three types of non-Hodgkin lymphoma. Our method is based on the Stationary Wavelet Transform (SWT), and it consists of three steps: 1) extracting sub-bands from the histological image through SWT, 2) applying Analysis of Variance (ANOVA) to clean noise and select the most relevant information, 3) classifying it by the Support Vector Machine (SVM) algorithm. The kernel types Linear, RBF and Polynomial were evaluated with our method applied to 210 images of lymphoma from the National Institute on Aging. We concluded that the following combination led to the most relevant results: detail sub-band, ANOVA and SVM with Linear and RBF kernels

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

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

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

  17. 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.

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

    Science.gov (United States)

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

    2015-10-01

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

  19. A Wavelet-Based Approach for Ultrasound Image Restoration

    Directory of Open Access Journals (Sweden)

    Mohammed Tarek GadAllah

    2014-08-01

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

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

    OpenAIRE

    Fei Chen; Yuan-Ting Zhang

    2003-01-01

    DNA sequence analysis is of great significance for increasing our understanding of genomic functions. An important task facing us is the exploration of hidden structural information stored in the DNA sequence. This paper introduces a DNA structure-based adaptive wavelet transform (WT) – the bionic wavelet transform (BWT) – for DNA sequence analysis. The symbolic DNA sequence can be separated into four channels of indicator sequences. An adaptive symbol-to-number mapping, determined from the s...

  1. 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.

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

    OpenAIRE

    Turunen, Jukka

    2011-01-01

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

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

    OpenAIRE

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

    2012-01-01

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

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

    OpenAIRE

    Qiaoning Yang; Jianlin Wang

    2016-01-01

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

  5. Wavelet Analysis on Turbulent Structure in Drag-Reducing Channel Flow Based on Direct Numerical Simulation

    OpenAIRE

    Xuan Wu; Bo Yu(Brookhaven National Lab); Yi Wang

    2013-01-01

    Direct numerical simulation has been performed to study a polymer drag-reducing channel flow by using a discrete-element model. And then, wavelet analyses are employed to investigate the multiresolution characteristics of velocity components based on DNS data. Wavelet decomposition is applied to decompose velocity fluctuation time series into ten different frequency components including approximate component and detailed components, which show more regular intermittency and burst events in dr...

  6. A Parallel Wavelet-Based Algebraic Multigrid Black-Box Solver and Preconditioner

    OpenAIRE

    Fabio Henrique Pereira; Sílvio Ikuyo Nabeta

    2012-01-01

    This work introduces a new parallel wavelet-based algorithm for algebraic multigrid method (PWAMG) using a variation of the standard parallel implementation of discrete wavelet transforms. This new approach eliminates the grid coarsening process in traditional algebraic multigrid setup phase simplifying its implementation on distributed memory machines. The PWAMG method is used as a parallel black-box solver and as a preconditioner in some linear equations systems resulti...

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

    Directory of Open Access Journals (Sweden)

    Huaqing Wang

    2012-03-01

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

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

    Science.gov (United States)

    Rizal Isnanto, R.

    2015-06-01

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

  9. 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.)

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

    International Nuclear Information System (INIS)

    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.)

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

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

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

  12. 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...

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

    Institute of Scientific and Technical Information of China (English)

    Hu Haifeng; Yang Zhen; Bao Jianmin

    2012-01-01

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

  14. 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.

  15. 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...

  16. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

    Science.gov (United States)

    Feng, Xiao; Li, Qi; Zhu, Yajie; Hou, Junxiong; Jin, Lingyan; Wang, Jingjie

    2015-04-01

    In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of "dirty" air and "clean" air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries' air quality forecasting systems.

  17. 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.

  18. 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...

  19. 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.

  20. Wavelet-based coherence measures of global seismic noise properties

    Science.gov (United States)

    Lyubushin, A. A.

    2015-04-01

    The coherent behavior of four parameters characterizing the global field of low-frequency (periods from 2 to 500 min) seismic noise is studied. These parameters include generalized Hurst exponent, multifractal singularity spectrum support width, the normalized entropy of variance, and kurtosis. The analysis is based on the data from 229 broadband stations of GSN, GEOSCOPE, and GEOFON networks for a 17-year period from the beginning of 1997 to the end of 2013. The entire set of stations is subdivided into eight groups, which, taken together, provide full coverage of the Earth. The daily median values of the studied noise parameters are calculated in each group. This procedure yields four 8-dimensional time series with a time step of 1 day with a length of 6209 samples in each scalar component. For each of the four 8-dimensional time series, a multiple correlation measure is estimated, which is based on computing robust canonical correlations for the Haar wavelet coefficients at the first detail level within a moving time window of the length 365 days. These correlation measures for each noise property demonstrate essential increasing starting from 2007 to 2008 which was continued till the end of 2013. Taking into account a well-known phenomenon of noise correlation increasing before catastrophes, this increasing of seismic noise synchronization is interpreted as indicators of the strongest (magnitudes not less than 8.5) earthquakes activation which is observed starting from the Sumatra mega-earthquake of 26 Dec 2004. This synchronization continues growing up to the end of the studied period (2013), which can be interpreted as a probable precursor of the further increase in the intensity of the strongest earthquakes all over the world.

  1. 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.

  2. 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.

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

    Directory of Open Access Journals (Sweden)

    Fangyuan Nan

    2008-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2003-07-07

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

  5. 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.

  6. 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...

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

    DEFF Research Database (Denmark)

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

    2006-01-01

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

  8. Wavelet Based Feature Extraction for Clustering of Be Stars

    Czech Academy of Sciences Publication Activity Database

    Bromová, P.; Škoda, Petr; Zendulka, J.

    Vol. 210. Cham : Springer, 2013, s. 467-474 ISBN 978-3-319-00541-6. ISSN 2194-5357. [Nostradamus 2013. Ostrava (CZ), 03.06.2013-05.06.2013] Institutional support: RVO:67985815 Keywords : feature extraction * stellar spectra * wavelet transform Subject RIV: BN - Astronomy, Celestial Mechanics, Astrophysics

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

    Czech Academy of Sciences Publication Activity Database

    Bartušek, Karel; Přinosil, J.; Smékal, Z.

    2011-01-01

    Roč. 20, č. 1 (2011), s. 85-93. ISSN 1210-2512 R&D Projects: GA ČR GA102/09/0314 Institutional research plan: CEZ:AV0Z20650511 Keywords : wavelet transformation * filtering technique * magnetic resonance imaging Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 0.739, year: 2011

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Hong He

    2015-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Lixin Gao

    2010-05-01

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

  13. 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. PMID:22399894

  14. Wavelet Packet Based Features for Automatic Script Identification

    OpenAIRE

    M.C. Padma & P. A. Vijaya

    2010-01-01

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

  15. Characteristics of a class of vector-valued non-separable higher-dimensional wavelet packet bases

    International Nuclear Information System (INIS)

    In this paper, we introduce vector-valued non-separable higher-dimensional wavelet packets with an arbitrary integer dilation factor. An approach for constructing vector-valued higher-dimensional wavelet packet bases is proposed. Their characteristics are investigated by means of harmonic analysis method, matrix theory and operator theory, and three orthogonality formulas concerning the wavelet packets are presented. Orthogonal decomposition relation formulas of the space L2(Rn)p are derived by designing a series of subspaces of the vector-valued wavelet packets. Moreover, several orthonormal wavelet packet bases of L2(Rn)p are constructed from the wavelet packets. Relation to some physical theories such as E-infinity theory and multifractal theory is also discussed.

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

    Institute of Scientific and Technical Information of China (English)

    陈夏; 胡红利; 张娟; 周屈兰

    2012-01-01

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

  17. An Adaptive Energy Management System for Electric Vehicles Based on Driving Cycle Identification and Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Qiao Zhang

    2016-05-01

    Full Text Available Since driving cycle greatly affects load power demand, driving cycle identification (DCI is proposed to predict power demand that can be expected to prepare for the power distribution between battery and supercapacitor. The DCI is developed based on a learning vector quantization (LVQ neural network method, which is assessed in both training and validation based on the statistical data obtained from six standard driving cycles. In order to ensure network accuracy, characteristic parameter and slide time window, which are two important factors ensuring the network accuracy for onboard hybrid energy storage system (HESS applications in electric vehicles, are discussed and designed. Based on the identification results, Multi-level Haar wavelet transform (Haar-WT is proposed for allocating the high frequency components of power demand into the supercapacitor which could damage battery lifetime and the corresponding low frequency components into the battery system. The proposed energy management system can better increase system efficiency and battery lifetime compared with the conventional sole frequency control. The advantages are demonstrated based on a randomly generated driving cycle from the standard driving cycle library via simulation.

  18. 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.

  19. 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.  

  20. 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.

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

    CERN Document Server

    Rosu, H C; Ludu, A

    2013-01-01

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

  2. 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...

  3. 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.

  4. Wavelet Based EMG Artifact Removal From ECG Signal

    OpenAIRE

    Joy, Josy; Manimegalai, P

    2013-01-01

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

  5. Analysis and Comparison of Medical Image Fusion Techniques: Wavelet based Transform and Contourlet based Transform

    Directory of Open Access Journals (Sweden)

    C G Ravichandran

    2011-03-01

    Full Text Available Medical Image Fusion provides additional information for diagnosis when a registered and overlaid multiple patient images. Multiple images from the same imaging modality or multiple modalities can be used to create a fused image. The fused image is of immense help and provides more information to the doctor so as to diagnose the diseases. The CT (Computer Tomography offers less detailed information for soft tissues and good information on bony structures. But the MRI (Magnetic Resonance Imaging provides a more detailed information on the soft tissues but less detailed information on bone structures and the contrast resolution of soft tissues is far better in MRI than in CT. In this paper, Wavelet based Transform like DWT (Discrete Wavelet Transform and CWT (Complex Wavelet Transform is analyzed theoretically and it is compared with Contourlet based Transform like CCT (Complex Contourlet Transform and NSCT (No-Subsampled Contourlet Transform. The experimental results showing the evaluation measures like IE (Information Entrophy , AG (Average Gradient and SD (Standard Deviation.

  6. 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.

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

    Science.gov (United States)

    Yan, Wenhong; Jiang, Ning

    2015-09-01

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

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

    DEFF Research Database (Denmark)

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

    2006-01-01

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

  9. Time-Frequency Distribution of Music based on Sparse Wavelet Packet Representations

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft

    the minimization methods basis pursuit and best orthogonal basis. Visualizations of the time-frequency distribution are constructed based on a simplified energy distribution in the wavelet packet decomposition. The time-frequency distributions emphasizes structured musical content, including non......-stationary content, by masking the energy from less structured music instruments. We present four examples for visualizing structured content, including vocal and single instrument.......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 using...

  10. 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.

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

    Science.gov (United States)

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

    2014-08-15

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

  12. 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.

  13. A New Method of Reliability Evaluation Based on Wavelet Information Entropy for Equipment Condition Identification

    International Nuclear Information System (INIS)

    Aiming at reliability evaluation of condition identification of mechanical equipment, it is necessary to analyze condition monitoring information. A new method of reliability evaluation based on wavelet information entropy extracted from vibration signals of mechanical equipment is proposed. The method is quite different from traditional reliability evaluation models that are dependent on probability statistics analysis of large number sample data. The vibration signals of mechanical equipment were analyzed by means of second generation wavelet package (SGWP). We take relative energy in each frequency band of decomposed signal that equals a percentage of the whole signal energy as probability. Normalized information entropy (IE) is obtained based on the relative energy to describe uncertainty of a system instead of probability. The reliability degree is transformed by the normalized wavelet information entropy. A successful application has been achieved to evaluate the assembled quality reliability for a kind of dismountable disk-drum aero-engine. The reliability degree indicates the assembled quality satisfactorily.

  14. 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...

  15. Modeling multivariate volatility using wavelet-based realized covariance estimator

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Vácha, Lukáš

    Prague: Proffesional publishing, 2011, s. 29-34. ISBN 978-80-7431-058-4. [Mathematical Methods in Economics 2011. Janská Dolina (SK), 06.09.2011-09.09.2011] R&D Projects: GA ČR GAP402/10/1610; GA ČR GA402/09/0965; GA ČR GD402/09/H045 Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate realized volatility * covariation * jumps * wavelets Subject RIV: AH - Economics

  16. 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.

  17. 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.

  18. An accurate tongue tissue strain synthesis using pseudo-wavelet reconstruction-based tagline detection

    Science.gov (United States)

    Yuan, Xiaohui; Ozturk, Cengizhan; Chi-Fishman, Gloria

    2007-03-01

    This paper describe our work on tagline detection and tissue strain synthesis. The tagline detection method extends our previous work 16 using pseudo-wavelet reconstruction. The novelty in tagline detection is that we integrated an active contour model and successfully improved the detection and indexing performance. Using pseudo-wavelet reconstruction-based method, prominent wavelet coefficients were retained while others were eliminated. Taglines were then extracted from the reconstructed images using thresholding. Due to noise and artifacts, a tagline can be broken into segments. We employed an active contour model that tracks the most likely segments and bridges them. Experiments demonstrated that our method extracts taglines automatically with greater robustness. Tissue strain was also reconstructed using extracted taglines.

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

    Directory of Open Access Journals (Sweden)

    Richard J. Harris

    2012-10-01

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

  20. Application of threshold estimation for terahertz digital holography image denoising based on stationary wavelet transform

    Science.gov (United States)

    Cui, Shan-shan; Li, Qi; Ma, Xue

    2015-11-01

    Terahertz digital holography imaging technology is one of the hot topics in imaging domain, and it has drawn more and more public attention. Owing to the redundancy and translation invariance of the stationary wavelet transform, it has significant application in image denoising, and the threshold selection has a great influence on denoising. The denoising researches based on stationary wavelet transform are performed on the real terahertz image, with Bayesian estimation and Birge-Massart strategy applied to evaluate the threshold. The experimental results reveal that, Bayesian estimation combined with homomorphic stationary wavelet transform manifests the optimal denoising effect at 3 decomposition levels, which improves the signal-to-noise and preserves the image detail information simultaneously.

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

    Science.gov (United States)

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

    2016-09-01

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

  2. Target recognition by wavelet transform

    CERN Document Server

    Li Zheng Dong; He Wu Liang; Pei Chun Lan; Peng Wen; SongChen; Zheng Xiao Dong

    2002-01-01

    Wavelet transform has an important character of multi-resolution power, which presents pyramid structure, and this character coincides the way by which people distinguish object from coarse to fineness and from large to tiny. In addition to it, wavelet transform benefits to reducing image noise, simplifying calculation, and embodying target image characteristic point. A method of target recognition by wavelet transform is provided

  3. Target recognition by wavelet transform

    International Nuclear Information System (INIS)

    Wavelet transform has an important character of multi-resolution power, which presents pyramid structure, and this character coincides the way by which people distinguish object from coarse to fineness and from large to tiny. In addition to it, wavelet transform benefits to reducing image noise, simplifying calculation, and embodying target image characteristic point. A method of target recognition by wavelet transform is provided

  4. 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.

  5. 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.

  6. Wavelet-based multifractal analysis of dynamic infrared thermograms to assist in early breast cancer diagnosis

    Directory of Open Access Journals (Sweden)

    AlainArneodo

    2014-05-01

    Full Text Available Breast cancer is the most common type of cancer among women and despite recent advances in the medical field, there are still some inherent limitations in the currently used screening techniques. The radiological interpretation of screening X-ray mammograms often leads to over-diagnosis and, as a consequence, to unnecessary traumatic and painful biopsies. Here we propose a computer-aided multifractal analysis of dynamic infrared (IR imaging as an efficient method for identifying women with risk of breast cancer. Using a wavelet-based multi-scale method to analyze the temporal fluctuations of breast skin temperature collected from a panel of patients with diagnosed breast cancer and some female volunteers with healthy breasts, we show that the multifractal complexity of temperature fluctuations observed in healthy breasts is lost in mammary glands with malignant tumor. Besides potential clinical impact, these results open new perspectives in the investigation of physiological changes that may precede anatomical alterations in breast cancer development.

  7. Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

    Science.gov (United States)

    Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

    2014-12-01

    The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies--both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the

  8. 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.

  9. 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.    

  10. Wavelets in soft computing

    CERN Document Server

    Thuillard, Marc

    2001-01-01

    This book presents the state of integration of wavelet theory and multiresolution analysis into soft computing. It is the first book on hybrid methods combining wavelet analysis with fuzzy logic, neural networks or genetic algorithms. Much attention is given to new approaches (fuzzy-wavelet) that permit one to develop, using wavelet techniques, linguistically interpretable fuzzy systems from data. The book also introduces the reader to wavelet-based genetic algorithms and multiresolution search. A special place is given to methods that have been implemented in real world applications, particul

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

    Science.gov (United States)

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

    2016-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Guang Lu

    2013-03-01

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

  13. 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.

  14. SECURE VISUAL SECRET SHARING BASED ON DISCRETE WAVELET TRANSFORM

    Directory of Open Access Journals (Sweden)

    S. Jyothi Lekshmi

    2015-08-01

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

  15. Wavelet-based fractal analysis of airborne pollen

    Science.gov (United States)

    Degaudenzi, M. E.; Arizmendi, C. M.

    1999-06-01

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

  16. 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.

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

    Science.gov (United States)

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

    2014-10-01

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

  18. 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.

  19. A Vibration Analysis Based on Wavelet Entropy Method of a Scroll Compressor

    Directory of Open Access Journals (Sweden)

    Tao Liu

    2015-10-01

    Full Text Available Vibration-based condition monitoring and fault diagnosis is an effective approach to maintain the reliable operation of a scroll compressor. Unfortunately, the vibration signal from the scroll compressor always has characteristics of being non-linear and non-stationary, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on Wavelet entropy is proposed in this paper. Two forms of the wavelet entropy, namely the wavelet space feature spectrum entropy (WSFSE and the wavelet energy spectrum entropy (WESE, are defined to depict instantaneous characteristics of the local variation of the vibration signal. Four types of mechanical faulty vibration signal, namely unbalanced rotor, malfunctioning scroll, loosened mechanical assembly, and loosened bearing, are analyzed by using the proposed approach. The experimental results show that feature components and energy distribution of each fault signal is accurately identified and revealed, which proves that the combined application of WSFSE and WESE approach is a successful scheme for the vibration analysis of scroll compressors.

  20. 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 ...

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

    Directory of Open Access Journals (Sweden)

    H. M. Abdul-Ridha

    2008-01-01

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

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

    OpenAIRE

    H. M. Abdul-Ridha; Abduladhem Abdulkareem Ali

    2008-01-01

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

  3. [Analysis of spectral characteristics of oil film on water based on wavelet transform].

    Science.gov (United States)

    Li, Ying; Liu, Bing-Xin; Li, Bao-Yu; Chen, Duo

    2012-07-01

    The diagnostic features are the basis to detect and characterize the oil film on water through optical remote sensing. This work shows the results of lab spectral measurements of light diesel oil with thickness ranged 1.0 - 127 microm. A wavelet transform were performed to the reflectance, and the singularity (388-393 nm) was explored as the indicators of oil film thickness. The results indicate that the reflectance of light diesel oil film is higher than that of water in the range from 350-2 500 nm. There is a reflectance peak near 388 nm when the thickness of oil film is larger than 6 microm, however, no distinguished features could be recognized when oil films were thinner than 6 microm. The wavelet coefficients of the fifth decomposition level by applying Daubechies 4 (db4) mother wavelets proved successful for identifying the singularity of oil film's reflectance spectra and its accurate position. With the thickness lager than 6 microm, the detail coefficients performed an abrupt change within the range of 388-393 nm, and became more violent while oil films' thickness increased. This research demonstrated that oil films on water with different thickness could be distinguished based on wavelet detail coefficients, with important implications for detection of oils on water using UV and short wave optical remote sensing. PMID:23016354

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

    Directory of Open Access Journals (Sweden)

    Victoria Eugenia Montes

    2010-07-01

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

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

    Science.gov (United States)

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

    2011-01-01

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

  6. Comparison of neuron selection algorithms of wavelet-based neural network

    Science.gov (United States)

    Mei, Xiaodan; Sun, Sheng-He

    2001-09-01

    Wavelet networks have increasingly received considerable attention in various fields such as signal processing, pattern recognition, robotics and automatic control. Recently people are interested in employing wavelet functions as activation functions and have obtained some satisfying results in approximating and localizing signals. However, the function estimation will become more and more complex with the growth of the input dimension. The hidden neurons contribute to minimize the approximation error, so it is important to study suitable algorithms for neuron selection. It is obvious that exhaustive search procedure is not satisfying when the number of neurons is large. The study in this paper focus on what type of selection algorithm has faster convergence speed and less error for signal approximation. Therefore, the Genetic algorithm and the Tabu Search algorithm are studied and compared by some experiments. This paper first presents the structure of the wavelet-based neural network, then introduces these two selection algorithms and discusses their properties and learning processes, and analyzes the experiments and results. We used two wavelet functions to test these two algorithms. The experiments show that the Tabu Search selection algorithm's performance is better than the Genetic selection algorithm, TSA has faster convergence rate than GA under the same stopping criterion.

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

    Institute of Scientific and Technical Information of China (English)

    康健; 林云; 杜浩; 李婧

    2015-01-01

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

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

    Science.gov (United States)

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

    2005-11-01

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

  10. Controlling halo-chaos via wavelet-based feedback

    Directory of Open Access Journals (Sweden)

    Geng Zhao

    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.

  11. 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.

  12. Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography Images Using Wavelet Based Statistical Texture Features

    CERN Document Server

    Padma, A

    2011-01-01

    The research work presented in this paper is to achieve the tissue classification and automatically diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet based statistical texture analysis method. Comparative studies of texture analysis method are performed for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm (GA) is used to select the optimal texture features from the set of extracted texture features. We construct the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by comparing the classification results of the SVM based classifier with the Back Propagation Neural network classifier(BPN...

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

    OpenAIRE

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

    2010-01-01

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

  14. A Vibration Analysis Based on Wavelet Entropy Method of a Scroll Compressor

    OpenAIRE

    Tao Liu; Zaixin Wu

    2015-01-01

    Vibration-based condition monitoring and fault diagnosis is an effective approach to maintain the reliable operation of a scroll compressor. Unfortunately, the vibration signal from the scroll compressor always has characteristics of being non-linear and non-stationary, which makes vibration signal analysis and fault feature extraction very difficult. To extract the significant fault features, a vibration analysis method based on Wavelet entropy is proposed in this paper. Two forms of the wav...

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

    OpenAIRE

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

    2009-01-01

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

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

    OpenAIRE

    Ines Kahloul; Anouar Ben Mabrouk; Slah-Eddine Hallara

    2010-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    余辉; 谢远国; 周仲兴; 吕扬生

    2004-01-01

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

  18. A linear quality control design for high efficient wavelet-based ECG data compression.

    Science.gov (United States)

    Hung, King-Chu; Tsai, Chin-Feng; Ku, Cheng-Tung; Wang, Huan-Sheng

    2009-05-01

    In ECG data compression, maintaining reconstructed signal with desired quality is crucial for clinical application. In this paper, a linear quality control design based on the reversible round-off non-recursive discrete periodized wavelet transform (RRO-NRDPWT) is proposed for high efficient ECG data compression. With the advantages of error propagation resistance and octave coefficient normalization, RRO-NRDPWT enables the non-linear quantization control to obtain an approximately linear distortion by using a single control variable. Based on the linear programming, a linear quantization scale prediction model is presented for the quality control of reconstructed ECG signal. Following the use of the MIT-BIH arrhythmia database, the experimental results show that the proposed system, with lower computational complexity, can obtain much better quality control performance than that of other wavelet-based systems. PMID:19070935

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps

    Science.gov (United States)

    Janusek, D.; Kania, M.; Zaczek, R.; Zavala-Fernandez, H.; Zbieć, A.; Opolski, G.; Maniewski, R.

    2011-01-01

    T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant T-wave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected.

  4. Research on electrocardiogram baseline wandering correction based on wavelet transform, QRS barycenter fitting, and regional method.

    Science.gov (United States)

    Song, Jinzhong; Yan, Hong; Li, Yanjun; Mu, Kaiyu

    2010-09-01

    Baseline wandering in electrocardiogram (ECG) is one of the biggest interferences in visualization and computerized detection of waveforms (especially ST-segment) based on threshold decision. A new method based on wavelet transform, QRS barycenter fitting and regional method was proposed in this paper. Firstly, wavelet transform as a coarse correction was used to remove the baseline wandering, whose frequency bands were non-overlapping with that of ST-segment. Secondly, QRS barycenter fitting was applied as a detailed correction. The third, the regional method was used to transfer baseline to zero. Finally, the method in this paper was proved to perform better than filtering and function fitting methods in baseline wandering correction after the long-term ST database (LTST) verification. In addition, the proposed method is simple and easy to carry out, and in current use. PMID:20882381

  5. 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.

  6. 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.

  7. 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.

  8. Conjugate Event Study of Geomagnetic ULF Pulsations with Wavelet-based Indices

    Science.gov (United States)

    Xu, Z.; Clauer, C. R.; Kim, H.; Weimer, D. R.; Cai, X.

    2013-12-01

    The interactions between the solar wind and geomagnetic field produce a variety of space weather phenomena, which can impact the advanced technology systems of modern society including, for example, power systems, communication systems, and navigation systems. One type of phenomena is the geomagnetic ULF pulsation observed by ground-based or in-situ satellite measurements. Here, we describe a wavelet-based index and apply it to study the geomagnetic ULF pulsations observed in Antarctica and Greenland magnetometer arrays. The wavelet indices computed from these data show spectrum, correlation, and magnitudes information regarding the geomagnetic pulsations. The results show that the geomagnetic field at conjugate locations responds differently according to the frequency of pulsations. The index is effective for identification of the pulsation events and measures important characteristics of the pulsations. It could be a useful tool for the purpose of monitoring geomagnetic pulsations.

  9. 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.

  10. A wavelet-based method to remove spatial autocorrelation in the analysis of species distributional data

    Directory of Open Access Journals (Sweden)

    G. Carl

    2008-04-01

    Full Text Available Species distributional data based on lattice data often display spatial autocorrelation. In such cases, the assumption of independently and identically distributed errors can be violated in standard regression models. Based on a recently published review on methods to account for spatial autocorrelation, we describe here a new statistical approach which relies on the theory of wavelets. It provides a powerful tool for removing spatial autocorrelation without any prior knowledge of the underlying correlation structure. Our wavelet-revised model (WRM is applied to artificial datasets of species’ distributions, for both presence/absence (binary response and species abundance data (Poisson or normally distributed response. Making use of these published data enables us to compare WRM to other recently tested models and to recommend it as an attractive option for effective and computationally efficient autocorrelation removal.

  11. Internal fault current identification based on wavelet transform in power transformers

    Energy Technology Data Exchange (ETDEWEB)

    Monsef, H. [ECE Department, Faculty of Engineering, University of Tehran, Center of Excellence on Applied Electromagnetic Systems, Tehran (Iran); Lotfifard, S. [Department of Electrical and Computer Engineering, University of Tehran, Tehran (Iran)

    2007-10-15

    This paper presents a novel approach for differential protection of power transformers. This method uses wavelet transform (WT) and adaptive network-based fuzzy inference system (ANFIS) to discriminate internal faults from inrush currents. The proposed method has been designed based on the differences between amplitudes of wavelet transform coefficients in a specific frequency band generated by faults and inrush currents. The performance of this algorithm is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. Also the proposed algorithm is tested off-line using data collected from a prototype laboratory three-phase power transformer. The test results show that the new algorithm is very quick and accurate. (author)

  12. A Haar-wavelet-based Lucy-Richardson algorithm for positron emission tomography image restoration

    International Nuclear Information System (INIS)

    Deconvolution is an ill-posed problem that requires regularization. Noise would inevitably be enhanced during the iterative deconvolution process. The enhanced noise degrades the image quality, causing mistakes in clinical interpretations. This paper introduced a Haar-wavelet-based Lucy-Richardson algorithm (HALU) for positron emission tomography (PET) image restoration based on a spatially variant point spread function. After wavelet decomposition, Lucy-Richardson algorithm was applied to each approximation matrix with different iteration numbers. Thus, this enhanced the contrasts of our images without amplifying much of the noise level. The results showed that HALU can be able to recover the resolution and yield better contrast and lower noise level than the Lucy-Richardson algorithm.

  13. Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy

    Science.gov (United States)

    Zhang, Xin; Feng, Naizhang; Wang, Yan; Shen, Yi

    2015-03-01

    In order to detect cracks in railroad tracks, various experiments have been examined by Acoustic Emission (AE) method. However, little work has been done on studying rail defect detection at high speed. This paper presents a study on AE detection of rail defect at high speed based on rail-wheel test rig. Meanwhile, Wavelet Transform and Shannon entropy are employed to detect defects. Signals with and without defects are acquired, and characteristic frequencies from them at different speeds are analyzed. Based on appropriate decomposition level and Energy-to-Shannon entropy ratio, the optimal wavelet is selected. In order to suppress noise effects and ensure appropriate time resolution, the length of time window is investigated. Further, the characteristic frequency of time window is employed to detect defect. The results clearly illustrate that the proposed method can detect rail defect at high speed effectively.

  14. 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.

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

    Science.gov (United States)

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

    2016-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Kartik V. Bulusu

    2015-09-01

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

  17. A Chaos Based Blind Digital Image Watermarking in The Wavelet Transform Domain

    OpenAIRE

    Jila Ayubi; Shahram Mohanna; Farahnaz Mohanna; Mehdi Rezaei

    2011-01-01

    An effective watermarking algorithm based on the chaotic maps and the discrete wavelet transform for gray scale images is proposed. In the algorithm, the chaotic maps are employed to generate a key space with the length of 1040 numbers to increase the degree of security. Additionally, the XOR operator has been replaced by the mutation process for embedding the images. This improved mutation operator has been used to encrypt the watermark logo. Additionally, the upgraded mapping method determi...

  18. WAVELET-BASED 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.

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

    OpenAIRE

    Bo Chen; Zhi-wei Chen; Gan-jun Wang; Wei-ping Xie

    2014-01-01

    The sudden stiffness reduction in a structure may cause the signal discontinuity in the acceleration responses close to the damage location at the damage time instant. To this end, the damage detection on sudden stiffness reduction of building structures has been actively investigated in this study. The signal discontinuity of the structural acceleration responses of an example building is extracted based on the discrete wavelet transform. It is proved that the variation of the first level de...

  20. Wavelet-based fast time-resolved magnetic sensing with electronic spins in diamond

    OpenAIRE

    Xu, Nanyang; Jiang, Fengjian; Tian, Yu(School of Physics, University of Chinese Academy of Sciences, Beijing, 100049, China); Ye, Jianfeng; Shi, Fazhan; Lv, Haijiang; Wang, Ya; Wrachtrup, Jorg; Du, Jiangfeng

    2015-01-01

    Time-resolved magnetic sensing is of great importance from fundamental studies to applications in physical and biological sciences. Recently the nitrogen-vacancy (NV) defect center in diamond has been developed as a promising sensor of magnetic field under ambient conditions. However the methods to reconstruct time-resolved magnetic field with high sensitivity are not yet fully developed. Here, we propose and demonstrate a novel sensing method based on spin echo, and Haar wavelet transform. O...

  1. 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.

  2. Application of Set Pair Analysis-Based Similarity Forecast Model and Wavelet Denoising for Runoff Forecasting

    OpenAIRE

    Chien-Ming Chou

    2014-01-01

    This study presents the application of a set pair analysis-based similarity forecast (SPA-SF) model and wavelet denoising to forecast annual runoff. The SPA-SF model was built from identical, discrepant and contrary viewpoints. The similarity between estimated and historical data can be obtained. The weighted average of the annual runoff values characterized by the highest connection coefficients was regarded as the predicted value of the estimated annual runoff. In addition, runoff time seri...

  3. Impulse Response Identification Based on Varying Scale Orthogonal Wavelet Packet Transform

    Institute of Scientific and Technical Information of China (English)

    LIHe-Sheng; MAOJian-Qin; ZHAOMing-Sheng

    2005-01-01

    In this paper, by applying a group of specific orthogonal wavelet packet to Eykhoff algorithm, a new impulse response identification algorithm based on varying scale orthogonal WPT is provided. In comparison to Eykhoff algorithm, the new algorithm has better practicability and wider application range. Simulation results show that the proposed impulse response identification algorithm can be applied to both deterministic and random systems, and is of higher identification precision, stronger anti-noise interference ability and better system dynamic tracking property.

  4. ECG Signals Classification Based on Wavelet Transform and Probabilistic Neural Networks

    OpenAIRE

    Iman Moazen; Mohamadreza Ahmadzadeh

    2009-01-01

    In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Network (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the histogram of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decom...

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

    OpenAIRE

    Victoria Eugenia Montes; Gustavo A Guarín; Germán Castellanos Domínguez

    2010-01-01

    Different extraction methods were compared regarding the characteristics of normal ECG signals and those emitted in the presence of events related to ischemic cardiopathy based on diagnosis measurements, wavelet transformation and nonlinear analysis of main components. Methods were developed for automatic recognition between normal and ischemic ECG signals. Two effective feature selection techniques were proposed; one used multivariate statistical methods and the second univariate ones. Linea...

  6. 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.

  7. 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.

  8. Autocorrelation based denoising of manatee vocalizations using the undecimated discrete wavelet transform.

    Science.gov (United States)

    Gur, Berke M; Niezrecki, Christopher

    2007-07-01

    Recent interest in the West Indian manatee (Trichechus manatus latirostris) vocalizations has been primarily induced by an effort to reduce manatee mortality rates due to watercraft collisions. A warning system based on passive acoustic detection of manatee vocalizations is desired. The success and feasibility of such a system depends on effective denoising of the vocalizations in the presence of high levels of background noise. In the last decade, simple and effective wavelet domain nonlinear denoising methods have emerged as an alternative to linear estimation methods. However, the denoising performances of these methods degrades considerably with decreasing signal-to-noise ratio (SNR) and therefore are not suited for denoising manatee vocalizations in which the typical SNR is below 0 dB. Manatee vocalizations possess a strong harmonic content and a slow decaying autocorrelation function. In this paper, an efficient denoising scheme that exploits both the autocorrelation function of manatee vocalizations and effectiveness of the nonlinear wavelet transform based denoising algorithms is introduced. The suggested wavelet-based denoising algorithm is shown to outperform linear filtering methods, extending the detection range of vocalizations. PMID:17614478

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

    International Nuclear Information System (INIS)

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

  10. 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...

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. A Chaos Based Blind Digital Image Watermarking in The Wavelet Transform Domain

    Directory of Open Access Journals (Sweden)

    Jila Ayubi

    2011-07-01

    Full Text Available An effective watermarking algorithm based on the chaotic maps and the discrete wavelet transform for gray scale images is proposed. In the algorithm, the chaotic maps are employed to generate a key space with the length of 1040 numbers to increase the degree of security. Additionally, the XOR operator has been replaced by the mutation process for embedding the images. This improved mutation operator has been used to encrypt the watermark logo. Additionally, the upgraded mapping method determines the location of, DWT, Discrete Wavelet Transform, coefficients where the watermark is embedded. To evaluate the robustness and effectiveness of the proposed method, the effect of several attacks has been simulated on the hidden image. Simulation results indicate that the new algorithm can preserve the hidden information against geometric and non geometric attacks.

  17. Wavelet-Based Color Pathological Image Watermark through Dynamically Adjusting the Embedding Intensity

    Science.gov (United States)

    Liu, Guoyan; Liu, Hongjun; Kadir, Abdurahman

    2012-01-01

    This paper proposes a new dynamic and robust blind watermarking scheme for color pathological image based on discrete wavelet transform (DWT). The binary watermark image is preprocessed before embedding; firstly it is scrambled by Arnold cat map and then encrypted by pseudorandom sequence generated by robust chaotic map. The host image is divided into n × n blocks, and the encrypted watermark is embedded into the higher frequency domain of blue component. The mean and variance of the subbands are calculated, to dynamically modify the wavelet coefficient of a block according to the embedded 0 or 1, so as to generate the detection threshold. We research the relationship between embedding intensity and threshold and give the effective range of the threshold to extract the watermark. Experimental results show that the scheme can resist against common distortions, especially getting advantage over JPEG compression, additive noise, brightening, rotation, and cropping. PMID:23243463

  18. Image Edge Detection Using Hidden Markov Chain Model Based on the Non-decimated Wavelet

    Directory of Open Access Journals (Sweden)

    Renqi Zhang

    2009-03-01

    Full Text Available Edge detection plays an important role in digital image processing. Based on the non-decimated wavelet which is shift invariant, in this paper, we develop a new edge detection technique using Hidden Markov Chain (HMC model. With this proposed model (NWHMC, each wavelet coefficient contains a hidden state, herein, we adopt Laplacian model and Gaussian model to represent the information of the state “big” and the state “small”. The model can be trained by EM algorithm, and then we employ Viterbi algorithm to reveal the hidden state of each coefficient according to MAP estimation. The detecting results of several images are provided to evaluate the algorithm. In addition, the algorithm can be applied to noisy images efficiently.

  19. A 64-channel neural signal processor/ compressor based on Haar wavelet transform.

    Science.gov (United States)

    Shaeri, Mohammad Ali; Sodagar, Amir M; Abrishami-Moghaddam, Hamid

    2011-01-01

    A signal processor/compressor dedicated to implantable neural recording microsystems is presented. Signal compression is performed based on Haar wavelet. It is shown in this paper that, compared to other mathematical transforms already used for this purpose, compression of neural signals using this type of wavelet transform can be of almost the same quality, while demanding less circuit complexity and smaller silicon area. Designed in a 0.13-μm standard CMOS process, the 64-channel 8-bit signal processor reported in this paper occupies 113 μm x 110 μm of silicon area. It operates under a 1.8-V supply voltage at a master clock frequency of 3.2 MHz. PMID:22255805

  20. Fusion Based Gaussian noise Removal in the Images using Curvelets and Wavelets with Gaussian Filter

    Directory of Open Access Journals (Sweden)

    Naga Sravanthi Kota, G.Umamaheswara Reddy

    2011-10-01

    Full Text Available Denoising images using Curvelet transform approach has been widely used in many fields for itsability to obtain high quality images. Curvelet transform is superior to wavelet in the expression ofimage edge, such as geometry characteristic of curve, which has been already obtained goodresults in image denoising. However artifacts those appear in the result images of Curveletsapproach prevent its application in some fields such as medical image. This paper puts forward afusion based method using both Wavelets and Curvelet transforms because certain regions of theimage have the ringing and radial stripe after Curvelets transform. The experimental resultsindicate that fusion method has an abroad future for eliminating the noise of images. The resultsof the algorithm applied to ultrasonic medical images indicate that the algorithm can be usedefficiently in medical image fields also.

  1. Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Sindhumol S

    2013-06-01

    Full Text Available In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA, to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

  2. Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

    International Nuclear Information System (INIS)

    The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done

  3. Wavelet-based fast time-resolved magnetic sensing with electronic spins in diamond

    Science.gov (United States)

    Xu, Nanyang; Jiang, Fengjian; Tian, Yu; Ye, Jianfeng; Shi, Fazhan; Lv, Haijiang; Wang, Ya; Wrachtrup, Jörg; Du, Jiangfeng

    2016-04-01

    Time-resolved magnetic sensing is of great importance from fundamental studies to applications in physical and biological sciences. Recently, the nitrogen-vacancy defect center in diamond has been developed as a promising sensor of magnetic fields under ambient conditions. However, methods to reconstruct time-resolved magnetic fields with high sensitivity are not yet fully developed. Here, we propose and demonstrate a sensing method based on spin echo and Haar wavelet transformation. Our method is exponentially faster in reconstructing time-resolved magnetic fields with comparable sensitivity than existing methods. It is also easier to implement in experiments. Furthermore, the wavelet's unique features enable our method to extract information from the whole signal with only part of the measuring sequences. We then explore this feature for a fast detection of simulated nerve impulses. These results will be useful to time-resolved magnetic sensing with quantum probes at nanoscale.

  4. Difference between healthy children and ADHD based on wavelet spectral analysis of nuclear magnetic resonance images

    Energy Technology Data Exchange (ETDEWEB)

    González Gómez Dulce, I., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Moreno Barbosa, E., E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Hernández, Mario Iván Martínez, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com; Méndez, José Ramos, E-mail: isabeldgg@hotmail.com, E-mail: emoreno@fcfm.buap.mx, E-mail: mim@fcfm.buap.mx, E-mail: joserm84@gmail.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. (Mexico); Silvia, Hidalgo Tobón [Hospital Infantil de México, Federico Gómez, Mexico DF. Mexico and Physics Department, Universidad Autónoma Metropolitana. Iztapalapa, Mexico DF. (Mexico); Pilar, Dies Suarez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez, E-mail: pilydies@yahoo.com, E-mail: neurodoc@prodigy.net.mx [Hospital Infantil de México, Federico Gómez, Mexico DF. (Mexico); Benito, De Celis Alonso, E-mail: benileon@yahoo.com [Faculty of Physics and Mathematics, BUAP, Puebla, Pue. Mexico and Fundación para el Desarrollo Carlos Sigüenza. Puebla, Pue (Mexico)

    2014-11-07

    The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (p<0.0015) between groups. This difference might help in the future to distinguish healthy from ADHD patients and therefore diagnose ADHD. Even if results were statistically significant, the small size of the sample limits the applicability of this methods as it is presented here, and further work with larger samples and using freely available datasets must be done.

  5. Wavelet-Based Poisson Solver for Use in Particle-in-Cell Simulations

    CERN Document Server

    Terzic, Balsa; Mihalcea, Daniel; Pogorelov, Ilya V

    2005-01-01

    We report on a successful implementation of a wavelet-based Poisson solver for use in 3D particle-in-cell simulations. One new aspect of our algorithm is its ability to treat the general (inhomogeneous) Dirichlet boundary conditions. The solver harnesses advantages afforded by the wavelet formulation, such as sparsity of operators and data sets, existence of effective preconditioners, and the ability simultaneously to remove numerical noise and further compress relevant data sets. Having tested our method as a stand-alone solver on two model problems, we merged it into IMPACT-T to obtain a fully functional serial PIC code. We present and discuss preliminary results of application of the new code to the modelling of the Fermilab/NICADD and AES/JLab photoinjectors.

  6. 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.

  7. Extraction of failure characteristic of rolling element bearing based on wavelet transform under strong noise

    Institute of Scientific and Technical Information of China (English)

    ZHANG Hui; WANG Shu-juan

    2005-01-01

    There has been a lot of research has been performed regarding diagnosing rolling element bearing faults using wavelet analysis, but almost all methods are not ideal for picking up fault signal characteristics under strong noise. Therefore, this paper proposes auto-correlation, cross-correlation and weighted average fault diagnosis methods based on wavelet transform (WT) de-noising which combine correlation analysis with WT for the first time. These three methods compute the auto-correlation, the cross-correlation and the weighted average of the measured vibration signals, then de-noise by thresholding and computing the auto-correlation of de-noised coefficients of WT and FFT of energy sequence. The simulation results indicate that all methods enhance the capabilities of fault diagnosis of rolling bearings and pick up the fault characteristics effectively.

  8. A Novel VLSI Architecture for Real-Time Line-Based Wavelet Transform Using Lifting Scheme

    Institute of Scientific and Technical Information of China (English)

    Kai Liu; Ke-Yan Wang; Yun-Song Li; Cheng-Ke Wu

    2007-01-01

    In this paper, we propose a VLSI architecture that performs the line-based discrete wavelet transform (DWT) using a lifting scheme. The architecture consists of row processors, column processors, an intermediate buffer and a control module. Row processor and Column processor work as the horizontal and vertical filters respectively.Intermediate buffer is composed of five FIFOs to store temporary results of horizontal filter. Control module schedules the output order to external memory. Compared with existing ones, the presented architecture parallelizes all levels of wavelet transform to compute multilevel DWT within one image transmission time, and uses no external but one intermediate buffer to store several line results of horizontal filtering, which decreases resource required significantly and reduces memory efficiently. This architecture is suitable for various real-time image/video applications.

  9. 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.

  10. Wavelet-based adaptive numerical simulation of unsteady 3D flow around a bluff body

    Science.gov (United States)

    de Stefano, Giuliano; Vasilyev, Oleg

    2012-11-01

    The unsteady three-dimensional flow past a two-dimensional bluff body is numerically simulated using a wavelet-based method. The body is modeled by exploiting the Brinkman volume-penalization method, which results in modifying the governing equations with the addition of an appropriate forcing term inside the spatial region occupied by the obstacle. The volume-penalized incompressible Navier-Stokes equations are numerically solved by means of the adaptive wavelet collocation method, where the non-uniform spatial grid is dynamically adapted to the flow evolution. The combined approach is successfully applied to the simulation of vortex shedding flow behind a stationary prism with square cross-section. The computation is conducted at transitional Reynolds numbers, where fundamental unstable three-dimensional vortical structures exist, by well-predicting the unsteady forces arising from fluid-structure interaction.

  11. 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.

  12. 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 ...

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

    Directory of Open Access Journals (Sweden)

    K. Daqrouq

    2016-01-01

    Full Text Available An investigation of the electrocardiogram (ECG signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF obtained from the percentage energy (PE of terminal wavelet packet transform (WPT subsignals. In addition, the average framing percentage energy (AFE technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD, logarithmic difference signal ratio (LDSR, and correlation coefficient (CC. The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN environment, where the recognition rate was 81.48% for 5 dB.

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

    Science.gov (United States)

    Daqrouq, K; Dobaie, A

    2016-01-01

    An investigation of the electrocardiogram (ECG) signals and arrhythmia characterization by wavelet energy is proposed. This study employs a wavelet based feature extraction method for congestive heart failure (CHF) obtained from the percentage energy (PE) of terminal wavelet packet transform (WPT) subsignals. In addition, the average framing percentage energy (AFE) technique is proposed, termed WAFE. A new classification method is introduced by three confirmation functions. The confirmation methods are based on three concepts: percentage root mean square difference error (PRD), logarithmic difference signal ratio (LDSR), and correlation coefficient (CC). The proposed method showed to be a potential effective discriminator in recognizing such clinical syndrome. ECG signals taken from MIT-BIH arrhythmia dataset and other databases are utilized to analyze different arrhythmias and normal ECGs. Several known methods were studied for comparison. The best recognition rate selection obtained was for WAFE. The recognition performance was accomplished as 92.60% accurate. The Receiver Operating Characteristic curve as a common tool for evaluating the diagnostic accuracy was illustrated, which indicated that the tests are reliable. The performance of the presented system was investigated in additive white Gaussian noise (AWGN) environment, where the recognition rate was 81.48% for 5 dB. PMID:26949412

  15. ECG Signal Compression Technique Based on Discrete Wavelet Transform and QRS-Complex Estimation

    Directory of Open Access Journals (Sweden)

    Ahmed Zakaria

    2010-07-01

    Full Text Available In this paper, an Electrocardiogram (ECG signal is compressed based on discrete wavelet transform (DWT and QRS-complex estimation. The ECG signal is preprocessed by normalization and mean removal. Then, an error signal is formed as the difference between the preprocessed ECG signal and the estimated QRS-complex waveform. This error signal is wavelet transformed and the resulting wavelet coefficients are thresholded by setting to zero all coefficients that are smaller than certain threshold levels. The threshold levels of all subbands are calculated based on Energy Packing Efficiency (EPE such that minimum percentage root mean square difference (PRD and maximum compression ratio (CR are obtained. The resulted thresholded DWT coefficients are coded using the coding technique given in [1], [20]. The compression algorithm was implemented and tested upon records selected from the MIT - BIH arrhythmia database [2]. Simulation results show that the proposed algorithm leads to high CR associated with low distortion level relative to previously reported compression algorithms [1], [14] and [18]. For example, the compression of record 100 using the proposed algorithm yields to CR = 25.15 associated with PRD = 0.7% and PSNR = 45 dB. This achieves compression rate of nearly 128 bit/sec. The main features of this compression algorithm are the high efficiency, high speed and simplicity in design.

  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. 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.

  18. 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.

  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. A wavelets-based analysis of the phillips curve hypothesis for the Brazilian economy, 1980-2011

    OpenAIRE

    Edgard Almeida Pimentel

    2013-01-01

    This paper implements a wavelets-based analysis of the Phillips curve hypothesis — as formulated by Friedman and Phelps — for the Brazilian economy, concerning the last thirty years. We provide an introductory discussion on Phillips curve's main arguments and an exploratory data analysis for the variables under consideration: prices, unemployment and real wages. In the sequel, we estimate variances and correlation structures between these aggregates through wavelets. Our findings reject the P...

  1. 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.

  2. Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures

    OpenAIRE

    Kohei Arai; Rosa Andrie Asmara

    2014-01-01

    Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.

  3. Gender Classification Method Based on Gait Energy Motion Derived from Silhouette Through Wavelet Analysis of Human Gait Moving Pictures

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2014-02-01

    Full Text Available Gender classification method based on Gait Energy Motion: GEM derived through wavelet analysis of human gait moving pictures is proposed. Through experiments with human gait moving pictures, it is found that the extracted features of wavelet coefficients using silhouettes images are useful for improvement of gender classification accuracy. Also, it is found that the proposed gender classification method shows the best classification performance, 97.63% of correct classification ratio.

  4. An efficient wavelet based approximation method for a few second order differential equations arising in science and engineering

    OpenAIRE

    Padma, S; G. Hariharan; Kannan, K.

    2013-01-01

    A new wavelet based approximation method for solving the second order differential equations arising science and engineering is presented in this paper. Such differential equation is often applied to model phenomena in various fields of science and engineering. In this study, shifted second kind Chebyshev wavelet (CW) operational matrices of derivatives is introduced and applied for solvingthe second order differential equations with various initial conditions. The key idea for getting the nu...

  5. Analysis of hydrological trend for radioactivity content in bore-hole water samples using wavelet based denoising

    International Nuclear Information System (INIS)

    A wavelet transform based denoising methodology has been applied to detect the presence of any discernable trend in 137Cs and 90Sr activity levels in bore-hole water samples collected four times a year over a period of eight years, from 2002 to 2009, in the vicinity of typical nuclear facilities inside the restricted access zones. The conventional non-parametric methods viz., Mann–Kendall and Spearman rho, along with linear regression when applied for detecting the linear trend in the time series data do not yield results conclusive for trend detection with a confidence of 95% for most of the samples. The stationary wavelet based hard thresholding data pruning method with Haar as the analyzing wavelet was applied to remove the noise present in the same data. Results indicate that confidence interval of the established trend has significantly improved after pre-processing to more than 98% compared to the conventional non-parametric methods when applied to direct measurements. -- Highlights: ► Environmental trend analysis with wavelet pre-processing was carried out. ► Removal of local fluctuations to obtain the trend in a time series with various mother wavelets. ► Theoretical validation of the methodology with model outputs. ► Efficient detection of trend for 137Cs, 90Sr in bore-hole water samples improves the associated confidence interval to more than 98%. ► Wavelet based pre-processing reduces the indecisive nature of the detected trend

  6. Research on fault diagnosis method for rotating machinery vibration based on wavelet transformation and probabilistic neutral network

    International Nuclear Information System (INIS)

    Based on wavelet transformation and Neural Network Data Fusion, a Fault Diagnosis Technology is proposed. Fault feature extraction is carried out using wavelet decomposition, probabilistic neural network fault diagnosis technologies by optimizing the selection, and by the MATLAB Simulation, The simulation and results verify that using wavelet decomposition extract fault characteristics of the energy vector, which has strong generalization ability and anti-noise ability to adapt to Wide dynamic range and small sample, and building the adaptive probabilistic neural network is a good anti-noise capability, classification advantage of the high rate of diagnostic accuracy. Integration of the wavelet and neural network application will provide a better classification of diagnosis results, and better reliability and accuracy. (authors)

  7. A wavelet based neural model to optimize and read out a temporal population code

    Directory of Open Access Journals (Sweden)

    Andre Luvizotto

    2012-05-01

    wavelet based decoders.

  8. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    International Nuclear Information System (INIS)

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  9. An adaptive demodulation approach for bearing fault detection based on adaptive wavelet filtering and spectral subtraction

    Science.gov (United States)

    Zhang, Yan; Tang, Baoping; Liu, Ziran; Chen, Rengxiang

    2016-02-01

    Fault diagnosis of rolling element bearings is important for improving mechanical system reliability and performance. Vibration signals contain a wealth of complex information useful for state monitoring and fault diagnosis. However, any fault-related impulses in the original signal are often severely tainted by various noises and the interfering vibrations caused by other machine elements. Narrow-band amplitude demodulation has been an effective technique to detect bearing faults by identifying bearing fault characteristic frequencies. To achieve this, the key step is to remove the corrupting noise and interference, and to enhance the weak signatures of the bearing fault. In this paper, a new method based on adaptive wavelet filtering and spectral subtraction is proposed for fault diagnosis in bearings. First, to eliminate the frequency associated with interfering vibrations, the vibration signal is bandpass filtered with a Morlet wavelet filter whose parameters (i.e. center frequency and bandwidth) are selected in separate steps. An alternative and efficient method of determining the center frequency is proposed that utilizes the statistical information contained in the production functions (PFs). The bandwidth parameter is optimized using a local ‘greedy’ scheme along with Shannon wavelet entropy criterion. Then, to further reduce the residual in-band noise in the filtered signal, a spectral subtraction procedure is elaborated after wavelet filtering. Instead of resorting to a reference signal as in the majority of papers in the literature, the new method estimates the power spectral density of the in-band noise from the associated PF. The effectiveness of the proposed method is validated using simulated data, test rig data, and vibration data recorded from the transmission system of a helicopter. The experimental results and comparisons with other methods indicate that the proposed method is an effective approach to detecting the fault-related impulses

  10. A new approach to pre-processing digital image for wavelet-based watermark

    Science.gov (United States)

    Agreste, Santa; Andaloro, Guido

    2008-11-01

    The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.

  11. Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).

  12. A Novel Hénon Map Based Adaptive PSO for Wavelet Shrinkage Image Denoising

    Directory of Open Access Journals (Sweden)

    Shruti Gandhi

    2013-07-01

    Full Text Available Degradation of images due to noise has led to the formulation of various techniques for image restoration. Wavelet shrinkage image denoising being one such technique has been improved over the years by using Particle Swarm Optimization (PSO and its variants for optimization of the wavelet parameters. However, the use of PSO has been rendered ineffective due to premature convergence and failure to maintain good population diversity. This paper proposes a Hénon map based adaptive PSO (HAPSO for wavelet shrinkage image denoising. While significantly improving the population diversity of the particles, it also increases the convergence rate and thereby the precision of the denoising technique. The proposed PSO uses adaptive cognitive and social components and adaptive inertia weight factors. The Hénon map sequence is applied to the control parameters instead of random variables, which introduces ergodicity and stochastic property in the PSO. This results in a more improved global convergence as compared to the traditional PSO and classical thresholding techniques. Simulation results and comparisons with the standard approaches show the effectiveness of the proposed algorithm.

  13. The Parabolic Variance (PVAR): A Wavelet Variance Based on the Least-Square Fit.

    Science.gov (United States)

    Vernotte, Francois; Lenczner, Michel; Bourgeois, Pierre-Yves; Rubiola, Enrico

    2016-04-01

    This paper introduces the parabolic variance (PVAR), a wavelet variance similar to the Allan variance (AVAR), based on the linear regression (LR) of phase data. The companion article arXiv:1506.05009 [physics.ins-det] details the Ω frequency counter, which implements the LR estimate. The PVAR combines the advantages of AVAR and modified AVAR (MVAR). PVAR is good for long-term analysis because the wavelet spans over 2τ, the same as the AVAR wavelet, and good for short-term analysis because the response to white and flicker PM is 1/τ(3) and 1/τ(2), the same as the MVAR. After setting the theoretical framework, we study the degrees of freedom and the confidence interval for the most common noise types. Then, we focus on the detection of a weak noise process at the transition-or corner-where a faster process rolls off. This new perspective raises the question of which variance detects the weak process with the shortest data record. Our simulations show that PVAR is a fortunate tradeoff. PVAR is superior to MVAR in all cases, exhibits the best ability to divide between fast noise phenomena (up to flicker FM), and is almost as good as AVAR for the detection of random walk and drift. PMID:26571523

  14. Intelligent Ensemble Forecasting System of Stock Market Fluctuations Based on Symetric and Asymetric Wavelet Functions

    Science.gov (United States)

    Lahmiri, Salim; Boukadoum, Mounir

    2015-08-01

    We present a new ensemble system for stock market returns prediction where continuous wavelet transform (CWT) is used to analyze return series and backpropagation neural networks (BPNNs) for processing CWT-based coefficients, determining the optimal ensemble weights, and providing final forecasts. Particle swarm optimization (PSO) is used for finding optimal weights and biases for each BPNN. To capture symmetry/asymmetry in the underlying data, three wavelet functions with different shapes are adopted. The proposed ensemble system was tested on three Asian stock markets: The Hang Seng, KOSPI, and Taiwan stock market data. Three statistical metrics were used to evaluate the forecasting accuracy; including, mean of absolute errors (MAE), root mean of squared errors (RMSE), and mean of absolute deviations (MADs). Experimental results showed that our proposed ensemble system outperformed the individual CWT-ANN models each with different wavelet function. In addition, the proposed ensemble system outperformed the conventional autoregressive moving average process. As a result, the proposed ensemble system is suitable to capture symmetry/asymmetry in financial data fluctuations for better prediction accuracy.

  15. Face Recognition Based on Wavelet Transform and Regional Directional Weighted Local Binary Pattern

    Directory of Open Access Journals (Sweden)

    Fengxiang Wu

    2014-08-01

    Full Text Available With the development of information technology, face recognition technology has been continuously developed. This technology has attracted the attention of many researchers, including institutions and production enterprises. Face recognition technology has become a relatively independent application technology area in various social services. This paper presents a face recognition algorithm based on wavelet transform and regional directional weighted local binary pattern. First of all, this algorithm puts forward a new basis for a face recognition, namely the level of detailed components of face images containing valid facial texture details, and the recognition rate is better than that of the vertical component information and diagonal component information. This is called Horizontal Component Prior Principle(HCPP. According to HCPP, the original image is decomposed with wavelet transformation. The algorithm extracts the scale and level of detailed components. To improve the original LBP operator, it presents the regional directional weighted local binary pattern (RDW-LBP. Using the RDW-LBP, it can calculate the histogram of scale components and detailed components decomposed by wavelet. The histogram feature vector of face image can be got with the different weighted sub-regions. The feature vector can be matched with Chi-Square distance. This approach further enhances the ability to extract face direction information effectively

  16. Performance evaluation of wavelet-based ECG compression algorithms for telecardiology application over CDMA network.

    Science.gov (United States)

    Kim, Byung S; Yoo, Sun K

    2007-09-01

    The use of wireless networks bears great practical importance in instantaneous transmission of ECG signals during movement. In this paper, three typical wavelet-based ECG compression algorithms, Rajoub (RA), Embedded Zerotree Wavelet (EZ), and Wavelet Transform Higher-Order Statistics Coding (WH), were evaluated to find an appropriate ECG compression algorithm for scalable and reliable wireless tele-cardiology applications, particularly over a CDMA network. The short-term and long-term performance characteristics of the three algorithms were analyzed using normal, abnormal, and measurement noise-contaminated ECG signals from the MIT-BIH database. In addition to the processing delay measurement, compression efficiency and reconstruction sensitivity to error were also evaluated via simulation models including the noise-free channel model, random noise channel model, and CDMA channel model, as well as over an actual CDMA network currently operating in Korea. This study found that the EZ algorithm achieves the best compression efficiency within a low-noise environment, and that the WH algorithm is competitive for use in high-error environments with degraded short-term performance with abnormal or contaminated ECG signals. PMID:17701824

  17. Seismic Damage Analysis of Concrete Gravity Dam Based on Wavelet Transform

    Directory of Open Access Journals (Sweden)

    Dunben Sun

    2016-01-01

    Full Text Available The key to the dam damage assessment is analyzing the remaining seismic carrying capacity after an earthquake occurs. In this paper, taking Koyna concrete gravity dam as the object of study, the dynamic response and damage distribution of the dam are obtained based on the concrete damage plastic constitutive model. By using time-frequency localization performance of wavelet transform, the distribution characteristics of wavelet energy for gravity dam dynamic response signal are revealed under the action of different amplitude earthquakes. It is concluded by numerical study that the wavelet energy is concentrated in low-frequency range with the improving of seismic amplitude. The ultimate peak seismic acceleration is obtained according to the concentration degree of low-frequency energy. The earthquake damage of the dam under the moderate-intensity earthquake is simulated and its residual seismic bearing capacity is further analyzed. The new global damage index of the dam is proposed and the overall damage degree of the dam can be distinguished using defined formula under given earthquake actions. The seismic bearing capacity of the intact Koyna dam is 591 gal considering the dam-water interaction and its residual seismic bearing capacity after simulating earthquake can be calculated.

  18. 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

  19. 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.

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

    International Nuclear Information System (INIS)

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

  1. Face recognition based on wavelet neural network%小波神经网络在人脸识别中的应用

    Institute of Scientific and Technical Information of China (English)

    姜友谊

    2012-01-01

    Face recognition is a front complex subject, which includes physiology, psychology, image processing, computer vision, pattern recognition and mathematics,etc. As a research success in the are-a of wavelet analysis theory, Wavelet Neural Network, a feed-forward network, based on the foundation of reliable theory, avoids the blindness in structure design of BP neural network, excludes the probability of sub-optimization in local non-linear optimization problems during network training process and has the capabilities of function learning and generalization. This paper presents a face recognition algorithm based on wavelet neural network, which depends on the multi-resolution property of wavelet and the robustness and memorization features of neural network, combined with the wavelet neural network step adjustment algorithm, a wavelet neural network is designed for the use of face recognition, whose effectiveness and accuracy are verified by some experiments.%人脸识别是一个涉及生理学、心理学、图像处理、计算机视觉、模式识别和数学等多个学科的前沿课题.小波神经网络是在小波分析研究获得突破的基础上提出的一种前馈性网络,避免了BP网络等结构设计上的盲目性,网络训练过程从根本上避免了局部最优等非线性优化问题,有较强的函数学习能力和推广能力.基于小波神经网络,文中提出了一种新的人脸识别算法.该算法利用小波多分辨特性和神经网络的鲁棒性和记忆性,同时结合了加速网络收敛速度的小波神经网络步长调整算法.实验证明该算法有高的检测率和有效性.

  2. Feature-Based Adaptive Tolerance Tree (FATT): An Efficient Indexing Technique for Content-Based Image Retrieval Using Wavelet Transform

    OpenAIRE

    AnandhaKumar, Dr. P.; V. Balamurugan

    2010-01-01

    This paper introduces a novel indexing and access method, called Feature- Based Adaptive Tolerance Tree (FATT), using wavelet transform is proposed to organize large image data sets efficiently and to support popular image access mechanisms like Content Based Image Retrieval (CBIR).Conventional database systems are designed for managing textual and numerical data and retrieving such data is often based on simple comparisons of text or numerical values. However, this method is no longer adequa...

  3. The effect of isoflurane anesthesia on the electroencephalogram assessed by harmonic wavelet bicoherence-based indices

    Science.gov (United States)

    Li, Duan; Li, Xiaoli; Hagihira, Satoshi; Sleigh, Jamie W.

    2011-10-01

    Bicoherence quantifies the degree of quadratic phase coupling among different frequency components within a signal. Previous studies, using Fourier-based methods of bicoherence calculation (FBIC), have demonstrated that electroencephalographic bicoherence can be related to the end-tidal concentration of inhaled anesthetic drugs. However, FBIC methods require excessively long sections of the encephalogram. This problem might be overcome by the use of wavelet-based methods. In this study, we compare FBIC and a recently developed wavelet bicoherence (WBIC) method as a tool to quantify the effect of isoflurane on the electroencephalogram. We analyzed a set of previously published electroencephalographic data, obtained from 29 patients who underwent elective abdominal surgery under isoflurane general anesthesia combined with epidural anesthesia. Nine potential indices of the electroencephalographic anesthetic effect were obtained from the WBIC and FBIC techniques. The relationship between each index and end-tidal concentrations of isoflurane was evaluated using correlation coefficients (r), the inter-individual variations (CV) of index values, the coefficient of determination (R2) of the PKPD models and the prediction probability (PK). The WBIC-based indices tracked anesthetic effects better than the traditional FBIC-based ones. The DiagBic_En index (derived from the Shannon entropy of the diagonal bicoherence values) performed best [r = 0.79 (0.66-0.92), CV = 0.08 (0.05-0.12), R2 = 0.80 (0.75-0.85), PK = 0.79 (0.75-0.83)]. Short data segments of ~10-30 s were sufficient to reliably calculate the indices of WBIC. The wavelet-based bicoherence has advantages over the traditional Fourier-based bicoherence in analyzing volatile anesthetic effects on the electroencephalogram.

  4. Application of Wavelet Packet Energy Spectrum to Extract the Feature of the Pulse Signal

    Institute of Scientific and Technical Information of China (English)

    Dian-guo CAO; Yu-qiang WU; Xue-wen SHI; Peng WANG

    2010-01-01

    The wavelet packet is presented as a new kind of multi-scale analysis technique followed by Wavelet analysis. The fundamental and realization arithmetic of the wavelet packet analysis method are described in this paper. A new application approach of the wavelet packed method to extract the feature of the pulse signal from energy distributing angle is expatiated. It is convenient for the microchip to process and judge by using the wavelet packet analysis method to make the pulse signals quantized and analyzed. Kinds of experiments are simulated in the lab, and the experiments prove that it is a convenient and accurate method to extract the feature of the pulse signal based on wavelet packed-energy spectrum analysis.

  5. Estimation of Seismic Wavelets Based on the Multivariate Scale Mixture of Gaussians Model

    OpenAIRE

    Jing-Huai Gao; Bing Zhang

    2009-01-01

    This paper proposes a new method for estimating seismic wavelets. Suppose a seismic wavelet can be modeled by a formula with three free parameters (scale, frequency and phase). We can transform the estimation of the wavelet into determining these three parameters. The phase of the wavelet is estimated by constant-phase rotation to the seismic signal, while the other two parameters are obtained by the Higher-order Statistics (HOS) (fourth-order cumulant) matching method. In order to derive the...

  6. Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

    Science.gov (United States)

    LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

    2001-01-01

    The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

  7. Research on mine noise sources analysis based on least squares wave-let transform

    Institute of Scientific and Technical Information of China (English)

    CHENG Gen-yin; YU Sheng-chen; CHEN Shao-jie; WEI Zhi-yong; ZHANG Xiao-chen

    2010-01-01

    In order to determine the characteristics of noise source accurately, the noise distribution at different frequencies was determined by taking the differences into account between aerodynamic noises, mechanical noise, electrical noise in terms of in frequency and intensity. Designed a least squares wavelet with high precision and special effects for strong interference zone (multi-source noise), which is applicable to strong noise analysis produced by underground mine, and obtained distribution of noise in different frequency and achieves good results. According to the results of decomposition, the characteristics of noise sources production can be more accurately determined, which lays a good foundation for the follow-up focused and targeted noise control, and provides a new method that is greatly applicable for testing and analyzing noise control.

  8. Rotation and Scale Invariant Wavelet Feature for Content-Based Texture Image Retrieval.

    Science.gov (United States)

    Lee, Moon-Chuen; Pun, Chi-Man

    2003-01-01

    Introduces a rotation and scale invariant log-polar wavelet texture feature for image retrieval. The underlying feature extraction process involves a log-polar transform followed by an adaptive row shift invariant wavelet packet transform. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image…

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

    Directory of Open Access Journals (Sweden)

    Xinhua Nie

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

  10. A New Method for Pitch Tracking and Voicing Decision Based on Spectral Multi-Scale Analysis

    Directory of Open Access Journals (Sweden)

    Aicha Bouzid

    2009-11-01

    Full Text Available This paper proposes a new voicing detection and pitch estimation method that is particularly robust for noisy speech. This method is based on the spectral analysis of the speech multi-scale product. The multi-scale product (MP consists of making the product of wavelet transform coefficients. The wavelet used is the quadratic spline function. We argue that the spectral of Multi-scale Product Analysis is capable of revealing an estimate of a pitch-harmonic more accurately even in a heavy noisy scenario. We evaluate our approach on the Keele database. The experimental results show the robustness of our method for noisy speech, and the good performance for clean speech in comparison with state-of-the-art algorithms.

  11. Performance Comparison of Hybrid Wavelet Transform Formed by Combination of Different Base Transforms with DCT on Image Compression

    Directory of Open Access Journals (Sweden)

    H.B.Kekre

    2014-03-01

    Full Text Available In this paper image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is formed using two component orthogonal transforms. One is base transform which contributes to global features of an image and another transform contributes to local features. Here base transform is varied to observe its effect on image quality at different compression ratios. Different transforms like Discrete Kekre Transform (DKT, Walsh, Real-DFT, Sine, Hartley and Slant transform are chosen as base transforms. They are combined with Discrete Cosine Transform (DCT that contributes to local features of an image. Sizes of component orthogonal transforms are varied as 16-16, 32-8 and 64-4 to generate hybrid wavelet transform of size 256x256. Results of different combinations are compared and it has been observed that, DKT as a base transform combined with DCT gives better results for size 16x16 of both component transforms.

  12. Wavelet frame based scene reconstruction from range data

    Science.gov (United States)

    Ji, Hui; Shen, Zuowei; Xu, Yuhong

    2010-03-01

    How to reconstruct the scene (a visible surface) from a set of scattered, noisy and possibly sparse range data is a challenging problem in robotic navigation and computer graphics. As most real scenes can be modeled by piecewise smooth surfaces, traditional surface fitting techniques ( e.g. smoothing spline) generally can not preserve sharp discontinuities of surfaces. Based on sparse approximation of piecewise smooth functions in frame domain, we propose a new tight frame based formulation for reconstructing a piecewise smooth surface from a sparse range data set, which is robust to both additive noise and outliers. Furthermore, the resulting minimization problem from our formulation can be efficiently solved by the split Bregman method [1,2]. The numerical experiments show that the proposed approach is capable of reconstructing a piecewise smooth surface with sharp edges from sparse range data corrupted with noise and outliers.

  13. WAVELET-BASED WARPING TECHNIQUE FOR MOBILE DEVICES

    OpenAIRE

    Ekta Walia; Vishal Verma,

    2014-01-01

    The role of digital images is increasing rapidly in mobile devices. They are used in many applications including virtual tours, virtual reality, e-commerce etc. Such applications synthesize realistic looking novel views of the reference images on mobile devices using the techniques like image-based rendering (IBR). However, with this increasing role of digital images comes the serious issue of processing large images which requires considerable time. Hence, methods to compress ...

  14. Embedded zeroblock coding algorithm based on KLT and wavelet transform for hyperspectral image compression

    Science.gov (United States)

    Hou, Ying

    2009-10-01

    In this paper, a hyperspectral image lossy coder using three-dimensional Embedded ZeroBlock Coding (3D EZBC) algorithm based on Karhunen-Loève transform (KLT) and wavelet transform (WT) is proposed. This coding scheme adopts 1D KLT as spectral decorrelator and 2D WT as spatial decorrelator. Furthermore, the computational complexity and the coding performance of the low-complexity KLT are compared and evaluated. In comparison with several stateof- the-art coding algorithms, experimental results indicate that our coder can achieve better lossy compression performance.

  15. Wavelet Based Fault Detection, Classification in Transmission System with TCSC Controllers

    Directory of Open Access Journals (Sweden)

    G.Satyanarayana,

    2015-08-01

    Full Text Available This paper presents simulation results of the application of distance relays for the protection of transmission systems employing flexible alternating current transmission controllers such as Thyristor Controlled Series Capacitor (TCSC. The complete digital simulation of TCSC within a transmission system is performed in the MATLAB/Simulink environment using the Power System Block set (PSB. This paper presents an efficient method based on wavelet transforms both fault detection and classification which is almost independent of fault impedance, fault location and fault inception angle of transmission line fault currents with FACTS controllers.

  16. A novel 3D wavelet based filter for visualizing features in noisy biological data

    Energy Technology Data Exchange (ETDEWEB)

    Moss, W C; Haase, S; Lyle, J M; Agard, D A; Sedat, J W

    2005-01-05

    We have developed a 3D wavelet-based filter for visualizing structural features in volumetric data. The only variable parameter is a characteristic linear size of the feature of interest. The filtered output contains only those regions that are correlated with the characteristic size, thus denoising the image. We demonstrate the use of the filter by applying it to 3D data from a variety of electron microscopy samples including low contrast vitreous ice cryogenic preparations, as well as 3D optical microscopy specimens.

  17. Wavelet-based ECG compression by bit-field preserving and running length encoding.

    Science.gov (United States)

    Chan, Hsiao-Lung; Siao, You-Chen; Chen, Szi-Wen; Yu, Shih-Fan

    2008-04-01

    Efficient electrocardiogram (ECG) compression can reduce the payload of real-time ECG transmission as well as reduce the amount of data storage in long-term ECG recording. In this paper an ECG compression/decompression architecture based on the bit-field preserving (BFP) and running length encoding (RLE)/decoding schemes incorporated with the discrete wavelet transform (DWT) is proposed. Compared to complex and repetitive manipulations in the set partitioning in hierarchical tree (SPIHT) coding and the vector quantization (VQ), the proposed algorithm has advantages of simple manipulations and a feedforward structure that would be suitable to implement on very-large-scale integrated circuits and general microcontrollers. PMID:18164098

  18. Parameter estimation of analog circuits based on the fractional wavelet method

    Science.gov (United States)

    Yong, Deng; He, Zhang

    2015-03-01

    Aiming at the problem of parameter estimation in analog circuits, a new approach is proposed. The approach is based on the fractional wavelet to derive the Volterra series model of the circuit under test (CUT). By the gradient search algorithm used in the Volterra model, the unknown parameters in the CUT are estimated and the Volterra model is identified. The simulations show that the parameter estimation results of the proposed method in the paper are better than those of other parameter estimation methods. Project supported by the Key Research Project of Sichuan Provincial Department of Education, China (No. 13ZA0186).

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

    Science.gov (United States)

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

    2015-09-01

    Digital shearing speckle pattern interferometry (DSSPI) has been recognized as a practical tool in testing strain. The DSSPI system which is based on temporal analysis is attractive because of its ability to measure strain dynamically. In this paper, such a DSSPI system with Wollaston prism has been built. The principles and system arrangement are described and the preliminary experimental result of the displacement-derivative test of an aluminum plate is shown with the wavelet transformation method and the Fourier transformation method. The simulations have been conducted with the finite element method. The comparison of the results shows that quantitative measurement of displacement-derivative has been realized. PMID:26429424

  20. Lidar signal de-noising based on wavelet trimmed thresholding technique

    Institute of Scientific and Technical Information of China (English)

    Haitao Fang(方海涛); Deshuang Huang(黄德双)

    2004-01-01

    Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.

  1. A WAVELET TRANSFORM BASED WATERMARKING ALGORITHM FOR PROTECTING COPYRIGHTS OF DIGITAL IMAGES

    Directory of Open Access Journals (Sweden)

    Divya A

    2013-08-01

    Full Text Available This paper proposes an algorithm of Digital Watermarking based on Biorthogonal Wavelet Transform. Digital Watermarking is a technique to protect the copyright of the multimedia data. The position of the watermark can be detected without using the original image by utilizing the correlation between the neighbours of wave co-efficient. The strength of Digital watermark is obtained according to the edge intensities resulting in good robust and Imperceptible. Results show that the proposed watermark algorithm is invisible and has good robustness against common image processing operations.

  2. Research of Wavelet Based Multicarrier Modulation System with Near Shannon Limited Codes

    Institute of Scientific and Technical Information of China (English)

    ZHANGHaixia; YUANDongfeng; ZHAOFeng

    2005-01-01

    In this paper, by using turbo codes and Low density parity codes (LDPC) as channel correcting code scheme, Wavelet based multicarrier modulation (WMCM) systems are proposed and investigated on different transmission scenarios. The Bit error rate (BER) performance of these two near Shannon limited codes is simulated and compared with various code parameters. Simulated results show that Turbo coded WMCM (TCWMCM) performs better than LDPC coded WMCM (LDPC-CWMCM) on both AWGN and Rayleigh fading channels when these two kinds of codes are of the same code parameters.

  3. Digital Image Watermarking Based on DiscreteWavelet Transform

    Institute of Scientific and Technical Information of China (English)

    丁玮; 闫伟齐; 齐东旭

    2002-01-01

    This paper aims at digital watermark which is a new popular research topic recently, presents some methods to embed digital watermark based on modifying frequency coefficients in discrete wavelettransfor(DWT)domain.First,thepresent progress of digital watermark is briefly introduced; after that, starting from Pitas's method and discarding hispseudo random number method, the authors use a digital image scrambling technology as preprocessing fordigital watermarking. Then the authors discuss how to embed a 1-bit digital image as watermark in frequency domain. Finallyanotherdigital watermarking method is given in which 3-D DWT is used to transformagivendigitalimage.Basedontheexperimentalresults, it is shown that the proposed methods are robust to a large extent.

  4. Watermarking Scheme Based on Wavelet Transformation and Visual Cryptography

    Institute of Scientific and Technical Information of China (English)

    Young-Chang Hou; Shih-Chieh Wei; Hsin-Ju Liu; A-Yu Tseng

    2014-01-01

    Based on the principles of the visual cryptography and the law of large numbers, the unexpanded shares are generated during the processes of embedding and verifying the hidden watermark. The watermark embedding is done in the frequency domain, which can be decoded by the human visual system (HVS) without the necessity of any complicated computation and the help of the original image. Experimental results indicated that our method had a good robustness on darkening, lightening, blurring, sharpening, noise, distorting, jitter, joint photographic experts group (JPEG) compression, and crop attacks.

  5. Wavelet-based motion artifact removal for functional near-infrared spectroscopy

    International Nuclear Information System (INIS)

    Functional near-infrared spectroscopy (fNIRS) is a powerful tool for monitoring brain functional activities. Due to its non-invasive and non-restraining nature, fNIRS has found broad applications in brain functional studies. However, for fNIRS to work well, it is important to reduce its sensitivity to motion artifacts. We propose a new wavelet-based method for removing motion artifacts from fNIRS signals. The method relies on differences between artifacts and fNIRS signal in terms of duration and amplitude and is specifically designed for spike artifacts. We assume a Gaussian distribution for the wavelet coefficients corresponding to the underlying hemodynamic signal in detail levels and identify the artifact coefficients using this distribution. An input parameter controls the intensity of artifact attenuation in trade-off with the level of distortion introduced in the signal. The method only modifies wavelet coefficients in levels adaptively selected based on the degree of contamination with motion artifact. To demonstrate the feasibility of the method, we tested it on experimental fNIRS data collected from three infant subjects. Normalized mean-square error and artifact energy attenuation were used as criteria for performance evaluation. The results show 18.29 and 16.42 dB attenuation in motion artifacts energy for 700 and 830 nm wavelength signals in a total of 29 motion events with no more than −16.7 dB distortion in terms of normalized mean-square error in the artifact-free regions of the signal. (paper)

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

    Science.gov (United States)

    Oltean, Gabriel; Ivanciu, Laura-Nicoleta

    2016-01-01

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

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

    OpenAIRE

    M. Rasoulpoor; M. Banejad; Ahmadyfard, A

    2011-01-01

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

  8. Goal-based angular adaptivity applied to a wavelet-based discretisation of the neutral particle transport equation

    International Nuclear Information System (INIS)

    A method for applying goal-based adaptive methods to the angular resolution of the neutral particle transport equation is presented. The methods are applied to an octahedral wavelet discretisation of the spherical angular domain which allows for anisotropic resolution. The angular resolution is adapted across both the spatial and energy dimensions. The spatial domain is discretised using an inner-element sub-grid scale finite element method. The goal-based adaptive methods optimise the angular discretisation to minimise the error in a specific functional of the solution. The goal-based error estimators require the solution of an adjoint system to determine the importance to the specified functional. The error estimators and the novel methods to calculate them are described. Several examples are presented to demonstrate the effectiveness of the methods. It is shown that the methods can significantly reduce the number of unknowns and computational time required to obtain a given error. The novelty of the work is the use of goal-based adaptive methods to obtain anisotropic resolution in the angular domain for solving the transport equation. -- Highlights: •Wavelet angular discretisation used to solve transport equation. •Adaptive method developed for the wavelet discretisation. •Anisotropic angular resolution demonstrated through the adaptive method. •Adaptive method provides improvements in computational efficiency

  9. Goal-based angular adaptivity applied to a wavelet-based discretisation of the neutral particle transport equation

    Energy Technology Data Exchange (ETDEWEB)

    Goffin, Mark A., E-mail: mark.a.goffin@gmail.com [Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ (United Kingdom); Buchan, Andrew G.; Dargaville, Steven; Pain, Christopher C. [Applied Modelling and Computation Group, Department of Earth Science and Engineering, Imperial College London, London, SW7 2AZ (United Kingdom); Smith, Paul N. [ANSWERS Software Service, AMEC, Kimmeridge House, Dorset Green Technology Park, Winfrith Newburgh, Dorchester, Dorset, DT2 8ZB (United Kingdom); Smedley-Stevenson, Richard P. [AWE, Aldermaston, Reading, RG7 4PR (United Kingdom)

    2015-01-15

    A method for applying goal-based adaptive methods to the angular resolution of the neutral particle transport equation is presented. The methods are applied to an octahedral wavelet discretisation of the spherical angular domain which allows for anisotropic resolution. The angular resolution is adapted across both the spatial and energy dimensions. The spatial domain is discretised using an inner-element sub-grid scale finite element method. The goal-based adaptive methods optimise the angular discretisation to minimise the error in a specific functional of the solution. The goal-based error estimators require the solution of an adjoint system to determine the importance to the specified functional. The error estimators and the novel methods to calculate them are described. Several examples are presented to demonstrate the effectiveness of the methods. It is shown that the methods can significantly reduce the number of unknowns and computational time required to obtain a given error. The novelty of the work is the use of goal-based adaptive methods to obtain anisotropic resolution in the angular domain for solving the transport equation. -- Highlights: •Wavelet angular discretisation used to solve transport equation. •Adaptive method developed for the wavelet discretisation. •Anisotropic angular resolution demonstrated through the adaptive method. •Adaptive method provides improvements in computational efficiency.

  10. Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

    Science.gov (United States)

    Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

    2016-04-01

    The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

  11. A hyperspectral image compression algorithm based on wavelet transformation and fractal composition (AWFC)

    Institute of Scientific and Technical Information of China (English)

    HU; Xingtang; ZHANG; Bing; ZHANG; Xia; ZHENG; Lanfen; TONG; Qingxi

    2006-01-01

    Starting with a fractal-based image-compression algorithm based on wavelet transformation for hyperspectral images, the authors were able to obtain more spectral bands with the help of of hyperspectral remote sensing. Because large amounts of data and limited bandwidth complicate the storage and transmission of data measured by TB-level bits, it is important to compress image data acquired by hyperspectral sensors such as MODIS, PHI, and OMIS; otherwise, conventional lossless compression algorithms cannot reach adequate compression ratios. Other loss-compression methods can reach high compression ratios but lack good image fidelity, especially for hyperspectral image data. Among the third generation of image compression algorithms, fractal image compression based on wavelet transformation is superior to traditional compression methods,because it has high compression ratios and good image fidelity, and requires less computing time. To keep the spectral dimension invariable, the authors compared the results of two compression algorithms based on the storage-file structures of BSQ and of BIP, and improved the HV and Quadtree partitioning and domain-range matching algorithms in order to accelerate their encode/decode efficiency. The authors' Hyperspectral Image Process and Analysis System (HIPAS) software used a VC++6.0 integrated development environment (IDE), with which good experimental results were obtained. Possible modifications of the algorithm and limitations of the method are also discussed.

  12. Multiresolution With Super-Compact Wavelets

    Science.gov (United States)

    Lee, Dohyung

    2000-01-01

    The solution data computed from large scale simulations are sometimes too big for main memory, for local disks, and possibly even for a remote storage disk, creating tremendous processing time as well as technical difficulties in analyzing the data. The excessive storage demands a corresponding huge penalty in I/O time, rendering time and transmission time between different computer systems. In this paper, a multiresolution scheme is proposed to compress field simulation or experimental data without much loss of important information in the representation. Originally, the wavelet based multiresolution scheme was introduced in image processing, for the purposes of data compression and feature extraction. Unlike photographic image data which has rather simple settings, computational field simulation data needs more careful treatment in applying the multiresolution technique. While the image data sits on a regular spaced grid, the simulation data usually resides on a structured curvilinear grid or unstructured grid. In addition to the irregularity in grid spacing, the other difficulty is that the solutions consist of vectors instead of scalar values. The data characteristics demand more restrictive conditions. In general, the photographic images have very little inherent smoothness with discontinuities almost everywhere. On the other hand, the numerical solutions have smoothness almost everywhere and discontinuities in local areas (shock, vortices, and shear layers). The wavelet bases should be amenable to the solution of the problem at hand and applicable to constraints such as numerical accuracy and boundary conditions. In choosing a suitable wavelet basis for simulation data among a variety of wavelet families, the supercompact wavelets designed by Beam and Warming provide one of the most effective multiresolution schemes. Supercompact multi-wavelets retain the compactness of Haar wavelets, are piecewise polynomial and orthogonal, and can have arbitrary order of

  13. Wavelet-Based Real-Time Diagnosis of Complex Systems

    Science.gov (United States)

    Gulati, Sandeep; Mackey, Ryan

    2003-01-01

    A new method of robust, autonomous real-time diagnosis of a time-varying complex system (e.g., a spacecraft, an advanced aircraft, or a process-control system) is presented here. It is based upon the characterization and comparison of (1) the execution of software, as reported by discrete data, and (2) data from sensors that monitor the physical state of the system, such as performance sensors or similar quantitative time-varying measurements. By taking account of the relationship between execution of, and the responses to, software commands, this method satisfies a key requirement for robust autonomous diagnosis, namely, ensuring that control is maintained and followed. Such monitoring of control software requires that estimates of the state of the system, as represented within the control software itself, are representative of the physical behavior of the system. In this method, data from sensors and discrete command data are analyzed simultaneously and compared to determine their correlation. If the sensed physical state of the system differs from the software estimate (see figure) or if the system fails to perform a transition as commanded by software, or such a transition occurs without the associated command, the system has experienced a control fault. This method provides a means of detecting such divergent behavior and automatically generating an appropriate warning.

  14. Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

    Science.gov (United States)

    Niegowski, Maciej; Zivanovic, Miroslav

    2016-03-01

    We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. PMID:26774422

  15. Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing

    Science.gov (United States)

    Liang, Lei; Li, Xinwu; Gao, Xizhang; Guo, Huadong

    2015-01-01

    The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana.

  16. An approach to melodic segmentation and classification based on filtering with the Haar-wavelet

    DEFF Research Database (Denmark)

    Velarde, Gissel; Weyde, Tillman; Meredith, David

    2013-01-01

    We present a novel method of classification and segmentation of melodies in symbolic representation. The method is based on filtering pitch as a signal over time with the Haar-wavelet, and we evaluate it on two tasks. The filtered signal corresponds to a single-scale signal ws from the continuous...... Haar wavelet transform. The melodies are first segmented using local maxima or zero-crossings of ws. The segments of ws are then classified using the k–nearest neighbour algorithm with Euclidian and city-block distances. The method proves more effective than using unfiltered pitch signals and Gestalt......-based segmentation when used to recognize the parent works of segments from Bach’s Two-Part Inventions (BWV 772–786). When used to classify 360 Dutch folk tunes into 26 tune families, the performance of the method is comparable to the use of pitch signals, but not as good as that of string-matching methods based on...

  17. A Wavelet Bicoherence-Based Quadratic Nonlinearity Feature for Translational Axis Condition Monitoring

    Directory of Open Access Journals (Sweden)

    Yong Li

    2014-01-01

    Full Text Available The translational axis is one of the most important subsystems in modern machine tools, as its degradation may result in the loss of the product qualification and lower the control precision. Condition-based maintenance (CBM has been considered as one of the advanced maintenance schemes to achieve effective, reliable and cost-effective operation of machine systems, however, current vibration-based maintenance schemes cannot be employed directly in the translational axis system, due to its complex structure and the inefficiency of commonly used condition monitoring features. In this paper, a wavelet bicoherence-based quadratic nonlinearity feature is proposed for translational axis condition monitoring by using the torque signature of the drive servomotor. Firstly, the quadratic nonlinearity of the servomotor torque signature is discussed, and then, a biphase randomization wavelet bicoherence is introduced for its quadratic nonlinear detection. On this basis, a quadratic nonlinearity feature is proposed for condition monitoring of the translational axis. The properties of the proposed quadratic nonlinearity feature are investigated by simulations. Subsequently, this feature is applied to the real-world servomotor torque data collected from the X-axis on a high precision vertical machining centre. All the results show that the performance of the proposed feature is much better than that of original condition monitoring features.

  18. Wavelet-Based Digital Image Fusion on Reconfigurable FPGA Using Handel-C Language

    Directory of Open Access Journals (Sweden)

    Dr. G. Mohan

    2013-07-01

    Full Text Available Field Programmable Gate Array (FPGA technology has become a viable target for the implementation of real time algorithms in different fusion methods have been proposed mainly in the fields of remote sensing and computer vision. Image fusion is basically a process where multiple images (more than one are combined to form a single resultant fused image. This fused image is more productive as compared to its original input images. In most paper image fusion algorithms were implemented in simulation level only. In this paper Wavelet based Image fusion algorithm is employed and implemented on a Field-Programmable-Gate-Array-based hardware system using a Xilinx Platform Studio EDK 11.1 FPGA Spartan 3E is implemented. The FPGA technologies offer basic digital blocks with flexible interconnections to achieve high speed digital hardware realization. The FPGA consists of a system of logic blocks, such as Look up Tables, gates, or flip-flops and some amount of memory. The algorithm will be transferred from computer to FPGA board using JTAG cable. In this proposed work algorithm is developed by using Handle –C language to performing wavelet based image fusion. The result will be transferred back to system to analyze hardware resource taken by FPGA

  19. Application of wavelet neural network model based on genetic algorithm in the prediction of high-speed railway settlement

    Science.gov (United States)

    Tang, Shihua; Li, Feida; Liu, Yintao; Lan, Lan; Zhou, Conglin; Huang, Qing

    2015-12-01

    With the advantage of high speed, big transport capacity, low energy consumption, good economic benefits and so on, high-speed railway is becoming more and more popular all over the world. It can reach 350 kilometers per hour, which requires high security performances. So research on the prediction of high-speed railway settlement that as one of the important factors affecting the safety of high-speed railway becomes particularly important. This paper takes advantage of genetic algorithms to seek all the data in order to calculate the best result and combines the advantage of strong learning ability and high accuracy of wavelet neural network, then build the model of genetic wavelet neural network for the prediction of high-speed railway settlement. By the experiment of back propagation neural network, wavelet neural network and genetic wavelet neural network, it shows that the absolute value of residual errors in the prediction of high-speed railway settlement based on genetic algorithm is the smallest, which proves that genetic wavelet neural network is better than the other two methods. The correlation coefficient of predicted and observed value is 99.9%. Furthermore, the maximum absolute value of residual error, minimum absolute value of residual error-mean value of relative error and value of root mean squared error(RMSE) that predicted by genetic wavelet neural network are all smaller than the other two methods'. The genetic wavelet neural network in the prediction of high-speed railway settlement is more stable in terms of stability and more accurate in the perspective of accuracy.

  20. Gyrator wavelet transform based non-linear multiple single channel information fusion and authentication

    Science.gov (United States)

    Abuturab, Muhammad Rafiq

    2015-11-01

    A novel gyrator wavelet transform based non-linear multiple single channel information fusion and authentication is introduced. In this technique, each user channel is normalized, phase encoded, and modulated by random phase function, and then multiplexed into a single channel user ciphertext. Now, the secret channel of corresponding user is phase encoded, modulated by random phase function, and gyrator transformed, and then multiplexed into a single channel secret ciphertext. The user ciphertext and secret ciphertext are multiplied to get a single channel multiplex image and then inverse gyrator transformed. The resultant spectrum is phase- and amplitude-truncated to obtain the encrypted image and the asymmetric key, respectively. The encrypted image is a single-level 2-D discrete wavelet transformed. The information is decomposed into LL, HL, LH, and HH sub-bands. This process is repeated to obtain three sets of four sub-bands of three different images. Next, the individual sub-band of each encrypted image is fused to get four fused sub-bands. Finally, the four fused sub-bands are inverse single-level 2-D discrete wavelet transformed to obtain final encrypted image. This is the main advantage for the proposed system: using multiple individual decryption keys (authentication key, asymmetric key, secret keys, and sub-band keys) for each user not only expands the key spaces but also supplies non-linear keys to control the system security. Moreover, the orders of gyrator transform provide extra degrees of freedom. The theoretical analysis and numerical simulation results support the proposed method.