Multi-frequency fringe projection profilometry based on wavelet transform.
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
An Investigation of Wavelet Bases for Grid-Based Multi-Scale Simulations Final Report
Baty, R.S.; Burns, S.P.; Christon, M.A.; Roach, D.W.; Trucano, T.G.; Voth, T.E.; Weatherby, J.R.; Womble, D.E.
1998-11-01
The research summarized in this report is the result of a two-year effort that has focused on evaluating the viability of wavelet bases for the solution of partial differential equations. The primary objective for this work has been to establish a foundation for hierarchical/wavelet simulation methods based upon numerical performance, computational efficiency, and the ability to exploit the hierarchical adaptive nature of wavelets. This work has demonstrated that hierarchical bases can be effective for problems with a dominant elliptic character. However, the strict enforcement of orthogonality was found to be less desirable than weaker semi-orthogonality or bi-orthogonality for solving partial differential equations. This conclusion has led to the development of a multi-scale linear finite element based on a hierarchical change of basis. The reproducing kernel particle method has been found to yield extremely accurate phase characteristics for hyperbolic problems while providing a convenient framework for multi-scale analyses.
Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform
NASA Astrophysics Data System (ADS)
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
A gear rattle metric based on the wavelet multi-resolution analysis: Experimental investigation
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
Fast multi-scale edge detection algorithm based on wavelet transform
NASA Astrophysics Data System (ADS)
Zang, Jie; Song, Yanjun; Li, Shaojuan; Luo, Guoyun
2011-11-01
The traditional edge detection algorithms have certain noise amplificat ion, making there is a big error, so the edge detection ability is limited. In analysis of the low-frequency signal of image, wavelet analysis theory can reduce the time resolution; under high time resolution for high-frequency signal of the image, it can be concerned about the transient characteristics of the signal to reduce the frequency resolution. Because of the self-adaptive for signal, the wavelet transform can ext ract useful informat ion from the edge of an image. The wavelet transform is at various scales, wavelet transform of each scale provides certain edge informat ion, so called mult i-scale edge detection. Multi-scale edge detection is that the original signal is first polished at different scales, and then detects the mutation of the original signal by the first or second derivative of the polished signal, and the mutations are edges. The edge detection is equivalent to signal detection in different frequency bands after wavelet decomposition. This article is use of this algorithm which takes into account both details and profile of image to detect the mutation of the signal at different scales, provided necessary edge information for image analysis, target recognition and machine visual, and achieved good results.
Wavelets based on Hermite cubic splines
NASA Astrophysics Data System (ADS)
Cvejnová, Daniela; Černá, Dana; Finěk, Václav
2016-06-01
In 2000, W. Dahmen et al. designed biorthogonal multi-wavelets adapted to the interval [0,1] on the basis of Hermite cubic splines. In recent years, several more simple constructions of wavelet bases based on Hermite cubic splines were proposed. We focus here on wavelet bases with respect to which both the mass and stiffness matrices are sparse in the sense that the number of nonzero elements in any column is bounded by a constant. Then, a matrix-vector multiplication in adaptive wavelet methods can be performed exactly with linear complexity for any second order differential equation with constant coefficients. In this contribution, we shortly review these constructions and propose a new wavelet which leads to improved Riesz constants. Wavelets have four vanishing wavelet moments.
Multi-focus image fusion based on improved spectral graph wavelet transform
NASA Astrophysics Data System (ADS)
Yan, Xiang; Qin, Hanlin; Chen, Zhimin; Zhou, Huixin; Li, Jia; Zong, Jingguo
2015-10-01
Due to the limited depth-of-focus of optical lenses in imaging camera, it is impossible to acquire an image with all parts of the scene in focus. To make up for this defect, fusing the images at different focus settings into one image is a potential approach and many fusion methods have been developed. However, the existing methods can hardly deal with the problem of image detail blur. In this paper, a novel multiscale geometrical analysis called the directional spectral graph wavelet transform (DSGWT) is proposed, which integrates the nonsubsampled directional filter bank with the traditional spectral graph wavelet transform. Through combines the feature of efficiently representing the image containing regular or irregular areas of the spectral graph wavelet transform with the ability of capturing the directional information of the directional filter bank, the DSGWT can better represent the structure of images. Given the feature of the DSGWT, it is introduced to multi-focus image fusion to overcome the above disadvantage. On the one hand, using the high frequency subbands of the source images are obtained by the DSGWT, the proposed method efficiently represents the source images. On the other hand, using morphological filter to process the sparse feature matrix obtained by sum-modified-Laplacian focus measure criterion, the proposed method generates the fused subbands by morphological filtering. Comparison experiments have been performed on different image sets, and the experimental results demonstrate that the proposed method does significantly improve the fusion performance compared to the existing fusion methods.
NASA Astrophysics Data System (ADS)
Wei, Li; Shi, Junsheng; Huang, Xiaoqiao; Ding, Huimei
2015-12-01
In order to solve the problems that image's entropy of information decline obviously and boundary line phenomenon appear obviously in the processing of aerial remote sensing image's mosaic, an image mosaic approach is presented in this paper, which uses wavelet image fusion based on structure similarity and is capable of creating seamless mosaics in real-time. The approach consists of three steps. First, the overlapping area of two aerial images is extracted. Then, the two overlapping area images are fused adaptively by the method of multi-layer wavelet decomposition based on the structure similarity and appointed regulation. Finally, weighted average fusion is used again to avoid the visible boundary line for the both sides of the boundary of the above fusion image. Experimental results show the entropy of information, sharpness and standard deviation have been improved significantly, and the boundary line has been eliminated observably.
NASA Astrophysics Data System (ADS)
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
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Han, Dongying; li, Pei; An, Shujun; Shi, Peiming
2016-03-01
In actual fault diagnosis, useful information is often submerged in heavy noise, and the feature information is difficult to extract. A novel weak signal detection method aimed at the problem of detecting multi-frequency signals buried under heavy background noise is proposed based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance (SR). First, the noisy signal is processed by parameter compensation, with the noise and system parameters expanded 10 times to counteract the effect of the damping term. The processed signal is decomposed into multiple signals of different scale frequencies by wavelet transform. Following this, we adjust the size of the scaled signals' amplitudes and reconstruct the signals; the weak signal frequency components are then enhanced by multi-stable stochastic resonance. The enhanced components of the signal are processed through a band-pass filter, leaving the enhanced sections of the signal. The processed signal is analyzed by FFT to achieve detection of the multi-frequency weak signals. The simulation and experimental results show that the proposed method can enhance the signal amplitude, can effectively detect multi-frequency weak signals buried under heavy noise and is valuable and usable for bearing fault signal analysis.
NASA Astrophysics Data System (ADS)
Shekede, Munyaradzi D.; Murwira, Amon; Masocha, Mhosisi
2015-03-01
Although increased woody plant abundance has been reported in tropical savannas worldwide, techniques for detecting the direction and magnitude of change are mostly based on visual interpretation of historical aerial photography or textural analysis of multi-temporal satellite images. These techniques are prone to human error and do not permit integration of remotely sensed data from diverse sources. Here, we integrate aerial photographs with high spatial resolution satellite imagery and use a discrete wavelet transform to objectively detect the dynamics in bush encroachment at two protected Zimbabwean savanna sites. Based on the recently introduced intensity-dominant scale approach, we test the hypotheses that: (1) the encroachment of woody patches into the surrounding grassland matrix causes a shift in the dominant scale. This shift in the dominant scale can be detected using a discrete wavelet transform regardless of whether aerial photography and satellite data are used; and (2) as the woody patch size stabilises, woody cover tends to increase thereby triggering changes in intensity. The results show that at the first site where tree patches were already established (Lake Chivero Game Reserve), between 1972 and 1984 the dominant scale of woody patches initially increased from 8 m before stabilising at 16 m and 32 m between 1984 and 2012 while the intensity fluctuated during the same period. In contrast, at the second site, which was formely grass-dominated site (Kyle Game Reserve), we observed an unclear dominant scale (1972) which later becomes distinct in 1985, 1996 and 2012. Over the same period, the intensity increased. Our results imply that using our approach we can detect and quantify woody/bush patch dynamics in savanna landscapes.
NASA Technical Reports Server (NTRS)
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.
Numerical Algorithms Based on Biorthogonal Wavelets
NASA Technical Reports Server (NTRS)
Ponenti, Pj.; Liandrat, J.
1996-01-01
Wavelet bases are used to generate spaces of approximation for the resolution of bidimensional elliptic and parabolic problems. Under some specific hypotheses relating the properties of the wavelets to the order of the involved operators, it is shown that an approximate solution can be built. This approximation is then stable and converges towards the exact solution. It is designed such that fast algorithms involving biorthogonal multi resolution analyses can be used to resolve the corresponding numerical problems. Detailed algorithms are provided as well as the results of numerical tests on partial differential equations defined on the bidimensional torus.
NASA Astrophysics Data System (ADS)
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.
Wavelet-based polarimetry analysis
NASA Astrophysics Data System (ADS)
Ezekiel, Soundararajan; Harrity, Kyle; Farag, Waleed; Alford, Mark; Ferris, David; Blasch, Erik
2014-06-01
Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.
An Adaptive Digital Image Watermarking Algorithm Based on Morphological Haar Wavelet Transform
NASA Astrophysics Data System (ADS)
Huang, Xiaosheng; Zhao, Sujuan
At present, much more of the wavelet-based digital watermarking algorithms are based on linear wavelet transform and fewer on non-linear wavelet transform. In this paper, we propose an adaptive digital image watermarking algorithm based on non-linear wavelet transform--Morphological Haar Wavelet Transform. In the algorithm, the original image and the watermark image are decomposed with multi-scale morphological wavelet transform respectively. Then the watermark information is adaptively embedded into the original image in different resolutions, combining the features of Human Visual System (HVS). The experimental results show that our method is more robust and effective than the ordinary wavelet transform algorithms.
Komorowski, Dariusz; Pietraszek, Stanislaw
2016-01-01
This paper presents the analysis of multi-channel electrogastrographic (EGG) signals using the continuous wavelet transform based on the fast Fourier transform (CWTFT). The EGG analysis was based on the determination of the several signal parameters such as dominant frequency (DF), dominant power (DP) and index of normogastria (NI). The use of continuous wavelet transform (CWT) allows for better visible localization of the frequency components in the analyzed signals, than commonly used short-time Fourier transform (STFT). Such an analysis is possible by means of a variable width window, which corresponds to the scale time of observation (analysis). Wavelet analysis allows using long time windows when we need more precise low-frequency information, and shorter when we need high frequency information. Since the classic CWT transform requires considerable computing power and time, especially while applying it to the analysis of long signals, the authors used the CWT analysis based on the fast Fourier transform (FFT). The CWT was obtained using properties of the circular convolution to improve the speed of calculation. This method allows to obtain results for relatively long records of EGG in a fairly short time, much faster than using the classical methods based on running spectrum analysis (RSA). In this study authors indicate the possibility of a parametric analysis of EGG signals using continuous wavelet transform which is the completely new solution. The results obtained with the described method are shown in the example of an analysis of four-channel EGG recordings, performed for a non-caloric meal. PMID:26573647
Wavelet based free-form deformations for nonrigid registration
NASA Astrophysics Data System (ADS)
Sun, Wei; Niessen, Wiro J.; Klein, Stefan
2014-03-01
In nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have been proposed as a signal representation suitable for multi-scale problems. Wavelet analysis leads to a unique decomposition of a signal into its coarse- and fine-scale components. Potentially, this could therefore be useful for image registration. In this work, we investigate whether a wavelet-based FFD model has advantages for nonrigid image registration. We use a B-splines based wavelet, as defined by Cai and Wang.1 This wavelet is expressed as a linear combination of B-spline basis functions. Derived from the original B-spline function, this wavelet is smooth, differentiable, and compactly supported. The basis functions of this wavelet are orthogonal across scales in Sobolev space. This wavelet was previously used for registration in computer vision, in 2D optical flow problems,2 but it was not compared with the conventional B-spline FFD in medical image registration problems. An advantage of choosing this B-splines based wavelet model is that the space of allowable deformation is exactly equivalent to that of the traditional B-spline. The wavelet transformation is essentially a (linear) reparameterization of the B-spline transformation model. Experiments on 10 CT lung and 18 T1-weighted MRI brain datasets show that wavelet based registration leads to smoother deformation fields than traditional B-splines based registration, while achieving better accuracy.
NASA Astrophysics Data System (ADS)
Mukhopadhyay, Sabyasachi; Das, Nandan K.; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.
2014-05-01
DIC (Differential Interference Contrast Image) images of cervical pre-cancer tissues are taken from epithelium region, on which wavelet transform and multi-fractal analysis are applied. Discrete wavelet transform (DWT) through Daubechies basis are done for identifying fluctuations over polynomial trends for clear characterization and differentiation of tissues. A systematic investigation of denoised images is carried out through the continuous Morlet wavelet. The scalogram reveals the changes in coefficient peak values from grade-I to grade-III. Wavelet normalized energy plots are computed in order to show the difference of periodicity among different grades of cancerous tissues. Using the multi-fractal de-trended fluctuation analysis (MFDFA), it is observed that the values of Hurst exponent and width of singularity spectrum decrease as cancer progresses from grade-I to grade-III tissue.
2012-01-01
Background Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. Method The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. Results The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice. PMID:22236465
An image fusion method based on biorthogonal wavelet
NASA Astrophysics Data System (ADS)
Li, Jianlin; Yu, Jiancheng; Sun, Shengli
2008-03-01
Image fusion could process and utilize the source images, with complementing different image information, to achieve the more objective and essential understanding of the identical object. Recently, image fusion has been extensively applied in many fields such as medical imaging, micro photographic imaging, remote sensing, and computer vision as well as robot. There are various methods have been proposed in the past years, such as pyramid decomposition and wavelet transform algorithm. As for wavelet transform algorithm, due to the virtue of its multi-resolution, wavelet transform has been applied in image processing successfully. Another advantage of wavelet transform is that it can be much more easily realized in hardware, because its data format is very simple, so it could save a lot of resources, besides, to some extent, it can solve the real-time problem of huge-data image fusion. However, as the orthogonal filter of wavelet transform doesn't have the characteristics of linear phase, the phase distortion will lead to the distortion of the image edge. To make up for this shortcoming, the biorthogonal wavelet is introduced here. So, a novel image fusion scheme based on biorthogonal wavelet decomposition is presented in this paper. As for the low-frequency and high-frequency wavelet decomposition coefficients, the local-area-energy-weighted-coefficient fusion rule is adopted and different thresholds of low-frequency and high-frequency are set. Based on biorthogonal wavelet transform and traditional pyramid decomposition algorithm, an MMW image and a visible image are fused in the experiment. Compared with the traditional pyramid decomposition, the fusion scheme based biorthogonal wavelet is more capable to retain and pick up image information, and make up the distortion of image edge. So, it has a wide application potential.
Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan
2014-01-01
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. PMID:25587878
Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan
2014-01-01
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. PMID:25587878
Research on Medical Image Enhancement Algorithm Based on GSM Model for Wavelet Coefficients
NASA Astrophysics Data System (ADS)
Wang, Lei; Jiang, Nian-de; Ning, Xing
For the complexity and application diversity of medical CT image, this article presents a medical CT Image enhancing algorithm based on Gaussian Scale Mixture Model for wavelet coefficient in the study of wavelet multi-scale analysis. The noisy image is firstly denoised in auto-adapted Wiener filter. Secondly, through the qualitative analysis and classification of wavelet coefficients for the signal and noise, the wavelet's approximate distribution and statistical characteristics are described, combining GSM(Gaussian scale mixture) model for wavelet coefficient in this paper. It is shown that this algorithm can improve the denoised result and enhanced the medical CT image obviously.
NASA Astrophysics Data System (ADS)
Ryoo, Han Chool
2000-06-01
A significant problem in physiological state monitoring systems with single data channels is high rates of false alarm. In order to reduce false alarm probability, several data channels can be integrated to enhance system performance. In this work, we have investigated a sensor fusion methodology applicable to physiological state monitoring, which combines local decisions made from dispersed detectors. Difficulties in biophysical signal processing are associated with nonstationary signal patterns and individual characteristics of human physiology resulting in nonidentical observation statistics. Thus a two compartment design, a modified version of well established fusion theory in communication systems, is presented and applied to biological signal processing where we combine discrete wavelet transforms (DWT) with sensor fusion theory. The signals were decomposed in time-frequency domain by discrete wavelet transform (DWT) to capture localized transient features. Local decisions by wavelet power analysis are followed by global decisions at the data fusion center operating under an optimization criterion, i.e., minimum error criterion (MEC). We used three signals acquired from human volunteers exposed to high-G forces at the human centrifuge/dynamic flight simulator facility in Warminster, PA. The subjects performed anti-G straining maneuvers to protect them from the adverse effects of high-G forces. These maneuvers require muscular tensing and altered breathing patterns. We attempted to determine the subject's state by detecting the presence or absence of the voluntary anti-G straining maneuvers (AGSM). During the exposure to high G force the respiratory patterns, blood pressure and electroencephalogram (EEG) were measured to determine changes in the subject's state. Experimental results show that the probability of false alarm under MEC can be significantly reduced by applying the same rule found at local thresholds to all subjects, and MEC can be employed as a
Wavelet based recognition for pulsar signals
NASA Astrophysics Data System (ADS)
Shan, H.; Wang, X.; Chen, X.; Yuan, J.; Nie, J.; Zhang, H.; Liu, N.; Wang, N.
2015-06-01
A signal from a pulsar can be decomposed into a set of features. This set is a unique signature for a given pulsar. It can be used to decide whether a pulsar is newly discovered or not. Features can be constructed from coefficients of a wavelet decomposition. Two types of wavelet based pulsar features are proposed. The energy based features reflect the multiscale distribution of the energy of coefficients. The singularity based features first classify the signals into a class with one peak and a class with two peaks by exploring the number of the straight wavelet modulus maxima lines perpendicular to the abscissa, and then implement further classification according to the features of skewness and kurtosis. Experimental results show that the wavelet based features can gain comparatively better performance over the shape parameter based features not only in the clustering and classification, but also in the error rates of the recognition tasks.
Hydrologic regionalization using wavelet-based multiscale entropy method
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Liu, Hong; Mo, Yu L.
1998-08-01
There are many textures such as woven fabrics having repeating Textron. In order to handle the textural characteristics of images with defects, this paper proposes a new method based on 2D wavelet transform. In the method, a new concept of different adaptive wavelet bases is used to match the texture pattern. The 2D wavelet transform has two different adaptive orthonormal wavelet bases for rows and columns which differ from Daubechies wavelet bases. The orthonormal wavelet bases for rows and columns are generated by genetic algorithm. The experiment result demonstrate the ability of the different adaptive wavelet bases to characterize the texture and locate the defects in the texture.
FAST TRACK COMMUNICATION: From cardinal spline wavelet bases to highly coherent dictionaries
NASA Astrophysics Data System (ADS)
Andrle, Miroslav; Rebollo-Neira, Laura
2008-05-01
Wavelet families arise by scaling and translations of a prototype function, called the mother wavelet. The construction of wavelet bases for cardinal spline spaces is generally carried out within the multi-resolution analysis scheme. Thus, the usual way of increasing the dimension of the multi-resolution subspaces is by augmenting the scaling factor. We show here that, when working on a compact interval, the identical effect can be achieved without changing the wavelet scale but reducing the translation parameter. By such a procedure we generate a redundant frame, called a dictionary, spanning the same spaces as a wavelet basis but with wavelets of broader support. We characterize the correlation of the dictionary elements by measuring their 'coherence' and produce examples illustrating the relevance of highly coherent dictionaries to problems of sparse signal representation.
NASA Astrophysics Data System (ADS)
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.
Wavelet-based acoustic recognition of aircraft
Dress, W.B.; Kercel, S.W.
1994-09-01
We describe a wavelet-based technique for identifying aircraft from acoustic emissions during take-off and landing. Tests show that the sensor can be a single, inexpensive hearing-aid microphone placed close to the ground the paper describes data collection, analysis by various technique, methods of event classification, and extraction of certain physical parameters from wavelet subspace projections. The primary goal of this paper is to show that wavelet analysis can be used as a divide-and-conquer first step in signal processing, providing both simplification and noise filtering. The idea is to project the original signal onto the orthogonal wavelet subspaces, both details and approximations. Subsequent analysis, such as system identification, nonlinear systems analysis, and feature extraction, is then carried out on the various signal subspaces.
Wavelet-based multicomponent matching pursuit trace interpolation
NASA Astrophysics Data System (ADS)
Choi, Jihun; Byun, Joongmoo; Seol, Soon Jee; Kim, Young
2016-06-01
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. Multi-component matching pursuit methods generally used the sinusoidal basis function, which have shown to be effective for interpolating multi-component 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.
Wavelet based detection of manatee vocalizations
NASA Astrophysics Data System (ADS)
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.
A New Wavelet Based Approach to Assess Hydrological Models
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Xue, Juntao; Liu, Zhengguang; Zhang, Hongwei; Wang, Shucheng
2007-01-01
Snakes, or active contours, are used extensively in computer vision and image processing application, particularly to locate object boundaries. GVF (Gradient Vector Flow) model has resolved two key problems of the traditional deformable model. However, it still requires both the initial contour being close to the target and a large amount of computation. And it is difficult to process the cupped target edge. This paper analysis the characteristics of deformable model firstly, then proposed a new method based on B-spline lifting wavelet. Experimentations based on GVF model and MRI segmentation show that the proposed method is a good resolution to the initialization sensitivity and the large computation.
Wavelet based image quality self measurements
NASA Astrophysics Data System (ADS)
Al-Jawad, Naseer; Jassim, Sabah
2010-04-01
Noise in general is considered to be degradation in image quality. Moreover image quality is measured based on the appearance of the image edges and their clarity. Most of the applications performance is affected by image quality and level of different types of degradation. In general measuring image quality and identifying the type of noise or degradation is considered to be a key factor in raising the applications performance, this task can be very challenging. Wavelet transform now a days, is widely used in different applications. These applications are mostly benefiting from the wavelet localisation in the frequency domain. The coefficients of the high frequency sub-bands in wavelet domain are represented by Laplace histogram. In this paper we are proposing to use the Laplace distribution histogram to measure the image quality and also to identify the type of degradation affecting the given image. Image quality and the level of degradation are mostly measured using a reference image with reasonable quality. The discussed Laplace distribution histogram provides a self testing measurement for the quality of the image. This measurement is based on constructing the theoretical Laplace distribution histogram of the high frequency wavelet sub-band. This construction is based on the actual standard deviation, then to be compared with the actual Laplace distribution histogram. The comparison is performed using histogram intersection method. All the experiments are performed using the extended Yale database.
Giri, Bapun K; Mitra, Chiranjit; Panigrahi, Prasanta K; Iyengar, A N Sekar
2014-12-01
The multiscale dynamics of glow discharge plasma is analysed through wavelet transform, whose scale dependent variable window size aptly captures both transients and non-stationary periodic behavior. The optimal time-frequency localization ability of the continuous Morlet wavelet is found to identify the scale dependent periodic modulations efficiently, as also the emergence of neutral turbulence and dissipation, whereas the discrete Daubechies basis set has been used for detrending the temporal behavior to reveal the multi-fractality of the underlying dynamics. The scaling exponents and the Hurst exponent have been estimated through wavelet based detrended fluctuation analysis, and also Fourier methods and rescale range analysis. PMID:25554055
Compression of Ultrasonic NDT Image by Wavelet Based Local Quantization
NASA Astrophysics Data System (ADS)
Cheng, W.; Li, L. Q.; Tsukada, K.; Hanasaki, K.
2004-02-01
Compression on ultrasonic image that is always corrupted by noise will cause `over-smoothness' or much distortion. To solve this problem to meet the need of real time inspection and tele-inspection, a compression method based on Discrete Wavelet Transform (DWT) that can also suppress the noise without losing much flaw-relevant information, is presented in this work. Exploiting the multi-resolution and interscale correlation property of DWT, a simple way named DWCs classification, is introduced first to classify detail wavelet coefficients (DWCs) as dominated by noise, signal or bi-effected. A better denoising can be realized by selective thresholding DWCs. While in `Local quantization', different quantization strategies are applied to the DWCs according to their classification and the local image property. It allocates the bit rate more efficiently to the DWCs thus achieve a higher compression rate. Meanwhile, the decompressed image shows the effects of noise suppressed and flaw characters preserved.
Wavelet-based multifractal analysis of laser biopsy imagery
NASA Astrophysics Data System (ADS)
Jagtap, Jaidip; Ghosh, Sayantan; Panigrahi, Prasanta K.; Pradhan, Asima
2012-03-01
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 disease progression can be well characterized by the Hurst exponent and the scaling exponent. Due to the use of the Daubechies' family of wavelet kernels, we can extract polynomial trends of different orders, which help us characterize the underlying processes effectively. In this study, we observe that the Hurst exponent decreases as the cancer progresses. This measure could be relatively used to differentiate between different stages of cancer which could lead to the development of a novel non-invasive method for cancer detection and characterization.
EEG analysis using wavelet-based information tools.
Rosso, O A; Martin, M T; Figliola, A; Keller, K; Plastino, A
2006-06-15
Wavelet-based informational tools for quantitative electroencephalogram (EEG) record analysis are reviewed. Relative wavelet energies, wavelet entropies and wavelet statistical complexities are used in the characterization of scalp EEG records corresponding to secondary generalized tonic-clonic epileptic seizures. In particular, we show that the epileptic recruitment rhythm observed during seizure development is well described in terms of the relative wavelet energies. In addition, during the concomitant time-period the entropy diminishes while complexity grows. This is construed as evidence supporting the conjecture that an epileptic focus, for this kind of seizures, triggers a self-organized brain state characterized by both order and maximal complexity. PMID:16675027
The Numerical Performance of Wavelets for PDEs: The Multi-Scale Finite Element
Christon, M.A.; Roach, D.W.
1998-12-23
The research summarized in this paper is part of a multiyear effort focused on evaluating the viability of wavelet bases for the solution of partial differential equations. The primary objective for this work has been to establish a foundation for hierarchical/wavelet simulation methods based upon numerical performance, computational efficiency, and the ability to exploit the hierarchical adaptive nature of wavelets. This work has demonstrated that hierarchical bases can be effective for problems with a dominant elliptic character. However, the strict enforcement of orthogonality in the usual L 2 sense is less desirable than orthogonality in the energy norm. This conclusion has led to the development of a multi-scale lineax finite element based on a hierarchical change-of-basis. This work considers the numerical and computational performance of the hierarchical Schauder basis in a Galerkin context. A unique row-column lumping procedure is developed with multi-scale solution strategies for 1-D and 2-D elliptic partial differential equations.
Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor
NASA Astrophysics Data System (ADS)
Chattopadhyay, P.; Konar, P.
2014-01-01
In this paper the theoretical aspects and feature extraction capabilities of continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are experimentally verified from the point of view of fault diagnosis of induction motors. Vertical frame vibration signal is analyzed to develop a wavelet based multi-class fault detection scheme. The redundant and high dimensionality information of CWT makes it computationally in-efficient. Using greedy-search feature selection technique (Greedy-CWT) the redundancy is eliminated to a great extent and found much superior to the widely used DWT technique, even in presence of high level of noise. The results are verified using MLP, SVM, RBF classifiers. The feature selection technique has enabled determination of the most relevant CWT scales and corresponding coefficients. Thus, the inherent limitations of CWT like proper selection of scales and redundant information are eliminated. In the present investigation `db8' is found as the best mother wavelet, due to its long period and higher number of vanishing moments, for detection of motor faults.
The Noval Properties and Construction of Multi-scale Matrix-valued Bivariate Wavelet wraps
NASA Astrophysics Data System (ADS)
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.
Optimization of wavelet- and curvelet-based denoising algorithms by multivariate SURE and GCV
NASA Astrophysics Data System (ADS)
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.
Wavelet-based verification of the quantitative precipitation forecast
NASA Astrophysics Data System (ADS)
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.
Doppler ultrasound signal denoising based on wavelet frames.
Zhang, Y; Wang, Y; Wang, W; Liu, B
2001-05-01
A novel approach was proposed to denoise the Doppler ultrasound signal. Using this method, wavelet coefficients of the Doppler signal at multiple scales were first obtained using the discrete wavelet frame analysis. Then, a soft thresholding-based denoising algorithm was employed to deal with these coefficients to get the denoised signal. In the simulation experiments, the SNR improvements and the maximum frequency estimation precision were studied for the denoised signal. From the simulation and clinical studies, it was concluded that the performance of this discrete wavelet frame (DWF) approach is higher than that of the standard (critically sampled) wavelet transform (DWT) for the Doppler ultrasound signal denoising. PMID:11381694
Wavelet-based moment invariants for pattern recognition
NASA Astrophysics Data System (ADS)
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.
Research of image enhancement of dental cast based on wavelet transformation
NASA Astrophysics Data System (ADS)
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.
Directional wavelet based features for colonic polyp classification.
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
Wavelet-based approach to character skeleton.
You, Xinge; Tang, Yuan Yan
2007-05-01
Character skeleton plays a significant role in character recognition. The strokes of a character may consist of two regions, i.e., singular and regular regions. The intersections and junctions of the strokes belong to singular region, while the straight and smooth parts of the strokes are categorized to regular region. Therefore, a skeletonization method requires two different processes to treat the skeletons in theses two different regions. All traditional skeletonization algorithms are based on the symmetry analysis technique. The major problems of these methods are as follows. 1) The computation of the primary skeleton in the regular region is indirect, so that its implementation is sophisticated and costly. 2) The extracted skeleton cannot be exactly located on the central line of the stroke. 3) The captured skeleton in the singular region may be distorted by artifacts and branches. To overcome these problems, a novel scheme of extracting the skeleton of character based on wavelet transform is presented in this paper. This scheme consists of two main steps, namely: a) extraction of primary skeleton in the regular region and b) amendment processing of the primary skeletons and connection of them in the singular region. A direct technique is used in the first step, where a new wavelet-based symmetry analysis is developed for finding the central line of the stroke directly. A novel method called smooth interpolation is designed in the second step, where a smooth operation is applied to the primary skeleton, and, thereafter, the interpolation compensation technique is proposed to link the primary skeleton, so that the skeleton in the singular region can be produced. Experiments are conducted and positive results are achieved, which show that the proposed skeletonization scheme is applicable to not only binary image but also gray-level image, and the skeleton is robust against noise and affine transform. PMID:17491454
Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation
Mu, Donghui; Chen, Dongju; Fan, Jinwei; Wang, Xiaofeng; Zhang, Feihu
2012-01-01
This paper proposes a novel method for identifying carriage errors. A general mathematical model of a guideway system is developed, based on the multi-body system method. Based on the proposed model, most error sources in the guideway system can be measured. The flatness of a workpiece measured by the PGI1240 profilometer is represented by a wavelet. Cross-correlation analysis performed to identify the error source of the carriage. The error model is developed based on experimental results on the low frequency components of the signals. With the use of wavelets, the identification precision of test signals is very high. PMID:23012558
Wavelet based feature extraction and visualization in hyperspectral tissue characterization
Denstedt, Martin; Bjorgan, Asgeir; Milanič, Matija; Randeberg, Lise Lyngsnes
2014-01-01
Hyperspectral images of tissue contain extensive and complex information relevant for clinical applications. In this work, wavelet decomposition is explored for feature extraction from such data. Wavelet methods are simple and computationally effective, and can be implemented in real-time. The aim of this study was to correlate results from wavelet decomposition in the spectral domain with physical parameters (tissue oxygenation, blood and melanin content). Wavelet decomposition was tested on Monte Carlo simulations, measurements of a tissue phantom and hyperspectral data from a human volunteer during an occlusion experiment. Reflectance spectra were decomposed, and the coefficients were correlated to tissue parameters. This approach was used to identify wavelet components that can be utilized to map levels of blood, melanin and oxygen saturation. The results show a significant correlation (p <0.02) between the chosen tissue parameters and the selected wavelet components. The tissue parameters could be mapped using a subset of the calculated components due to redundancy in spectral information. Vessel structures are well visualized. Wavelet analysis appears as a promising tool for extraction of spectral features in skin. Future studies will aim at developing quantitative mapping of optical properties based on wavelet decomposition. PMID:25574437
Coarse-to-fine wavelet-based airport detection
NASA Astrophysics Data System (ADS)
Li, Cheng; Wang, Shuigen; Pang, Zhaofeng; Zhao, Baojun
2015-10-01
Airport detection on optical remote sensing images has attracted great interest in the applications of military optics scout and traffic control. However, most of the popular techniques for airport detection from optical remote sensing images have three weaknesses: 1) Due to the characteristics of optical images, the detection results are often affected by imaging conditions, like weather situation and imaging distortion; and 2) optical images contain comprehensive information of targets, so that it is difficult for extracting robust features (e.g., intensity and textural information) to represent airport area; 3) the high resolution results in large data volume, which makes real-time processing limited. Most of the previous works mainly focus on solving one of those problems, and thus, the previous methods cannot achieve the balance of performance and complexity. In this paper, we propose a novel coarse-to-fine airport detection framework to solve aforementioned three issues using wavelet coefficients. The framework includes two stages: 1) an efficient wavelet-based feature extraction is adopted for multi-scale textural feature representation, and support vector machine(SVM) is exploited for classifying and coarsely deciding airport candidate region; and then 2) refined line segment detection is used to obtain runway and landing field of airport. Finally, airport recognition is achieved by applying the fine runway positioning to the candidate regions. Experimental results show that the proposed approach outperforms the existing algorithms in terms of detection accuracy and processing efficiency.
The Brera Multi-scale Wavelet ROSAT HRI source catalogue
NASA Astrophysics Data System (ADS)
Panzera, M. R.; Campana, S.; Covino, S.; Lazzati, D.; Mignani, R. P.; Moretti, A.; Tagliaferri, G.
2003-02-01
We present the Brera Multi-scale Wavelet ROSAT HRI source catalogue (BMW-HRI) derived from all ROSAT HRI pointed observations with exposure times 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 the presence of extended or bright sources. Images have been also visually inspected after the analysis to ensure verification. The final catalogue, derived from 4303 observations, consists of 29 089 sources detected with a detection probability of >=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. Some information is available on the web via the DIANA Interface. As an external check, we compared our catalogue with the previously available ROSHRICAT catalogue (both in its short and long versions) and we were able to recover, for the short version, ~ 90% of the entries. We computed the sky coverage of the entire HRI data set by means of simulations. The complete BMW-HRI catalogue provides a sky coverage of 732 deg2 down to a limiting flux of ~ 10-12 erg s-1 cm-2 and of 10 deg2 down to ~ 10-14 erg s-1 cm-2. We were able to compute the cosmological log(N)-log(S) distribution down to a flux of =~ 1.2 x 10-14 erg s-1 cm-2. The catalogue is only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/399/351
Chen, Hui; Lin, Zan; Mo, Lin; Wu, Hegang; Wu, Tong; Tan, Chao
2015-12-01
Spectrum is inherently local in nature since it can be thought of as a signal being composed of various frequency components. Wavelet transform (WT) is a powerful tool that partitions a signal into components with different frequency. The property of multi-resolution enables WT a very effective and natural tool for analyzing spectrum-like signal. In this study, a continuous wavelet transform (CWT)-based variable selection procedure was proposed to search for a set of informative wavelet coefficients for constructing a near-infrared (NIR) spectral diagnosis model of cancer. The CWT provided a fine multi-resolution feature space for selecting best predictors. A measure of discriminating power (DP) was defined to evaluate the coefficients. Partial least squares-discriminant analysis (PLS-DA) was used as the classification algorithm. A NIR spectral dataset associated to cancer diagnosis was used for experiment. The optimal results obtained correspond to the wavelet of db2. It revealed that on condition of having better performance on the training set, the optimal PLS-DA model using only 40 wavelet coefficients in 10 scales achieved the same performance as the one using all the variables in the original space on the test set: an overall accuracy of 93.8%, sensitivity of 92.5% and specificity of 96.3%. It confirms that the CWT-based feature selection coupled with PLS-DA is feasible and effective for constructing models of diagnostic cancer by NIR spectroscopy. PMID:26143320
NASA Astrophysics Data System (ADS)
Nejadmalayeri, Alireza
The current work develops a wavelet-based adaptive variable fidelity approach that integrates Wavelet-based Direct Numerical Simulation (WDNS), Coherent Vortex Simulations (CVS), and Stochastic Coherent Adaptive Large Eddy Simulations (SCALES). The proposed methodology employs the notion of spatially and temporarily varying wavelet thresholding combined with hierarchical wavelet-based turbulence modeling. The transition between WDNS, CVS, and SCALES regimes is achieved through two-way physics-based feedback between the modeled SGS dissipation (or other dynamically important physical quantity) and the spatial resolution. The feedback is based on spatio-temporal variation of the wavelet threshold, where the thresholding level is adjusted on the fly depending on the deviation of local significant SGS dissipation from the user prescribed level. This strategy overcomes a major limitation for all previously existing wavelet-based multi-resolution schemes: the global thresholding criterion, which does not fully utilize the spatial/temporal intermittency of the turbulent flow. Hence, the aforementioned concept of physics-based spatially variable thresholding in the context of wavelet-based numerical techniques for solving PDEs is established. The procedure consists of tracking the wavelet thresholding-factor within a Lagrangian frame by exploiting a Lagrangian Path-Line Diffusive Averaging approach based on either linear averaging along characteristics or direct solution of the evolution equation. This innovative technique represents a framework of continuously variable fidelity wavelet-based space/time/model-form adaptive multiscale methodology. This methodology has been tested and has provided very promising results on a benchmark with time-varying user prescribed level of SGS dissipation. In addition, a longtime effort to develop a novel parallel adaptive wavelet collocation method for numerical solution of PDEs has been completed during the course of the current work
On the Daubechies-based wavelet differentiation matrix
NASA Technical Reports Server (NTRS)
Jameson, Leland
1993-01-01
The differentiation matrix for a Daubechies-based wavelet basis is constructed and superconvergence is proven. That is, it will be proven that under the assumption of periodic boundary conditions that the differentiation matrix is accurate of order 2M, even though the approximation subspace can represent exactly only polynomials up to degree M-1, where M is the number of vanishing moments of the associated wavelet. It is illustrated that Daubechies-based wavelet methods are equivalent to finite difference methods with grid refinement in regions of the domain where small-scale structure is present.
NASA Astrophysics Data System (ADS)
Semler, Lindsay; Dettori, Lucia
The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.
Wavelet-expansion-based stochastic response of chain-like MDOF structures
NASA Astrophysics Data System (ADS)
Kong, Fan; Li, Jie
2015-12-01
This paper presents a wavelet-expansion-based approach for response determination of a chain-like multi-degree-of-freedom (MDOF) structure subject to full non-stationary stochastic excitations. Specifically, the generalized harmonic wavelet (GHW) is first utilized as the expansion basis to solve the dynamic equation of structures via the Galerkin treatment. In this way, a linear matrix relationship between the deterministic response and excitation can be derived. Further, considering the GHW-based representation of the stochastic processes, a time-varying power spectrum density (PSD) relationship on a certain wavelet scale or frequency band between the excitation and response is derived. Finally, pertinent numerical simulations, including deterministic dynamic analysis and Monte Carlo simulations of both the response PSD and the story-drift-based reliability, are utilized to validate the proposed approach.
NASA Astrophysics Data System (ADS)
Grenga, Temistocle
The aim of this research is to further develop a dynamically adaptive algorithm based on wavelets that is able to solve efficiently multi-dimensional compressible reactive flow problems. This work demonstrates the great potential for the method to perform direct numerical simulation (DNS) of combustion with detailed chemistry and multi-component diffusion. In particular, it addresses the performance obtained using a massive parallel implementation and demonstrates important savings in memory storage and computational time over conventional methods. In addition, fully-resolved simulations of challenging three dimensional problems involving mixing and combustion processes are performed. These problems are particularly challenging due to their strong multiscale characteristics. For these solutions, it is necessary to combine the advanced numerical techniques applied to modern computational resources.
Wavelet based ECG compression with adaptive thresholding and efficient coding.
Alshamali, A
2010-01-01
This paper proposes a new wavelet-based ECG compression technique. It is based on optimized thresholds to determine significant wavelet coefficients and an efficient coding for their positions. Huffman encoding is used to enhance the compression ratio. The proposed technique is tested using several records taken from the MIT-BIH arrhythmia database. Simulation results show that the proposed technique outperforms others obtained by previously published schemes. PMID:20608811
NASA Astrophysics Data System (ADS)
Wang, Yuan
2013-09-01
A grounded electrical source airborne transient electromagnetic (GREATEM) system on an airship enjoys high depth of prospecting and spatial resolution, as well as outstanding detection efficiency and easy flight control. However, the movement and swing of the front-fixed receiving coil can cause severe baseline drift, leading to inferior resistivity image formation. Consequently, the reduction of baseline drift of GREATEM is of vital importance to inversion explanation. To correct the baseline drift, a traditional interpolation method estimates the baseline `envelope' using the linear interpolation between the calculated start and end points of all cycles, and obtains the corrected signal by subtracting the envelope from the original signal. However, the effectiveness and efficiency of the removal is found to be low. Considering the characteristics of the baseline drift in GREATEM data, this study proposes a wavelet-based method based on multi-resolution analysis. The optimal wavelet basis and decomposition levels are determined through the iterative comparison of trial and error. This application uses the sym8 wavelet with 10 decomposition levels, and obtains the approximation at level-10 as the baseline drift, then gets the corrected signal by removing the estimated baseline drift from the original signal. To examine the performance of our proposed method, we establish a dipping sheet model and calculate the theoretical response. Through simulations, we compare the signal-to-noise ratio, signal distortion, and processing speed of the wavelet-based method and those of the interpolation method. Simulation results show that the wavelet-based method outperforms the interpolation method. We also use field data to evaluate the methods, compare the depth section images of apparent resistivity using the original signal, the interpolation-corrected signal and the wavelet-corrected signal, respectively. The results confirm that our proposed wavelet-based method is an
Template-free wavelet-based detection of local symmetries.
Puspoki, Zsuzsanna; Unser, Michael
2015-10-01
Our goal is to detect and group different kinds of local symmetries in images in a scale- and rotation-invariant way. We propose an efficient wavelet-based method to determine the order of local symmetry at each location. Our algorithm relies on circular harmonic wavelets which are used to generate steerable wavelet channels corresponding to different symmetry orders. To give a measure of local symmetry, we use the F-test to examine the distribution of the energy across different channels. We provide experimental results on synthetic images, biological micrographs, and electron-microscopy images to demonstrate the performance of the algorithm. PMID:26011883
Space-based RF signal classification using adaptive wavelet features
Caffrey, M.; Briles, S.
1995-04-01
RF signals are dispersed in frequency as they propagate through the ionosphere. For wide-band signals, this results in nonlinearly- chirped-frequency, transient signals in the VHF portion of the spectrum. This ionospheric dispersion provide a means of discriminating wide-band transients from other signals (e.g., continuous-wave carriers, burst communications, chirped-radar signals, etc.). The transient nature of these dispersed signals makes them candidates for wavelet feature selection. Rather than choosing a wavelet ad hoc, we adaptively compute an optimal mother wavelet via a neural network. Gaussian weighted, linear frequency modulate (GLFM) wavelets are linearly combined by the network to generate our application specific mother wavelet, which is optimized for its capacity to select features that discriminate between the dispersed signals and clutter (e.g., multiple continuous-wave carriers), not for its ability to represent the dispersed signal. The resulting mother wavelet is then used to extract features for a neutral network classifier. The performance of the adaptive wavelet classifier is the compared to an FFT based neural network classifier.
Watermarking ancient documents based on wavelet packets
NASA Astrophysics Data System (ADS)
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).
[Application of kalman filtering based on wavelet transform in ICP-AES].
Qin, Xia; Shen, Lan-sun
2002-12-01
Kalman filtering is a recursive algorithm, which has been proposed as an attractive alternative to correct overlapping interferences in ICP-AES. However, the noise in ICP-AES contaminates the signal arising from the analyte and hence limits the accuracy of kalman filtering. Wavelet transform is a powerful technique in signal denoising due to its multi-resolution characteristics. In this paper, first, the effect of noise on kalman filtering is discussed. Then we apply the wavelet-transform-based soft-thresholding as the pre-processing of kalman filtering. The simulation results show that the kalman filtering based on wavelet transform can effectively reduce the noise and increase the accuracy of the analysis. PMID:12914186
Hand posture recognizer based on separator wavelet networks
NASA Astrophysics Data System (ADS)
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.
Wavelet transformation based watermarking technique for human electrocardiogram (ECG).
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
A pseudo wavelet-based method for accurate tagline tracing on tagged MR images of the tongue
NASA Astrophysics Data System (ADS)
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.
[ECoG classification based on wavelet variance].
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
[An algorithm of a wavelet-based medical image quantization].
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
Three-dimensional compression scheme based on wavelet transform
NASA Astrophysics Data System (ADS)
Yang, Wu; Xu, Hui; Liao, Mengyang
1999-03-01
In this paper, a 3D compression method based on separable wavelet transform is discussed in detail. The most commonly used digital modalities generate multiple slices in a single examination, which are normally anatomically or physiologically correlated to each other. 3D wavelet compression methods can achieve more efficient compression by exploring the correlation between slices. The first step is based on a separable 3D wavelet transform. Considering the difference between pixel distances within a slice and those between slices, one biorthogonal Antoninin filter bank is applied within 2D slices and a second biorthogonal Villa4 filter bank on the slice direction. Then, S+P transform is applied in the low-resolution wavelet components and an optimal quantizer is presented after analysis of the quantization noise. We use an optimal bit allocation algorithm, which, instead of eliminating the coefficients of high-resolution components in smooth areas, minimizes the system reconstruction distortion at a given bit-rate. Finally, to remain high coding efficiency and adapt to different properties of each component, a comprehensive entropy coding method is proposed, in which arithmetic coding method is applied in high-resolution components and adaptive Huffman coding method in low-resolution components. Our experimental results are evaluated by several image measures and our 3D wavelet compression scheme is proved to be more efficient than 2D wavelet compression.
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
Wavelet-based reconstruction of fossil-fuel CO2 emissions from sparse measurements
NASA Astrophysics Data System (ADS)
McKenna, S. A.; Ray, J.; Yadav, V.; Van Bloemen Waanders, B.; Michalak, A. M.
2012-12-01
We present a method to estimate spatially resolved fossil-fuel CO2 (ffCO2) emissions from sparse measurements of time-varying CO2 concentrations. It is based on the wavelet-modeling of the strongly non-stationary spatial distribution of ffCO2 emissions. The dimensionality of the wavelet model is first reduced using images of nightlights, which identify regions of human habitation. Since wavelets are a multiresolution basis set, most of the reduction is accomplished by removing fine-scale wavelets, in the regions with low nightlight radiances. The (reduced) wavelet model of emissions is propagated through an atmospheric transport model (WRF) to predict CO2 concentrations at a handful of measurement sites. The estimation of the wavelet model of emissions i.e., inferring the wavelet weights, is performed by fitting to observations at the measurement sites. This is done using Staggered Orthogonal Matching Pursuit (StOMP), which first identifies (and sets to zero) the wavelet coefficients that cannot be estimated from the observations, before estimating the remaining coefficients. This model sparsification and fitting is performed simultaneously, allowing us to explore multiple wavelet-models of differing complexity. This technique is borrowed from the field of compressive sensing, and is generally used in image and video processing. We test this approach using synthetic observations generated from emissions from the Vulcan database. 35 sensor sites are chosen over the USA. FfCO2 emissions, averaged over 8-day periods, are estimated, at a 1 degree spatial resolutions. We find that only about 40% of the wavelets in emission model can be estimated from the data; however the mix of coefficients that are estimated changes with time. Total US emission can be reconstructed with about ~5% errors. The inferred emissions, if aggregated monthly, have a correlation of 0.9 with Vulcan fluxes. We find that the estimated emissions in the Northeast US are the most accurate. Sandia
NASA Astrophysics Data System (ADS)
Qian, Fang; Guo, Jin; Sun, Tao; Wang, Tingfeng
2015-04-01
Laser active imaging systems are widespread tools used in region surveillance and threat identification. However, the photoelectric imaging detector in the imaging systems is easy to be disturbed and this leads to errors of the recognition and even the missing of the target. In this paper, a novel wavelet-weighted multi-scale structural similarity (WWMS-SSIM) algorithm is proposed. 2-D four-level wavelet decomposition is performed for the original and disturbed images. Each image can be partitioned into one low-frequency subband (LL) and a series of octave high-frequency subbands (HL, LH and HH). Luminance, contrast and structure comparison are computed in different subbands with different weighting factors. Based on the results of the above, we can construct a modified WWMS-SSIM. Cross-distorted image quality assessment experiments show that the WWMS-SSIM algorithm is more suitable for the subjective visual feeling comparing with NMSE and SSIM. In the laser-dazzling image quality assessment experiments, the WWMS-SSIM gives more reasonable evaluations to the images with different power and laser spot positions, which can be useful to give the guidance of the laser active imaging system defense and application.
Application of wavelet-based multiple linear regression model to rainfall forecasting in Australia
NASA Astrophysics Data System (ADS)
He, X.; Guan, H.; Zhang, X.; Simmons, C.
2013-12-01
In this study, a wavelet-based multiple linear regression model is applied to forecast monthly rainfall in Australia by using monthly historical rainfall data and climate indices as inputs. The wavelet-based model is constructed by incorporating the multi-resolution analysis (MRA) with the discrete wavelet transform and multiple linear regression (MLR) model. The standardized monthly rainfall anomaly and large-scale climate index time series are decomposed using MRA into a certain number of component subseries at different temporal scales. The hierarchical lag relationship between the rainfall anomaly and each potential predictor is identified by cross correlation analysis with a lag time of at least one month at different temporal scales. The components of predictor variables with known lag times are then screened with a stepwise linear regression algorithm to be selectively included into the final forecast model. The MRA-based rainfall forecasting method is examined with 255 stations over Australia, and compared to the traditional multiple linear regression model based on the original time series. The models are trained with data from the 1959-1995 period and then tested in the 1996-2008 period for each station. The performance is compared with observed rainfall values, and evaluated by common statistics of relative absolute error and correlation coefficient. The results show that the wavelet-based regression model provides considerably more accurate monthly rainfall forecasts for all of the selected stations over Australia than the traditional regression model.
A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring
Liao, T. W.; Ting, C.F.; Qu, Jun; Blau, Peter Julian
2007-01-01
Grinding wheel surface condition changes as more material is removed. This paper presents a wavelet-based methodology for grinding wheel condition monitoring based on acoustic emission (AE) signals. Grinding experiments in creep feed mode were conducted to grind alumina specimens with a resinoid-bonded diamond wheel using two different conditions. During the experiments, AE signals were collected when the wheel was 'sharp' and when the wheel was 'dull'. Discriminant features were then extracted from each raw AE signal segment using the discrete wavelet decomposition procedure. An adaptive genetic clustering algorithm was finally applied to the extracted features in order to distinguish different states of grinding wheel condition. The test results indicate that the proposed methodology can achieve 97% clustering accuracy for the high material removal rate condition, 86.7% for the low material removal rate condition, and 76.7% for the combined grinding conditions if the base wavelet, the decomposition level, and the GA parameters are properly selected.
Li, Jingsong; Yu, Benli; Fischer, Horst
2015-04-01
This paper presents a novel methodology-based discrete wavelet transform (DWT) and the choice of the optimal wavelet pairs to adaptively process tunable diode laser absorption spectroscopy (TDLAS) spectra for quantitative analysis, such as molecular spectroscopy and trace gas detection. The proposed methodology aims to construct an optimal calibration model for a TDLAS spectrum, regardless of its background structural characteristics, thus facilitating the application of TDLAS as a powerful tool for analytical chemistry. The performance of the proposed method is verified using analysis of both synthetic and observed signals, characterized with different noise levels and baseline drift. In terms of fitting precision and signal-to-noise ratio, both have been improved significantly using the proposed method. PMID:25741689
Multiple wavelet-tree-based image coding and robust transmission
NASA Astrophysics Data System (ADS)
Cao, Lei; Chen, Chang Wen
2004-10-01
In this paper, we present techniques based on multiple wavelet-tree coding for robust image transmission. The algorithm of set partitioning in hierarchical trees (SPIHT) is a state-of-the-art technique for image compression. This variable length coding (VLC) technique, however, is extremely sensitive to channel errors. To improve the error resilience capability and in the meantime to keep the high source coding efficiency through VLC, we propose to encode each wavelet tree or a group of wavelet trees using SPIHT algorithm independently. Instead of encoding the entire image as one bitstream, multiple bitstreams are generated. Therefore, error propagation is limited within individual bitstream. Two methods based on subsampling and human visual sensitivity are proposed to group the wavelet trees. The multiple bitstreams are further protected by the rate compatible puncture convolutional (RCPC) codes. Unequal error protection are provided for both different bitstreams and different bit segments inside each bitstream. We also investigate the improvement of error resilience through error resilient entropy coding (EREC) and wavelet tree coding when channels are slightly corruptive. A simple post-processing technique is also proposed to alleviate the effect of residual errors. We demonstrate through simulations that systems with these techniques can achieve much better performance than systems transmitting a single bitstream in noisy environments.
Color graph based wavelet transform with perceptual information
NASA Astrophysics Data System (ADS)
Malek, Mohamed; Helbert, David; Carré, Philippe
2015-09-01
We propose a numerical strategy to define a multiscale analysis for color and multicomponent images based on the representation of data on a graph. Our approach consists of computing the graph of an image using the psychovisual information and analyzing it by using the spectral graph wavelet transform. We suggest introducing color dimension into the computation of the weights of the graph and using the geodesic distance as a mean of distance measurement. We thus have defined a wavelet transform based on a graph with perceptual information by using the CIELab color distance. This new representation is illustrated with denoising and inpainting applications. Overall, by introducing psychovisual information in the graph computation for the graph wavelet transform, we obtain very promising results. Thus, results in image restoration highlight the interest of the appropriate use of color information.
Contrast-based image fusion using the discrete wavelet transform
NASA Astrophysics Data System (ADS)
Pu, Tian; Ni, GuoGiang
2000-08-01
We introduce a contrast-based image fusion method using the wavelet multiresolution analysis. This method includes three steps. First, the multiresolution architectures of the two original input images are obtained using the discrete wavelet transform. A new concept called directive contrast is presented. Second, the multiresolution architecture of the fused image can be achieved by selecting the corresponding subband signals of each input image based on the directive contrast. Finally, the fused image is reconstructed using the inverse wavelet transform. This algorithm is relevant to visual sensitivity and is tested by merging visual and IR images. The result shows that the fused image can integrate the details of each original image. The visual aesthetics and the computed SNRs of the fused images show that the new approaches can provide better fusion results than some previous multiresolution fusion methods.
Comparative study of different wavelet based neural network models for rainfall-runoff modeling
NASA Astrophysics Data System (ADS)
Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.
2014-07-01
The use of wavelet transformation in rainfall-runoff modeling has become popular because of its ability to simultaneously deal with both the spectral and the temporal information contained within time series data. The selection of an appropriate wavelet function plays a crucial role for successful implementation of the wavelet based rainfall-runoff artificial neural network models as it can lead to further enhancement in the model performance. The present study is therefore conducted to evaluate the effects of 23 mother wavelet functions on the performance of the hybrid wavelet based artificial neural network rainfall-runoff models. The hybrid Multilayer Perceptron Neural Network (MLPNN) and the Radial Basis Function Neural Network (RBFNN) models are developed in this study using both the continuous wavelet and the discrete wavelet transformation types. The performances of the 92 developed wavelet based neural network models with all the 23 mother wavelet functions are compared with the neural network models developed without wavelet transformations. It is found that among all the models tested, the discrete wavelet transform multilayer perceptron neural network (DWTMLPNN) and the discrete wavelet transform radial basis function (DWTRBFNN) models at decomposition level nine with the db8 wavelet function has the best performance. The result also shows that the pre-processing of input rainfall data by the wavelet transformation can significantly increases performance of the MLPNN and the RBFNN rainfall-runoff models.
Wavelet-based analysis of circadian behavioral rhythms.
Leise, Tanya L
2015-01-01
The challenging problems presented by noisy biological oscillators have led to the development of a great variety of methods for accurately estimating rhythmic parameters such as period and amplitude. This chapter focuses on wavelet-based methods, which can be quite effective for assessing how rhythms change over time, particularly if time series are at least a week in length. These methods can offer alternative views to complement more traditional methods of evaluating behavioral records. The analytic wavelet transform can estimate the instantaneous period and amplitude, as well as the phase of the rhythm at each time point, while the discrete wavelet transform can extract the circadian component of activity and measure the relative strength of that circadian component compared to those in other frequency bands. Wavelet transforms do not require the removal of noise or trend, and can, in fact, be effective at removing noise and trend from oscillatory time series. The Fourier periodogram and spectrogram are reviewed, followed by descriptions of the analytic and discrete wavelet transforms. Examples illustrate application of each method and their prior use in chronobiology is surveyed. Issues such as edge effects, frequency leakage, and implications of the uncertainty principle are also addressed. PMID:25662453
An Automated Parallel Image Registration Technique Based on the Correlation of Wavelet Features
NASA Technical Reports Server (NTRS)
LeMoigne, Jacqueline; Campbell, William J.; Cromp, Robert F.; Zukor, Dorothy (Technical Monitor)
2001-01-01
With the increasing importance of multiple platform/multiple remote sensing missions, fast and automatic integration of digital data from disparate sources has become critical to the success of these endeavors. Our work utilizes maxima of wavelet coefficients to form the basic features of a correlation-based automatic registration algorithm. Our wavelet-based registration algorithm is tested successfully with data from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Landsat/Thematic Mapper(TM), which differ by translation and/or rotation. By the choice of high-frequency wavelet features, this method is similar to an edge-based correlation method, but by exploiting the multi-resolution nature of a wavelet decomposition, our method achieves higher computational speeds for comparable accuracies. This algorithm has been implemented on a Single Instruction Multiple Data (SIMD) massively parallel computer, the MasPar MP-2, as well as on the CrayT3D, the Cray T3E and a Beowulf cluster of Pentium workstations.
Entropy-based optimization of wavelet spatial filters.
Farina, Darino; Kamavuako, Ernest Nlandu; Wu, Jian; Naddeo, Francesco
2008-03-01
A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible. PMID:18334382
NASA Astrophysics Data System (ADS)
Mukhopadhyay, Sabyasachi; Das, Nandan K.; Pradhan, Asima; Ghosh, Nirmalya; Panigrahi, Prasanta K.
2014-02-01
The objective of the present work is to diagnose pre-cancer by wavelet transform and multi-fractal de-trended fluctuation analysis of DIC images of normal and different grades of cancer tissues. Our DIC imaging and fluctuation analysis methods (Discrete and continuous wavelet transform, MFDFA) confirm the ability to diagnose and detect the early stage of cancer in cervical tissue.
Sequential damage detection based on the continuous wavelet transform
NASA Astrophysics Data System (ADS)
Liao, Yizheng; Balafas, Konstantinos; Rajagopal, Ram; Kiremidjian, Anne S.
2015-03-01
This paper presents a sequential structural damage detection algorithm that is based on a statistical model for the wavelet transform of the structural responses. The detector uses the coefficients of the wavelet model and does not require prior knowledge of the structural properties. Principal Component Analysis is applied to select and extract the most sensitive features from the wavelet coefficients as the damage sensitive features. The damage detection algorithm is validated using the simulation data collected from a four-story steel moment frame. Various features have been explored and the detection algorithm was able to identify damage. Additionally, we show that for a desired probability of false alarm, the proposed detector is asymptotically optimal on the expected delay.
Adaptive wavelet-based recognition of oscillatory patterns on electroencephalograms
NASA Astrophysics Data System (ADS)
Nazimov, Alexey I.; Pavlov, Alexey N.; Hramov, Alexander E.; Grubov, Vadim V.; Koronovskii, Alexey A.; Sitnikova, Evgenija Y.
2013-02-01
The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.
Wavelet based hierarchical coding scheme for radar image compression
NASA Astrophysics Data System (ADS)
Sheng, Wen; Jiao, Xiaoli; He, Jifeng
2007-12-01
This paper presents a wavelet based hierarchical coding scheme for radar image compression. Radar signal is firstly quantized to digital signal, and reorganized as raster-scanned image according to radar's repeated period frequency. After reorganization, the reformed image is decomposed to image blocks with different frequency band by 2-D wavelet transformation, each block is quantized and coded by the Huffman coding scheme. A demonstrating system is developed, showing that under the requirement of real time processing, the compression ratio can be very high, while with no significant loss of target signal in restored radar image.
Characterizing cerebrovascular dynamics with the wavelet-based multifractal formalism
NASA Astrophysics Data System (ADS)
Pavlov, A. N.; Abdurashitov, A. S.; Sindeeva, O. A.; Sindeev, S. S.; Pavlova, O. N.; Shihalov, G. M.; Semyachkina-Glushkovskaya, O. V.
2016-01-01
Using the wavelet-transform modulus maxima (WTMM) approach we study the dynamics of cerebral blood flow (CBF) in rats aiming to reveal responses of macro- and microcerebral circulations to changes in the peripheral blood pressure. We show that the wavelet-based multifractal formalism allows quantifying essentially different reactions in the CBF-dynamics at the level of large and small cerebral vessels. We conclude that unlike the macrocirculation that is nearly insensitive to increased peripheral blood pressure, the microcirculation is characterized by essential changes of the CBF-complexity.
Perceptually lossless wavelet-based compression for medical images
NASA Astrophysics Data System (ADS)
Lin, Nai-wen; Yu, Tsaifa; Chan, Andrew K.
1997-05-01
In this paper, we present a wavelet-based medical image compression scheme so that images displayed on different devices are perceptually lossless. Since visual sensitivity of human varies with different subbands, we apply the perceptual lossless criteria to quantize the wavelet transform coefficients of each subband such that visual distortions are reduced to unnoticeable. Following this, we use a high compression ratio hierarchical tree to code these coefficients. Experimental results indicate that our perceptually lossless coder achieves a compression ratio 2-5 times higher than typical lossless compression schemes while producing perceptually identical image content on the target display device.
Low-memory-usage image coding with line-based wavelet transform
NASA Astrophysics Data System (ADS)
Ye, Linning; Guo, Jiangling; Nutter, Brian; Mitra, Sunanda
2011-02-01
When compared to the traditional row-column wavelet transform, the line-based wavelet transform can achieve significant memory savings. However, the design of an image codec using the line-based wavelet transform is an intricate task because of the irregular order in which the wavelet coefficients are generated. The independent block coding feature of JPEG2000 makes it work effectively with the line-based wavelet transform. However, with wavelet tree-based image codecs, such as set partitioning in hierarchical trees, the memory usage of the codecs does not realize significant advantage with the line-based wavelet transform because many wavelet coefficients must be buffered before the coding starts. In this paper, the line-based wavelet transform was utilized to facilitate backward coding of wavelet trees (BCWT). Although the BCWT algorithm is a wavelet tree-based algorithm, its coding order differs from that of the traditional wavelet tree-based algorithms, which allows the proposed line-based image codec to become more memory efficient than other line-based image codecs, including line-based JPEG2000, while still offering comparable rate distortion performance and much lower system complexity.
Use of Multi-Resolution Wavelet Feature Pyramids for Automatic Registration of Multi-Sensor Imagery
NASA Technical Reports Server (NTRS)
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.
Wavelet-based ground vehicle recognition using acoustic signals
NASA Astrophysics Data System (ADS)
Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.
1996-03-01
We present, in this paper, a wavelet-based acoustic signal analysis to remotely recognize military vehicles using their sound intercepted by acoustic sensors. Since expedited signal recognition is imperative in many military and industrial situations, we developed an algorithm that provides an automated, fast signal recognition once implemented in a real-time hardware system. This algorithm consists of wavelet preprocessing, feature extraction and compact signal representation, and a simple but effective statistical pattern matching. The current status of the algorithm does not require any training. The training is replaced by human selection of reference signals (e.g., squeak or engine exhaust sound) distinctive to each individual vehicle based on human perception. This allows a fast archiving of any new vehicle type in the database once the signal is collected. The wavelet preprocessing provides time-frequency multiresolution analysis using discrete wavelet transform (DWT). Within each resolution level, feature vectors are generated from statistical parameters and energy content of the wavelet coefficients. After applying our algorithm on the intercepted acoustic signals, the resultant feature vectors are compared with the reference vehicle feature vectors in the database using statistical pattern matching to determine the type of vehicle from where the signal originated. Certainly, statistical pattern matching can be replaced by an artificial neural network (ANN); however, the ANN would require training data sets and time to train the net. Unfortunately, this is not always possible for many real world situations, especially collecting data sets from unfriendly ground vehicles to train the ANN. Our methodology using wavelet preprocessing and statistical pattern matching provides robust acoustic signal recognition. We also present an example of vehicle recognition using acoustic signals collected from two different military ground vehicles. In this paper, we will
Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints
I. W. Ginsberg
1999-09-01
Multiresolutional decompositions known as spectral fingerprints are often used to extract spectral features from multispectral/hyperspectral data. In this study, the authors investigate the use of wavelet-based algorithms for generating spectral fingerprints. The wavelet-based algorithms are compared to the currently used method, traditional convolution with first-derivative Gaussian filters. The comparison analyses consists of two parts: (a) the computational expense of the new method is compared with the computational costs of the current method and (b) the outputs of the wavelet-based methods are compared with those of the current method to determine any practical differences in the resulting spectral fingerprints. The results show that the wavelet-based algorithms can greatly reduce the computational expense of generating spectral fingerprints, while practically no differences exist in the resulting fingerprints. The analysis is conducted on a database of hyperspectral signatures, namely, Hyperspectral Digital Image Collection Experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting fingerprints is on the order of 0.02.
3D Wavelet-Based Filter and Method
Moss, William C.; Haase, Sebastian; Sedat, John W.
2008-08-12
A 3D wavelet-based filter for visualizing and locating structural features of a user-specified linear size in 2D or 3D image data. The only input parameter is a characteristic linear size of the feature of interest, and the filter output contains only those regions that are correlated with the characteristic size, thus denoising the image.
Iterative support detection-based split Bregman method for wavelet frame-based image inpainting.
He, Liangtian; Wang, Yilun
2014-12-01
The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piece-wise smooth functions, such as images, and the corresponding wavelet frame-based image restoration models are mostly based on the penalization of the l1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame l1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional l1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based l1 norm image inpainting model as well as some typical l(p) (0 ≤ p < 1) norm-based nonconvex algorithms such as mean doubly augmented Lagrangian method, in terms of better preservation of sharp edges, due to their failing to make use of the structure of the wavelet frame coefficients. PMID:25312924
Digital audio signal filtration based on the dual-tree wavelet transform
NASA Astrophysics Data System (ADS)
Yaseen, A. S.; Pavlov, A. N.
2015-07-01
A new method of digital audio signal filtration based on the dual-tree wavelet transform is described. An adaptive approach is proposed that allows the automatic adjustment of parameters of the wavelet filter to be optimized. A significant improvement of the quality of signal filtration is demonstrated in comparison to the traditionally used filters based on the discrete wavelet transform.
Fast wavelet based sparse approximate inverse preconditioner
Wan, W.L.
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.
Network Anomaly Detection Based on Wavelet Analysis
NASA Astrophysics Data System (ADS)
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.
Multi-resolution Gabor wavelet feature extraction for needle detection in 3D ultrasound
NASA Astrophysics Data System (ADS)
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.
A wavelet-based approach to face verification/recognition
NASA Astrophysics Data System (ADS)
Jassim, Sabah; Sellahewa, Harin
2005-10-01
Face verification/recognition is a tough challenge in comparison to identification based on other biometrics such as iris, or fingerprints. Yet, due to its unobtrusive nature, the face is naturally suitable for security related applications. Face verification process relies on feature extraction from face images. Current schemes are either geometric-based or template-based. In the latter, the face image is statistically analysed to obtain a set of feature vectors that best describe it. Performance of a face verification system is affected by image variations due to illumination, pose, occlusion, expressions and scale. This paper extends our recent work on face verification for constrained platforms, where the feature vector of a face image is the coefficients in the wavelet transformed LL-subbands at depth 3 or more. It was demonstrated that the wavelet-only feature vector scheme has a comparable performance to sophisticated state-of-the-art when tested on two benchmark databases (ORL, and BANCA). The significance of those results stem from the fact that the size of the k-th LL- subband is 1/4k of the original image size. Here, we investigate the use of wavelet coefficients in various subbands at level 3 or 4 using various wavelet filters. We shall compare the performance of the wavelet-based scheme for different filters at different subbands with a number of state-of-the-art face verification/recognition schemes on two benchmark databases, namely ORL and the control section of BANCA. We shall demonstrate that our schemes have comparable performance to (or outperform) the best performing other schemes.
Wavelet-based multicomponent matching pursuit trace interpolation
NASA Astrophysics Data System (ADS)
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.
Fresnelets: new multiresolution wavelet bases for digital holography.
Liebling, Michael; Blu, Thierry; Unser, Michael
2003-01-01
We propose a construction of new wavelet-like bases that are well suited for the reconstruction and processing of optically generated Fresnel holograms recorded on CCD-arrays. The starting point is a wavelet basis of L2 to which we apply a unitary Fresnel transform. The transformed basis functions are shift-invariant on a level-by-level basis but their multiresolution properties are governed by the special form that the dilation operator takes in the Fresnel domain. We derive a Heisenberg-like uncertainty relation that relates the localization of Fresnelets with that of their associated wavelet basis. According to this criterion, the optimal functions for digital hologram processing turn out to be Gabor functions, bringing together two separate aspects of the holography inventor's work. We give the explicit expression of orthogonal and semi-orthogonal Fresnelet bases corresponding to polynomial spline wavelets. This special choice of Fresnelets is motivated by their near-optimal localization properties and their approximation characteristics. We then present an efficient multiresolution Fresnel transform algorithm, the Fresnelet transform. This algorithm allows for the reconstruction (backpropagation) of complex scalar waves at several user-defined, wavelength-independent resolutions. Furthermore, when reconstructing numerical holograms, the subband decomposition of the Fresnelet transform naturally separates the image to reconstruct from the unwanted zero-order and twin image terms. This greatly facilitates their suppression. We show results of experiments carried out on both synthetic (simulated) data sets as well as on digitally acquired holograms. PMID:18237877
A wavelet-based approach to fall detection.
Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo
2015-01-01
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the "prototype fall".In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. PMID:26007719
A Wavelet-Based Approach to Fall Detection
Palmerini, Luca; Bagalà, Fabio; Zanetti, Andrea; Klenk, Jochen; Becker, Clemens; Cappello, Angelo
2015-01-01
Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms. PMID:26007719
Wavelet decomposition-based efficient face liveness detection
NASA Astrophysics Data System (ADS)
Moniruzzaman, Md.; Alam, Mohammad S.
2016-04-01
Existing face recognition systems are susceptible to spoofing attacks. So, Face liveness detection is a pivotal part for reliable face recognition, which has recently acknowledged vast attention. In this paper we propose a wavelet decomposition based face liveness recognition system using an energy calculation technique. Live faces contain high energy components compared to fake or printed image. In this paper, we calculate energy components of live face as well as fake face using discrete wavelet decomposition method. We analyze percentage of energy at different levels as well as for different wavelet basis function. We also analyze percentage of energy at different RGB bands and efficient face liveness detection method has been proposed. Discrete wavelet representation has been used to calculate decomposed energy components. Moreover, it provides differentiation of several spatial orientations as well as average and detailed information which are missing in the fake faces. This technique provides excellent discrimination capability when compared to the previously reported works based on the discrete Fourier transform and n-dimensional Fourier transform operations. To verify the proposed approach, we tested the performance using various face antispoofing datasets such as university of south Alabama (UFAD), and MSU face antispoofing dataset which incorporates different types of attacks. The test results obtained using the proposed technique shows better performance compared to existing techniques.
On the spline-based wavelet differentiation matrix
NASA Technical Reports Server (NTRS)
Jameson, Leland
1993-01-01
The differentiation matrix for a spline-based wavelet basis is constructed. Given an n-th order spline basis it is proved that the differentiation matrix is accurate of order 2n + 2 when periodic boundary conditions are assumed. This high accuracy, or superconvergence, is lost when the boundary conditions are no longer periodic. Furthermore, it is shown that spline-based bases generate a class of compact finite difference schemes.
Wavelet packet-based insufficiency murmurs analysis method
NASA Astrophysics Data System (ADS)
Choi, Samjin; Jiang, Zhongwei
2007-12-01
In this paper, the aortic and mitral insufficiency murmurs analysis method using the wavelet packet technique is proposed for classifying the valvular heart defects. Considering the different frequency distributions between the normal sound and insufficiency murmurs in frequency domain, we used two properties such as the relative wavelet energy and the Shannon wavelet entropy which described the energy information and the entropy information at the selected frequency band, respectively. Then, the signal to murmur ratio (SMR) measures which could mean the ratio between the frequency bands for normal heart sounds and for aortic and mitral insufficiency murmurs allocated to 15.62-187.50 Hz and 187.50-703.12 Hz respectively, were employed as a classification manner to identify insufficiency murmurs. The proposed measures were validated by some case studies. The 194 heart sound signals with 48 normal and 146 abnormal sound cases acquired from 6 healthy volunteers and 30 patients were tested. The normal sound signals recorded by applying a self-produced wireless electric stethoscope system to subjects with no history of other heart complications were used. Insufficiency murmurs were grouped into two valvular heart defects such as aortic insufficiency and mitral insufficiency. These murmur subjects included no other coexistent valvular defects. As a result, the proposed insufficiency murmurs detection method showed relatively very high classification efficiency. Therefore, the proposed heart sound classification method based on the wavelet packet was validated for the classification of valvular heart defects, especially insufficiency murmurs.
Real-time wavelet based blur estimation on cell BE platform
NASA Astrophysics Data System (ADS)
Lukic, Nemanja; Platiša, Ljiljana; Pižurica, Aleksandra; Philips, Wilfried; Temerinac, Miodrag
2010-01-01
We propose a real-time system for blur estimation using wavelet decomposition. The system is based on an emerging multi-core microprocessor architecture (Cell Broadband Engine, Cell BE) known to outperform any available general purpose or DSP processor in the domain of real-time advanced video processing solutions. We start from a recent wavelet domain blur estimation algorithm which uses histograms of a local regularity measure called average cone ratio (ACR). This approach has shown a very good potential for assessing the level of blur in the image yet some important aspects remain to be addressed in order for the method to become a practically working one. Some of these aspects are explored in our work. Furthermore, we develop an efficient real-time implementation of the novelty metric and integrate it into a system that captures live video. The proposed system estimates blur extent and renders the results to the remote user in real-time.
Targets tracking relating method based on fuzzy reasoning and wavelet energy detection
NASA Astrophysics Data System (ADS)
Liu, Gang; Shi, Yao-wu
2008-12-01
Targets tracking methods based digital image are used widely. During the process of multi-targets tracking, when the targets falling in the tracking door are not only one, joint data association is used as a traditional method. But there is so much operation in this method that the need of real time can not be meet. Targets tracking relating method based on fuzzy reasoning and expending Kalman filter is adopted in this paper. Firstly, wavelet energy is adopted to detect targets in the image. The wavelet of bior1.3 is used to decompose image. It is anti-symmetric wavelet and is fit for detecting variational information in horizontal and vertical direction. Because the detected image generally have natural texture background, such as sea, sky, and so on. The targets have high frequency characteristic at horizontal and vertical direction in these images, but the background has high frequency characteristic at one of the two direction. The wavelet energy of targets at two direction go beyond that of background. Then through selecting self-adaptive threshold, targets and background can be divided. By the method of figure centers distance classifying, the figure centers of every target can be calculated. Secondly, Targets figure-center coordinate is detecting through self-adapting figure-center filter. Thirdly, fuzzy reasoning method is used in the progress of data relating. It is easy in engineering application compared with the JPDA arithmetic. In the experiment, after several frames relating, the mean square error of row coordinate reduce from 0.278 to 0.148. The errors of multi-targets figure-center coordinate gradually decrease. The forecast veracity gradually increase.
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
Glassman, Elena L
2005-11-01
This paper describes the development and testing of a wavelet-like filter, named the SNAP, created from a neural activity simulation and used, in place of a wavelet, in a wavelet transform for improving EEG wavelet analysis, intended for brain-computer interfaces. The hypothesis is that an optimal wavelet can be approximated by deriving it from underlying components of the EEG. The SNAP was compared to standard wavelets by measuring Support Vector Machine-based EEG classification accuracy when using different wavelets/filters for EEG analysis. When classifying P300 evoked potentials, the error, as a function of the wavelet/filter used, ranged from 6.92% to 11.99%, almost twofold. Classification using the SNAP was more accurate than that with any of the six standard wavelets tested. Similarly, when differentiating between preparation for left- or right-hand movements, classification using the SNAP was more accurate (10.03% error) than for four out of five of the standard wavelets (9.54% to 12.00% error) and internationally competitive (7% error) on the 2001 NIPS competition test set. Phenomena shown only in maps of discriminatory EEG activity may explain why the SNAP appears to have promise for improving EEG wavelet analysis. It represents the initial exploration of a potential family of EEG-specific wavelets. PMID:16285389
Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach
NASA Astrophysics Data System (ADS)
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.
A family of orthonormal wavelet bases with dilation factor 4
NASA Astrophysics Data System (ADS)
Karoui, Abderrazek
2006-05-01
In this paper, we study a method for the construction of orthonormal wavelet bases with dilation factor 4. More precisely, for any integer M>0, we construct an orthonormal scaling filter mM([xi]) that generates a mother scaling function [phi]M, associated with the dilation factor 4. The computation of the different coefficients of mM([xi])2 is done by the use of a simple iterative method. Also, this work shows how this construction method provides us with a whole family of compactly supported orthonormal wavelet bases with arbitrary high regularity. A first estimate of [alpha](M), the asymptotic regularity of [phi]M is given by [alpha](M)~0.25M. Examples are provided to illustrate the results of this work.
Wavelet-based image analysis system for soil texture analysis
NASA Astrophysics Data System (ADS)
Sun, Yun; Long, Zhiling; Jang, Ping-Rey; Plodinec, M. John
2003-05-01
Soil texture is defined as the relative proportion of clay, silt and sand found in a given soil sample. It is an important physical property of soil that affects such phenomena as plant growth and agricultural fertility. Traditional methods used to determine soil texture are either time consuming (hydrometer), or subjective and experience-demanding (field tactile evaluation). Considering that textural patterns observed at soil surfaces are uniquely associated with soil textures, we propose an innovative approach to soil texture analysis, in which wavelet frames-based features representing texture contents of soil images are extracted and categorized by applying a maximum likelihood criterion. The soil texture analysis system has been tested successfully with an accuracy of 91% in classifying soil samples into one of three general categories of soil textures. In comparison with the common methods, this wavelet-based image analysis approach is convenient, efficient, fast, and objective.
Wavelet-based audio embedding and audio/video compression
NASA Astrophysics Data System (ADS)
Mendenhall, Michael J.; Claypoole, Roger L., Jr.
2001-12-01
Watermarking, traditionally used for copyright protection, is used in a new and exciting way. An efficient wavelet-based watermarking technique embeds audio information into a video signal. Several effective compression techniques are applied to compress the resulting audio/video signal in an embedded fashion. This wavelet-based compression algorithm incorporates bit-plane coding, index coding, and Huffman coding. To demonstrate the potential of this audio embedding and audio/video compression algorithm, we embed an audio signal into a video signal and then compress. Results show that overall compression rates of 15:1 can be achieved. The video signal is reconstructed with a median PSNR of nearly 33 dB. Finally, the audio signal is extracted from the compressed audio/video signal without error.
Wavelet-based ultrasound image denoising: performance analysis and comparison.
Rizi, F Yousefi; Noubari, H Ahmadi; Setarehdan, S K
2011-01-01
Ultrasound images are generally affected by multiplicative speckle noise, which is mainly due to the coherent nature of the scattering phenomenon. Speckle noise filtering is thus a critical pre-processing step in medical ultrasound imaging provided that the diagnostic features of interest are not lost. A comparative study of the performance of alternative wavelet based ultrasound image denoising methods is presented in this article. In particular, the contourlet and curvelet techniques with dual tree complex and real and double density wavelet transform denoising methods were applied to real ultrasound images and results were quantitatively compared. The results show that curvelet-based method performs superior as compared to other methods and can effectively reduce most of the speckle noise content of a given image. PMID:22255196
Medical image compression algorithm based on wavelet transform
NASA Astrophysics Data System (ADS)
Chen, Minghong; Zhang, Guoping; Wan, Wei; Liu, Minmin
2005-02-01
With rapid development of electronic imaging and multimedia technology, the telemedicine is applied to modern medical servings in the hospital. Digital medical image is characterized by high resolution, high precision and vast data. The optimized compression algorithm can alleviate restriction in the transmission speed and data storage. This paper describes the characteristics of human vision system based on the physiology structure, and analyses the characteristics of medical image in the telemedicine, then it brings forward an optimized compression algorithm based on wavelet zerotree. After the image is smoothed, it is decomposed with the haar filters. Then the wavelet coefficients are quantified adaptively. Therefore, we can maximize efficiency of compression and achieve better subjective visual image. This algorithm can be applied to image transmission in the telemedicine. In the end, we examined the feasibility of this algorithm with an image transmission experiment in the network.
Embedded wavelet-based face recognition under variable position
NASA Astrophysics Data System (ADS)
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).
Digital watermarking algorithm based on HVS in wavelet domain
NASA Astrophysics Data System (ADS)
Zhang, Qiuhong; Xia, Ping; Liu, Xiaomei
2013-10-01
As a new technique used to protect the copyright of digital productions, the digital watermark technique has drawn extensive attention. A digital watermarking algorithm based on discrete wavelet transform (DWT) was presented according to human visual properties in the paper. Then some attack analyses were given. Experimental results show that the watermarking scheme proposed in this paper is invisible and robust to cropping, and also has good robustness to cut , compression , filtering , and noise adding .
Wavelet-based asphalt concrete texture grading and classification
NASA Astrophysics Data System (ADS)
Almuntashri, Ali; Agaian, Sos
2011-03-01
In this Paper, we introduce a new method for evaluation, quality control, and automatic grading of texture images representing different textural classes of Asphalt Concrete (AC). Also, we present a new asphalt concrete texture grading, wavelet transform, fractal, and Support Vector Machine (SVM) based automatic classification and recognition system. Experimental results were simulated using different cross-validation techniques and achieved an average classification accuracy of 91.4.0 % in a set of 150 images belonging to five different texture grades.
Fast wavelet based algorithms for linear evolution equations
NASA Technical Reports Server (NTRS)
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.
New image watermarking algorithm based on mixed scales wavelets
NASA Astrophysics Data System (ADS)
El Hajji, Mohamed; Douzi, Hassan; Mammass, Driss; Harba, Rachid; Ros, Frédéric
2012-01-01
Watermarking is a technology for embedding secure information in digital content such as audio, images, and video. An effective watermarking algorithm is proposed based on a discrete wavelet transform (DWT) using mixed scales representation. The watermark is embedded in dominant blocks using quantization index modulation (QIM). These dominant blocks correspond to the texture and contour zones. Experimental results demonstrate that the proposed method is robust against various attacks and improves watermark invisibility.
Wavelet-based face verification for constrained platforms
NASA Astrophysics Data System (ADS)
Sellahewa, Harin; Jassim, Sabah A.
2005-03-01
Human Identification based on facial images is one of the most challenging tasks in comparison to identification based on other biometric features such as fingerprints, palm prints or iris. Facial recognition is the most natural and suitable method of identification for security related applications. This paper is concerned with wavelet-based schemes for efficient face verification suitable for implementation on devices that are constrained in memory size and computational power such as PDA"s and smartcards. Beside minimal storage requirements we should apply as few as possible pre-processing procedures that are often needed to deal with variation in recoding conditions. We propose the LL-coefficients wavelet-transformed face images as the feature vectors for face verification, and compare its performance of PCA applied in the LL-subband at levels 3,4 and 5. We shall also compare the performance of various versions of our scheme, with those of well-established PCA face verification schemes on the BANCA database as well as the ORL database. In many cases, the wavelet-only feature vector scheme has the best performance while maintaining efficacy and requiring minimal pre-processing steps. The significance of these results is their efficiency and suitability for platforms of constrained computational power and storage capacity (e.g. smartcards). Moreover, working at or beyond level 3 LL-subband results in robustness against high rate compression and noise interference.
Multi-resolution and wavelet representations for identifying signatures of disease.
Sajda, Paul; Laine, Andrew; Zeevi, Yehoshua
2002-01-01
Identifying physiological and anatomical signatures of disease in signals and images is one of the fundamental challenges in biomedical engineering. The challenge is most apparent given that such signatures must be identified in spite of tremendous inter and intra-subject variability and noise. Crucial for uncovering these signatures has been the development of methods that exploit general statistical properties of natural signals. The signal processing and applied mathematics communities have developed, in recent years, signal representations which take advantage of Gabor-type and wavelet-type functions that localize signal energy in a joint time-frequency and/or space-frequency domain. These techniques can be expressed as multi-resolution transformations, of which perhaps the best known is the wavelet transform. In this paper we review wavelets, and other related multi-resolution transforms, within the context of identifying signatures for disease. These transforms construct a general representation of signals which can be used in detection, diagnosis and treatment monitoring. We present several examples where these transforms are applied to biomedical signal and imaging processing. These include computer-aided diagnosis in mammography, real-time mosaicking of ophthalmic slit-lamp imagery, characterization of heart disease via ultrasound, predicting epileptic seizures and signature analysis of the electroencephalogram, and reconstruction of positron emission tomography data. PMID:14646044
Multi-resolutional brain network filtering and analysis via wavelets on non-Euclidean space.
Kim, Won Hwa; Adluru, Nagesh; Chung, Moo K; Charchut, Sylvia; GadElkarim, Johnson J; Altshuler, Lori; Moody, Teena; Kumar, Anand; Singh, Vikas; Leow, Alex D
2013-01-01
Advances in resting state fMRI and diffusion weighted imaging (DWI) have led to much interest in studies that evaluate hypotheses focused on how brain connectivity networks show variations across clinically disparate groups. However, various sources of error (e.g., tractography errors, magnetic field distortion, and motion artifacts) leak into the data, and make downstream statistical analysis problematic. In small sample size studies, such noise have an unfortunate effect that the differential signal may not be identifiable and so the null hypothesis cannot be rejected. Traditionally, smoothing is often used to filter out noise. But the construction of convolving with a Gaussian kernel is not well understood on arbitrarily connected graphs. Furthermore, there are no direct analogues of scale-space theory for graphs--ones which allow to view the signal at multiple resolutions. We provide rigorous frameworks for performing 'multi-resolutional' analysis on brain connectivity graphs. These are based on the recent theory of non-Euclidean wavelets. We provide strong evidence, on brain connectivity data from a network analysis study (structural connectivity differences in adult euthymic bipolar subjects), that the proposed algorithm allows identifying statistically significant network variations, which are clinically meaningful, where classical statistical tests, if applied directly, fail. PMID:24505816
Health monitoring of cooling fan bearings based on wavelet filter
NASA Astrophysics Data System (ADS)
He, Wei; Miao, Qiang; Azarian, Michael; Pecht, Michael
2015-12-01
In this paper, a vibration-based health monitoring approach for cooling fans is proposed using a wavelet filter for early detection of faults in fan bearings and for the assessment of fault severity. To match the wavelet filter to the fault characteristic signal, a fuzzy rule is introduced to maximize the amplitudes of bearing characteristic frequencies (BCFs), which are an indicator of bearing faults. The sum of the amplitudes of BCFs and their harmonics (SABCF) is used as an index to capture the bearing degradation trend. A comparative study is conducted with commonly used time-domain indices in the degradation assessment, and performance is quantified by three measures, i.e., monotonicity, prognosability, and trendability. The analysis results of the experimental data show that the proposed method can effectively detect incipient defects and can better capture the degradation trend of fan bearings than traditional time-domain indices in vibration analysis.
A wavelet-based feature vector model for DNA clustering.
Bao, J P; Yuan, R Y
2015-01-01
DNA data are important in the bioinformatic domain. To extract useful information from the enormous collection of DNA sequences, DNA clustering is often adopted to efficiently deal with DNA data. The alignment-free method is a very popular way of creating feature vectors from DNA sequences, which are then used to compare DNA similarities. This paper proposes a wavelet-based feature vector (WFV) model, which is also an alignment-free method. From the perspective of signal processing, a DNA sequence is a sequence of digital signals. However, most traditional alignment-free models only extract features in the time domain. The WFV model uses discrete wavelet transform to adaptively yield feature vectors with a fixed dimension based on the features in both the time and frequency domains. The level of wavelet transform is adjusted according to the length of the DNA sequence rather than a fixed manually set value. The WFV model prefers a 32-dimension feature vector, which greatly promotes system performance. We compared the WFV model with the other five alignment-free models, i.e., k-tuple, DMK, TSM, AMI, and CV, on several large-scale DNA datasets on the DNA clustering application by means of the K-means algorithm. The experimental results showed that the WFV model outperformed the other models in terms of both the clustering results and the running time. PMID:26782569
NASA Astrophysics Data System (ADS)
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.
FPGA Based Wavelet Trigger in Radio Detection of Cosmic Rays
NASA Astrophysics Data System (ADS)
Szadkowski, Zbigniew; Szadkowska, Anna
2014-12-01
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 (˜ V2/ 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.
FPGA Based Wavelet Trigger in Radio Detection of Cosmic Rays
NASA Astrophysics Data System (ADS)
Szadkowski, Zbigniew; Szadkowska, Anna
2014-09-01
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 (˜V2/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.
Wavelet-based neural network analysis of internal carotid arterial Doppler signals.
Ubeyli, Elif Derya; Güler, Inan
2006-06-01
In this study, internal carotid arterial Doppler signals recorded from 130 subjects, where 45 of them suffered from internal carotid artery stenosis, 44 of them suffered from internal carotid artery occlusion and the rest of them were healthy subjects, were classified using wavelet-based neural network. Wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of the internal carotid arterial Doppler signals. Multi-layer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis and occlusion in internal carotid arteries. In order to determine the MLPNN inputs, spectral analysis of the internal carotid arterial Doppler signals was performed using wavelet transform (WT). The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All these data sets were obtained from internal carotid arteries of healthy subjects, subjects suffering from internal carotid artery stenosis and occlusion. The correct classification rate was 96% for healthy subjects, 96.15% for subjects having internal carotid artery stenosis and 96.30% for subjects having internal carotid artery occlusion. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect internal carotid artery stenosis and occlusion. PMID:16848135
NASA Astrophysics Data System (ADS)
Chen, Guoxiong; Cheng, Qiuming
2016-02-01
Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.
Wavelet-SVM classifier based on texture features for land cover classification
NASA Astrophysics Data System (ADS)
Zhang, Ning; Wu, Bingfang; Zhu, Jianjun; Zhou, Yuemin; Zhu, Liang
2008-12-01
Texture features are recognized to be a special hint in images, which represent the spatial relations of the gray pixels. Nowadays, the applications of the texture analysis in image classification spread abroad. Combined with wavelet multi-resolution analysis or support vector machine statistical learning theory, texture analysis could improve the quality of classification increasingly. In this paper, we focus on the land cover for the Three Gorges reservoir using remote sensing data SPOT-5, a new classification method, wavelet-SVM classifier based on texture features, is employed for this study. Compare to the traditional maximum likelihood classifier and SVM classifier only use spectrum feature, this method produces more accurate classification results. According to the real environment of the Three Gorges reservoir land cover, a best texture group is selected from several texture features. Decompose the image at different levels, which is one of the main advantage of wavelet, and then compute the texture features in every sub-image, and the next step is eliminating the redundant, every texture features are centralized on the first principal components using principal component analysis. Finally, with the first principal components inputted, we can get the classification result using SVM in every decomposition scale, but what the problem we couldn't overlook is how to select the best SVM parameters. So an iterative rule based on the classification accuracy is induced, the more accuracy, the proper parameters.
Improving 3D Wavelet-Based Compression of Hyperspectral Images
NASA Technical Reports Server (NTRS)
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
Analysis of Satellite Drag Coefficient Based on Wavelet Transform
NASA Astrophysics Data System (ADS)
Liu, Wei; Wang, Ronglan; Liu, Siqing
Abstract: Drag coefficient sequence was obtained by solving Tiangong1 continuous 55days GPS orbit data with different arc length. The same period solar flux f10.7 and geomagnetic index Ap ap series were high and low frequency multi-wavelet decomposition. Statistical analysis results of the layers sliding correlation between space environmental parameters and decomposition of Cd, showed that the satellite drag coefficient sequence after wavelet decomposition and the corresponding level of f10.7 Ap sequence with good lag correlation. It also verified that the Cd prediction is feasible. Prediction residuals of Cd with different regression models and different sample length were analysed. The results showed that the case was best when setting sample length 20 days and f10.7 regression model were used. It also showed that NRLMSIS-00 model's response in the region of 350km (Tiangong's altitude) and low-middle latitude (Tiangong's inclination) is excessive in ascent stage of geomagnetic activity Ap and is inadequate during fall off segment. Additionally, the low-frequency decomposition components NRLMSIS-00 model's response is appropriate in f10.7 rising segment. High frequency decomposition section, Showed NRLMSIS-00 model's response is small-scale inadequate during f10.7 ascent segment and is reverse in decline of f10.7. Finally, the potential use of a summary and outlook were listed; This method has an important reference value to improve the spacecraft orbit prediction accuracy. Key words: wavelet transform; drag coefficient; lag correlation; Tiangong1;space environment
An Evolved Wavelet Library Based on Genetic Algorithm
Vaithiyanathan, D.; Seshasayanan, R.; Kunaraj, K.; Keerthiga, J.
2014-01-01
As the size of the images being captured increases, there is a need for a robust algorithm for image compression which satiates the bandwidth limitation of the transmitted channels and preserves the image resolution without considerable loss in the image quality. Many conventional image compression algorithms use wavelet transform which can significantly reduce the number of bits needed to represent a pixel and the process of quantization and thresholding further increases the compression. In this paper the authors evolve two sets of wavelet filter coefficients using genetic algorithm (GA), one for the whole image portion except the edge areas and the other for the portions near the edges in the image (i.e., global and local filters). Images are initially separated into several groups based on their frequency content, edges, and textures and the wavelet filter coefficients are evolved separately for each group. As there is a possibility of the GA settling in local maximum, we introduce a new shuffling operator to prevent the GA from this effect. The GA used to evolve filter coefficients primarily focuses on maximizing the peak signal to noise ratio (PSNR). The evolved filter coefficients by the proposed method outperform the existing methods by a 0.31 dB improvement in the average PSNR and a 0.39 dB improvement in the maximum PSNR. PMID:25405225
Wavelet-based localization of oscillatory sources from magnetoencephalography data.
Lina, J M; Chowdhury, R; Lemay, E; Kobayashi, E; Grova, C
2014-08-01
Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy. PMID:22410322
Wavelet-based multiscale adjoint waveform-difference tomography using body and surface waves
NASA Astrophysics Data System (ADS)
Yuan, Y. O.; Simons, F. J.; Bozdag, E.
2014-12-01
We present a multi-scale scheme for full elastic waveform-difference inversion. Using a wavelet transform proves to be a key factor to mitigate cycle-skipping effects. We start with coarse representations of the seismogram to correct a large-scale background model, and subsequently explain the residuals in the fine scales of the seismogram to map the heterogeneities with great complexity. We have previously applied the multi-scale approach successfully to body waves generated in a standard model from the exploration industry: a modified two-dimensional elastic Marmousi model. With this model we explored the optimal choice of wavelet family, number of vanishing moments and decomposition depth. For this presentation we explore the sensitivity of surface waves in waveform-difference tomography. The incorporation of surface waves is rife with cycle-skipping problems compared to the inversions considering body waves only. We implemented an envelope-based objective function probed via a multi-scale wavelet analysis to measure the distance between predicted and target surface-wave waveforms in a synthetic model of heterogeneous near-surface structure. Our proposed method successfully purges the local minima present in the waveform-difference misfit surface. An elastic shallow model with 100~m in depth is used to test the surface-wave inversion scheme. We also analyzed the sensitivities of surface waves and body waves in full waveform inversions, as well as the effects of incorrect density information on elastic parameter inversions. Based on those numerical experiments, we ultimately formalized a flexible scheme to consider both body and surface waves in adjoint tomography. While our early examples are constructed from exploration-style settings, our procedure will be very valuable for the study of global network data.
[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].
Zhao, An; Wu, Baoming
2006-12-01
This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained. PMID:17228703
NASA Astrophysics Data System (ADS)
Li, Changbo; Wang, Liangshu; Sun, Bin; Feng, Runhai; Wu, Yongjing
2015-09-01
In this paper, we introduce the method of Wavelet Multi-scale Decomposition (WMD) combined with Power Spectrum Analysis (PSA) for the separation of regional gravity and magnetic anomalies. The Songliao Basin is situated between the Siberian Plate and the North China Plate, and its main structural trend of gravity and magnetic anomaly fields is NNE. The study area shows a significant feature of deep collage-type construction. According to the feature of gravity field, the region was divided into five sub-regions. The gravity and magnetic fields of the Songliao Basin were separated using WMD with a 4th order separation. The apparent depth of anomalies in each order was determined by Logarithmic PSA. Then, the shallow high-frequency anomalies were removed and the 2nd-4th order wavelet detail anomalies were used to study the basin's major faults. Twenty-six faults within the basement were recognized. The 4th order wavelet approximate anomalies were used for the inversion of the Moho discontinuity and the Curie isothermal surface.
Wavelet-based semi-automatic live-wire segmentation
NASA Astrophysics Data System (ADS)
Haenselmann, Thomas; Effelsberg, Wolfgang
2003-06-01
The live-wire approach is a well-known algorithm based on a graph search to locate boundaries for image segmentation. We will extend the original cost function, which is solely based on finding strong edges, so that the approach can take a large variety of boundaries into account. The cost function adapts to the local characteristics of a boundary by analyzing a user-defined sample using a continuous wavelet decomposition. We will finally extend the approach into 3D in order to segment objects in volumetric data, e. g., from medical CT and MR scans.
Research of the wavelet based ECW remote sensing image compression technology
NASA Astrophysics Data System (ADS)
Zhang, Lan; Gu, Xingfa; Yu, Tao; Dong, Yang; Hu, Xinli; Xu, Hua
2007-11-01
This paper mainly study the wavelet based ECW remote sensing image compression technology. Comparing with the tradition compression technology JPEG and new compression technology JPEG2000 witch based on wavelet we can find that when compress quite large remote sensing image the ER Mapper Compressed Wavelet (ECW) can has significant advantages. The way how to use the ECW SDK was also discussed and prove that it's also the best and faster way to compress China-Brazil Earth Resource Satellite (CBERS) image.
Remotely sensed image compression based on wavelet transform
NASA Technical Reports Server (NTRS)
Kim, Seong W.; Lee, Heung K.; Kim, Kyung S.; Choi, Soon D.
1995-01-01
In this paper, we present an image compression algorithm that is capable of significantly reducing the vast amount of information contained in multispectral images. The developed algorithm exploits the spectral and spatial correlations found in multispectral images. The scheme encodes the difference between images after contrast/brightness equalization to remove the spectral redundancy, and utilizes a two-dimensional wavelet transform to remove the spatial redundancy. the transformed images are then encoded by Hilbert-curve scanning and run-length-encoding, followed by Huffman coding. We also present the performance of the proposed algorithm with the LANDSAT MultiSpectral Scanner data. The loss of information is evaluated by PSNR (peak signal to noise ratio) and classification capability.
MRA-based wavelet frames and applications: image segmentation and surface reconstruction
NASA Astrophysics Data System (ADS)
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.
Landin, Cristina Juarez; Reyes, Magally Martinez; Martin, Anabelem Soberanes; Rosas, Rosa Maria Valdovinos; Ramirez, Jose Luis Sanchez; Ponomaryov, Volodymyr; Soto, Maria Dolores Torres
2011-01-01
The analysis of different Wavelets including novel Wavelet families based on atomic functions are presented, especially for ultrasound (US) and mammography (MG) images compression. This way we are able to determine with what type of filters Wavelet works better in compression of such images. Key properties: Frequency response, approximation order, projection cosine, and Riesz bounds were determined and compared for the classic Wavelets W9/7 used in standard JPEG2000, Daubechies8, Symlet8, as well as for the complex Kravchenko-Rvachev Wavelets ψ(t) based on the atomic functions up(t), fup (2)(t), and eup(t). The comparison results show significantly better performance of novel Wavelets that is justified by experiments and in study of key properties. PMID:21431590
Pipek, János; Nagy, Szilvia
2013-03-01
The wave function of a many electron system contains inhomogeneously distributed spatial details, which allows to reduce the number of fine detail wavelets in multiresolution analysis approximations. Finding a method for decimating the unnecessary basis functions plays an essential role in avoiding an exponential increase of computational demand in wavelet-based calculations. We describe an effective prediction algorithm for the next resolution level wavelet coefficients, based on the approximate wave function expanded up to a given level. The prediction results in a reasonable approximation of the wave function and allows to sort out the unnecessary wavelets with a great reliability. PMID:23115109
Wavelet-based detection of transients in biological signals
NASA Astrophysics Data System (ADS)
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.
Dynamic contrast-based quantization for lossy wavelet image compression.
Chandler, Damon M; Hemami, Sheila S
2005-04-01
This paper presents a contrast-based quantization strategy for use in lossy wavelet image compression that attempts to preserve visual quality at any bit rate. Based on the results of recent psychophysical experiments using near-threshold and suprathreshold wavelet subband quantization distortions presented against natural-image backgrounds, subbands are quantized such that the distortions in the reconstructed image exhibit root-mean-squared contrasts selected based on image, subband, and display characteristics and on a measure of total visual distortion so as to preserve the visual system's ability to integrate edge structure across scale space. Within a single, unified framework, the proposed contrast-based strategy yields images which are competitive in visual quality with results from current visually lossless approaches at high bit rates and which demonstrate improved visual quality over current visually lossy approaches at low bit rates. This strategy operates in the context of both nonembedded and embedded quantization, the latter of which yields a highly scalable codestream which attempts to maintain visual quality at all bit rates; a specific application of the proposed algorithm to JPEG-2000 is presented. PMID:15825476
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
Implemented Wavelet Packet Tree based Denoising Algorithm in Bus Signals of a Wearable Sensorarray
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
A wavelet-based computational method for solving stochastic Itô–Volterra integral equations
Mohammadi, Fakhrodin
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.
GPU-based cone-beam reconstruction using wavelet denoising
NASA Astrophysics Data System (ADS)
Jin, Kyungchan; Park, Jungbyung; Park, Jongchul
2012-03-01
The scattering noise artifact resulted in low-dose projection in repetitive cone-beam CT (CBCT) scans decreases the image quality and lessens the accuracy of the diagnosis. To improve the image quality of low-dose CT imaging, the statistical filtering is more effective in noise reduction. However, image filtering and enhancement during the entire reconstruction process exactly may be challenging due to high performance computing. The general reconstruction algorithm for CBCT data is the filtered back-projection, which for a volume of 512×512×512 takes up to a few minutes on a standard system. To speed up reconstruction, massively parallel architecture of current graphical processing unit (GPU) is a platform suitable for acceleration of mathematical calculation. In this paper, we focus on accelerating wavelet denoising and Feldkamp-Davis-Kress (FDK) back-projection using parallel processing on GPU, utilize compute unified device architecture (CUDA) platform and implement CBCT reconstruction based on CUDA technique. Finally, we evaluate our implementation on clinical tooth data sets. Resulting implementation of wavelet denoising is able to process a 1024×1024 image within 2 ms, except data loading process, and our GPU-based CBCT implementation reconstructs a 512×512×512 volume from 400 projection data in less than 1 minute.
Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform
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
Perceptual security of encrypted images based on wavelet scaling analysis
NASA Astrophysics Data System (ADS)
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.
Background Subtraction Based on Three-Dimensional Discrete Wavelet Transform.
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
Weak transient fault feature extraction based on an optimized Morlet wavelet and kurtosis
NASA Astrophysics Data System (ADS)
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.
Electrocardiogram signal denoising based on a new improved wavelet thresholding.
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. PMID:27587134
Electrocardiogram signal denoising based on a new improved wavelet thresholding
NASA Astrophysics Data System (ADS)
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.
An image adaptive, wavelet-based watermarking of digital images
NASA Astrophysics Data System (ADS)
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.
Complex wavelet based speckle reduction using multiple ultrasound images
NASA Astrophysics Data System (ADS)
Uddin, Muhammad Shahin; Tahtali, Murat; Pickering, Mark R.
2014-04-01
Ultrasound imaging is a dominant tool for diagnosis and evaluation in medical imaging systems. However, as its major limitation is that the images it produces suffer from low quality due to the presence of speckle noise, to provide better clinical diagnoses, reducing this noise is essential. The key purpose of a speckle reduction algorithm is to obtain a speckle-free high-quality image whilst preserving important anatomical features, such as sharp edges. As this can be better achieved using multiple ultrasound images rather than a single image, we introduce a complex wavelet-based algorithm for the speckle reduction and sharp edge preservation of two-dimensional (2D) ultrasound images using multiple ultrasound images. The proposed algorithm does not rely on straightforward averaging of multiple images but, rather, in each scale, overlapped wavelet detail coefficients are weighted using dynamic threshold values and then reconstructed by averaging. Validation of the proposed algorithm is carried out using simulated and real images with synthetic speckle noise and phantom data consisting of multiple ultrasound images, with the experimental results demonstrating that speckle noise is significantly reduced whilst sharp edges without discernible distortions are preserved. The proposed approach performs better both qualitatively and quantitatively than previous existing approaches.
Spatially adaptive bases in wavelet-based coding of semi-regular meshes
NASA Astrophysics Data System (ADS)
Denis, Leon; Florea, Ruxandra; Munteanu, Adrian; Schelkens, Peter
2010-05-01
In this paper we present a wavelet-based coding approach for semi-regular meshes, which spatially adapts the employed wavelet basis in the wavelet transformation of the mesh. The spatially-adaptive nature of the transform requires additional information to be stored in the bit-stream in order to allow the reconstruction of the transformed mesh at the decoder side. In order to limit this overhead, the mesh is first segmented into regions of approximately equal size. For each spatial region, a predictor is selected in a rate-distortion optimal manner by using a Lagrangian rate-distortion optimization technique. When compared against the classical wavelet transform employing the butterfly subdivision filter, experiments reveal that the proposed spatially-adaptive wavelet transform significantly decreases the energy of the wavelet coefficients for all subbands. Preliminary results show also that employing the proposed transform for the lowest-resolution subband systematically yields improved compression performance at low-to-medium bit-rates. For the Venus and Rabbit test models the compression improvements add up to 1.47 dB and 0.95 dB, respectively.
Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation
NASA Astrophysics Data System (ADS)
Lei, Sheau-Fang; Tung, Ying-Kai
Spectral subtraction is commonly used for speech enhancement in a single channel system because of the simplicity of its implementation. However, this algorithm introduces perceptually musical noise while suppressing the background noise. We propose a wavelet-based approach in this paper for suppressing the background noise for speech enhancement in a single channel system. The wavelet packet transform, which emulates the human auditory system, is used to decompose the noisy signal into critical bands. Wavelet thresholding is then temporally adjusted with the noise power by time-adapted noise estimation. The proposed algorithm can efficiently suppress the noise while reducing speech distortion. Experimental results, including several objective measurements, show that the proposed wavelet-based algorithm outperforms spectral subtraction and other wavelet-based denoising approaches for speech enhancement for nonstationary noise environments.
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
Image-based scene representation using wavelet-based interval morphing
NASA Astrophysics Data System (ADS)
Bao, Paul; Xu, Dan
1999-07-01
Scene appearance for a continuous range of viewpoint can be represented by a discrete set of images via image morphing. In this paper, we present a new robust image morphing scheme based on 2D wavelet transform and interval field interpolation. Traditional mesh-base and field-based morphing algorithms, usually designed in the spatial image space, suffer from very high time complexity and therefore make themselves impractical in real-time virtual environment applications. Compared with traditional morphing methods, the proposed wavelet-based interval morphing scheme performs interval interpolation in both the frequency and spatial spaces. First, the images of the scene can be significantly compressed in the frequency domain with little degradation in visual quality and therefore the complexity of the scene can be significantly reduced. Second, since a feature point in the image may correspond to a neighborhood in a subband image in the wavelet domain, we define feature interval for the wavelet-transformed images for an accurate feature matching between the morphing images. Based on the feature intervals, we employ the interval field interpolation to morph the images progressively in a coarse-to-fine process. Finally, we use a post-warping procedure to transform the interpolated views to its desired position. A nice future of using wavelet transformation is its multiresolution representation mode, which enables the progressive morphing of scene.
Optimal sensor placement for time-domain identification using a wavelet-based genetic algorithm
NASA Astrophysics Data System (ADS)
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.
Domijan, A. Jr.; Shaiq, M.
1998-12-31
The characteristics of power system transients seen today present a problem for the classical Fourier analysis technique because it fails to effectively and accurately localize and quantify power system effects. This paper looks into the application of the technique of multi-resolution analysis, based on the wavelet transforms, on voltage waveforms obtained from a pulse width modulation (PWM) induction motor drive during work on ASHRAE Research Project 770. The technique is introduced and applied to mathematically generated waveforms simulating the constant and transient speed operations of a PWM induction motor. It is then applied to waveforms obtained during constant speed operation of an actual induction motor. A new power quality criterion based on the wavelet transform coefficients also is presented and compared to total harmonic distortion (THD). It is seen that since THD is based only on the root mean squared (RMS) value of the harmonics present in the waveforms, it fails to quantify the effects of phases of these harmonics. In addition, all harmonics are weighted equally when calculating the THD, which leads to loss of information regarding the individual harmonic number and magnitude. The new method presents a number that takes into account all the above mentioned effects.
A wavelet-based method to exploit epigenomic language in the regulatory region
Nguyen, Nha; Vo, An; Won, Kyoung-Jae
2014-01-01
Motivation: Epigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed. Results: To study epigenomic condition for gene regulation, we developed a method, AWNFR, to classify epigenomic landscapes based on the detected epigenomic landscapes. Assuming mixture of Gaussians for a nucleosome, the proposed method captures the shape of histone modification and identifies potential regulatory regions in the wavelet domain. For accuracy estimation as well as enhanced computational speed, we developed a novel algorithm based on down-sampling operation and footprint in wavelet. We showed the algorithmic advantages of AWNFR using the simulated data. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes. We studied co-occurring histone marks and showed that AWNFR captures the epigenomic variation across time. Availability and implementation: The source code and supplemental document of AWNFR are available at http://wonk.med.upenn.edu/AWNFR. Contact: wonk@mail.med.upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24096080
NASA Astrophysics Data System (ADS)
Yaşar, Hüseyin; Ceylan, Murat
2015-03-01
Breast cancer is one of the types of cancer which is most commonly seen in women. Density of breast is an important indicator for the risk of cancer. In addition, densities of tissue may harden the diagnosis by hiding the abnormalities occurring on the breast. For this reason, during the process of diagnosis, the process of automatic classification of breast density has a significant importance. In this study, a new system with the base of Artificial Neural Network (ANN) and multiple resolution analysis is suggested. Wavelet and curvelet analyses having the most common use have been used as multi resolution analysis. 4 pieces of statistics which are minimum value, maximum value, mean value and standard deviation have been extracted from the images which have been eluted to their sub-bands via multi resolution analysis. For the purpose of testing the success of the system, 322 pieces of images which are in MIAS database have been used. The obtained results for different backgrounds are so satisfying; and the highest classification values have been obtained as 97.16 % with Wavelet transform and ANN for fatty background and 79.80 % with Wavelet transform and ANN for fatty-glanduar background. The same results have been obtained using Wavelet transform and ANN and Curvelet transform and ANN for dense background and accuracy rate of 84.82 % have been reached. The results of mean classification have been obtained, for three pieces of tissue types (fatty, fatty-glanduar, dense), in sequence as 84.47 % with the use of ANN, 85.71 % with the use of curvelet analysis and ANN; and 87.26 % with the use of wavelet analysis and ANN.
[Medical image processing based on wavelet characteristics and edge blur detection].
Zhu, Baihui; Wan, Zhiping
2014-06-01
To solve the problems of noise interference and edge signal weakness for the existing medical image, we used two-dimensional wavelet transform to process medical images. Combined the directivity of the image edges and the correlation of the wavelet coefficients, we proposed a medical image processing algorithm based on wavelet characteristics and edge blur detection. This algorithm improved noise reduction capabilities and the edge effect due to wavelet transformation and edge blur detection. The experimental results showed that directional correlation improved edge based on wavelet transform fuzzy algorithm could effectively reduce the noise signal in the medical image and save the image edge signal. It has the advantage of the high-definition and de-noising ability. PMID:25219221
Resolution enhancement of composite spectra using wavelet-based derivative spectrometry.
Kharintsev, S S; Kamalova, D I; Salakhov, M Kh; Sevastianov, A A
2005-01-01
An approach based on the using of the continuous wavelet transform (CWT) in derivative spectrometry (DS) is considered. Within the framework of the approach we develop a numerical differentiation algorithm with continuous wavelets for improving resolution of composite spectra. The wavelet-based derivative spectrometry (WDS) method results in best contrast in differential curves compared to the conventional derivative spectrometry method. A main advantage is that, as opposed to DS, WDS gives stable estimations of derivative in the wavelet domain without using the regularization. A wavelet shape and the information redundancy are of the greatest importance when the continuous wavelet transform is used. As an appropriate wavelet we offer to utilize the nth derivative of a component with a priori known shape. The energy distribution into scales allows one to determine a unique wavelet projection and in that way to avoid the information redundancy. A comparative study of WDS and DS with the statistical regularization method (SRM) is made; in particular, limits of applicability of these are given. Examples of the application of both DS and WDS for improving resolution of synthetic composite bands and real-world composite ones coming from molecular spectroscopy are given. PMID:15556433
Experimental wavelet based denoising for indoor infrared wireless communications.
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
A Wavelet Based Dissipation Method for ALE Schemes
Cabot, B; Eliason, D.; Jameson, L.
2000-07-01
Wavelet analysis is natural tool to detect the presence of numerical noise, shocks and other features which might drive a calculation to become unstable. Here we suggest ways where wavelets can be used effectively to define a dissipation flag to replace dissipation flags traditionally used in ALE numerical schemes.
Wavelet-based signal analysis for heart failure hospitalization prediction.
Iakovidis, Dimitris K; Douska, Dimitra; Barba, Evaggelia; Koulaouzidis, George
2016-01-01
Heart failure (HF) is commonly a chronic condition associated with frequent hospital admissions. Early knowledge about a possible deterioration of this condition would enable early treatment for the prevention of adverse events and related hospital admissions. In this paper we present a computational method for predictive information extraction from daily physiological signals, which can be obtained by a telemonitoring system with wearable sensors. It is based on wavelet analysis of temporal signal patterns. Experiments with data from patients enrolled in a telemonitoring protocol show that the proposed method is capable of predicting HF hospitalization events one day before they happen, even in the case of low compliance to the protocol. These results indicate a promising perspective towards a monitoring system that would provide improved life quality for HF patients. PMID:27225548
Adaptive inpainting algorithm based on DCT induced wavelet regularization.
Li, Yan-Ran; Shen, Lixin; Suter, Bruce W
2013-02-01
In this paper, we propose an image inpainting optimization model whose objective function is a smoothed l(1) norm of the weighted nondecimated discrete cosine transform (DCT) coefficients of the underlying image. By identifying the objective function of the proposed model as a sum of a differentiable term and a nondifferentiable term, we present a basic algorithm inspired by Beck and Teboulle's recent work on the model. Based on this basic algorithm, we propose an automatic way to determine the weights involved in the model and update them in each iteration. The DCT as an orthogonal transform is used in various applications. We view the rows of a DCT matrix as the filters associated with a multiresolution analysis. Nondecimated wavelet transforms with these filters are explored in order to analyze the images to be inpainted. Our numerical experiments verify that under the proposed framework, the filters from a DCT matrix demonstrate promise for the task of image inpainting. PMID:23060331
An image fusion method based region segmentation and complex wavelets
NASA Astrophysics Data System (ADS)
Zhang, Junju; Yuan, Yihui; Chang, Benkang; Han, Yiyong; Liu, Lei; Qiu, Yafeng
2009-07-01
A fusion algorithm for infrared and visible light images based on region segmentation and the dual-tree complex wavelet transform. Before image segmentation, morphological top-hat filtering is firstly performed on the IR image and visual images respectively and the details of the luminous area are eliminated. Morphological bottom-hat filtering is then performed on the two kinds of images respectively and the details of the dark area are eliminated. Make the top-hat filtered image subtract the bottom-hat filtered image and obtain the enhanced images. Then the threshold method is used to segment the enhanced images. After image segmentation, the DTCWT coefficients from different regions are merged separately. Finally the fused image is obtained by performing inverse DTCWT. The evaluation results show the validity of the presented algorithm.
Wavelet-based detection of abrupt changes in natural frequencies of time-variant systems
NASA Astrophysics Data System (ADS)
Dziedziech, K.; Staszewski, W. J.; Basu, B.; Uhl, T.
2015-12-01
Detection of abrupt changes in natural frequencies from vibration responses of time-variant systems is a challenging task due to the complex nature of physics involved. It is clear that the problem needs to be analysed in the combined time-frequency domain. The paper proposes an application of the input-output wavelet-based Frequency Response Function for this analysis. The major focus and challenge relate to ridge extraction of the above time-frequency characteristics. It is well known that classical ridge extraction procedures lead to ridges that are smooth. However, this property is not desired when abrupt changes in the dynamics are considered. The methods presented in the paper are illustrated using simulated and experimental multi-degree-of-freedom systems. The results are compared with the classical Frequency Response Function and with the output only analysis based on the wavelet auto-power response spectrum. The results show that the proposed method captures correctly the dynamics of the analysed time-variant systems.
NASA Astrophysics Data System (ADS)
Zheng, Yufeng; Essock, Edward A.; Hansen, Bruce C.
2004-05-01
There are numerous applications for image fusion, some of which include medical imaging, remote sensing, nighttime operations and multi-spectral imaging. In general, the discrete wavelet transform (DWT) and various pyramids (such as Laplacian, ratio, contrast, gradient and morphological pyramids) are the most common and effective methods. For quantitative evaluation of the quality of fused imagery, the root mean square error (RMSE) is the most suitable measure of quality if there is a "ground truth" image available; otherwise, the entropy, spatial frequency or image quality index of the input images and the fused images can be calculated and compared. Here, after analyzing the pyramids" performance with the four measures mentioned, an advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is presented. Specifically, at each scale of the wavelet transformed images, a principle vector was derived from two input images and then applied to two of the images" approximation coefficients (i.e., they were fused by using the principal eigenvector). For the detail coefficients (i.e., three quarters of the coefficients), the larger absolute values were chosen and subjected to a neighborhood morphological processing procedure which served to verify the selected pixels by using a "filling" and "cleaning" operation (this operation filled or removed isolated pixels in a 3-by-3 local region). The fusion performance of the advanced DWT (aDWT) method proposed here was compared with six other common methods, and, based on the four quantitative measures, was found to perform the best when tested on the four input image types. Since the different image sources used here varied with respect to intensity, contrast, noise, and intrinsic characteristics, the aDWT is a promising image fusion procedure for inhomogeneous imagery.
The Brera Multi-scale Wavelet HRI Cluster Survey. I. Selection of the sample and number counts
NASA Astrophysics Data System (ADS)
Moretti, A.; Guzzo, L.; Campana, S.; Lazzati, D.; Panzera, M. R.; Tagliaferri, G.; Arena, S.; Braglia, F.; Dell'Antonio, I.; Longhetti, M.
2004-12-01
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 deg2 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 of detecting a few clusters with z˜ 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 of galaxies down to a flux of 3× 10-14 erg s-1 cm-2, which is consistent with previous measurements from PSPC-based samples. Several clusters with redshifts up to z=0.92 have already been confirmed, either by cross-correlation with existing PSPC surveys or from early results of an ongoing follow-up campaign. Overall, these results indicate that the excellent HRI PSF (5 arcsec FWHM on axis) more than compensates for the negative effect of the higher instrumental background on the detection of high-redshift clusters. In addition, it allows us to detect compact clusters that could be lost at lower resolution, thus potentially providing an important new insight into cluster evolution. Partially based on observations taken at ESO and TNG telescopes.
Fuzzy wavelet plus a quantum neural network as a design base for power system stability enhancement.
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
NASA Astrophysics Data System (ADS)
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.
An NMR log echo data de-noising method based on the wavelet packet threshold algorithm
NASA Astrophysics Data System (ADS)
Meng, Xiangning; Xie, Ranhong; Li, Changxi; Hu, Falong; Li, Chaoliu; Zhou, Cancan
2015-12-01
To improve the de-noising effects of low signal-to-noise ratio (SNR) nuclear magnetic resonance (NMR) log echo data, this paper applies the wavelet packet threshold algorithm to the data. The principle of the algorithm is elaborated in detail. By comparing the properties of a series of wavelet packet bases and the relevance between them and the NMR log echo train signal, ‘sym7’ is found to be the optimal wavelet packet basis of the wavelet packet threshold algorithm to de-noise the NMR log echo train signal. A new method is presented to determine the optimal wavelet packet decomposition scale; this is within the scope of its maximum, using the modulus maxima and the Shannon entropy minimum standards to determine the global and local optimal wavelet packet decomposition scales, respectively. The results of applying the method to the simulated and actual NMR log echo data indicate that compared with the wavelet threshold algorithm, the wavelet packet threshold algorithm, which shows higher decomposition accuracy and better de-noising effect, is much more suitable for de-noising low SNR-NMR log echo data.
NASA Technical Reports Server (NTRS)
Matic, Roy M.; Mosley, Judith I.
1994-01-01
Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data.
Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Piersanti, Mirko; Materassi, Massimo; Spogli, Luca; Cicone, Antonio; Alberti, Tommaso
2016-04-01
Highly irregular fluctuations of the power of trans-ionospheric GNSS signals, namely radio power scintillation, are, at least to a large extent, the effect of ionospheric plasma turbulence, a by-product of the non-linear and non-stationary evolution of the plasma fields defining the Earth's upper atmosphere. One could expect the ionospheric turbulence characteristics of inter-scale coupling, local randomness and high time variability to be inherited by the scintillation on radio signals crossing the medium. On this basis, the remote sensing of local features of the turbulent plasma could be expected as feasible by studying radio scintillation. The dependence of the statistical properties of the medium fluctuations on the space- and time-scale is the distinctive character of intermittent turbulent media. In this paper, a multi-scale statistical analysis of some samples of GPS radio scintillation is presented: the idea is that assessing how the statistics of signal fluctuations vary with time scale under different Helio-Geophysical conditions will be of help in understanding the corresponding multi-scale statistics of the turbulent medium causing that scintillation. In particular, two techniques are tested as multi-scale decomposition schemes of the signals: the discrete wavelet analysis and the Empirical Mode Decomposition. The discussion of the results of the one analysis versus the other will be presented, trying to highlight benefits and limits of each scheme, also under suitably different helio-geophysical conditions.
Traffic characterization and modeling of wavelet-based VBR encoded video
Yu Kuo; Jabbari, B.; Zafar, S.
1997-07-01
Wavelet-based video codecs provide a hierarchical structure for the encoded data, which can cater to a wide variety of applications such as multimedia systems. The characteristics of such an encoder and its output, however, have not been well examined. In this paper, the authors investigate the output characteristics of a wavelet-based video codec and develop a composite model to capture the traffic behavior of its output video data. Wavelet decomposition transforms the input video in a hierarchical structure with a number of subimages at different resolutions and scales. the top-level wavelet in this structure contains most of the signal energy. They first describe the characteristics of traffic generated by each subimage and the effect of dropping various subimages at the encoder on the signal-to-noise ratio at the receiver. They then develop an N-state Markov model to describe the traffic behavior of the top wavelet. The behavior of the remaining wavelets are then obtained through estimation, based on the correlations between these subimages at the same level of resolution and those wavelets located at an immediate higher level. In this paper, a three-state Markov model is developed. The resulting traffic behavior described by various statistical properties, such as moments and correlations, etc., is then utilized to validate their model.
Multispectral image compression technology based on dual-tree discrete wavelet transform
NASA Astrophysics Data System (ADS)
Fang, Zhijun; Luo, Guihua; Liu, Zhicheng; Gan, Yun; Lu, Yu
2009-10-01
The paper proposes a combination of DCT and the Dual-Tree Discrete Wavelet Transform (DDWT) to solve the problems in multi-spectral image data storage and transmission. The proposed method not only removes spectral redundancy by1D DCT, but also removes spatial redundancy by 2D Dual-Tree Discrete Wavelet Transform. Therefore, it achieves low distortion under the conditions of high compression and high-quality reconstruction of the multi-spectral image. Tested by DCT, Haar and DDWT, the results show that the proposed method eliminates the blocking effect of wavelet and has strong visual sense and smooth image, which means the superiors with DDWT has more prominent quality of reconstruction and less noise.
Medical image interpolation method based on similarity analysis of discrete wavelet transforms
NASA Astrophysics Data System (ADS)
Peng, Shichun; Liu, Jian
2007-12-01
A new interpolation method based on multi-resolution technique is presented and used for medical image zooming. The aim of this work is to focus on similarity analysis of adjacent sub-bands provided by Discrete Wavelet Transform (DWT) to enhance the accuracy of the interpolation. First, decompose the original image into sub-bands by the DWT; second, consider the similarity between adjacent sub-bands to calculate the high frequency components; third, use the original image as the low frequency component and apply the inverse DWT to obtain the final interpolation result. Experimental results on magnetic resonance (MR) images and positron emission tomography (PET) images illustrate the effectiveness of the proposed method.
Atmospheric turbulence mitigation using complex wavelet-based fusion.
Anantrasirichai, Nantheera; Achim, Alin; Kingsbury, Nick G; Bull, David R
2013-06-01
Restoring a scene distorted by atmospheric turbulence is a challenging problem in video surveillance. The effect, caused by random, spatially varying, perturbations, makes a model-based solution difficult and in most cases, impractical. In this paper, we propose a novel method for mitigating the effects of atmospheric distortion on observed images, particularly airborne turbulence which can severely degrade a region of interest (ROI). In order to extract accurate detail about objects behind the distorting layer, a simple and efficient frame selection method is proposed to select informative ROIs only from good-quality frames. The ROIs in each frame are then registered to further reduce offsets and distortions. We solve the space-varying distortion problem using region-level fusion based on the dual tree complex wavelet transform. Finally, contrast enhancement is applied. We further propose a learning-based metric specifically for image quality assessment in the presence of atmospheric distortion. This is capable of estimating quality in both full- and no-reference scenarios. The proposed method is shown to significantly outperform existing methods, providing enhanced situational awareness in a range of surveillance scenarios. PMID:23475359
Wavelet-based acoustic emission detection method with adaptive thresholding
NASA Astrophysics Data System (ADS)
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.
Wavelet-Variance-Based Estimation for Composite Stochastic Processes.
Guerrier, Stéphane; Skaloud, Jan; Stebler, Yannick; Victoria-Feser, Maria-Pia
2013-09-01
This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is the case in engineering and natural sciences. The proposed estimation method offers an alternative to classical estimation based on the likelihood, that is straightforward to implement and often the only feasible estimation method with complex models. The estimator furnishes results as the optimization of a criterion based on a standardized distance between the sample wavelet variances (WV) estimates and the model-based WV. Indeed, the WV provides a decomposition of the variance process through different scales, so that they contain the information about different features of the stochastic model. We derive the asymptotic properties of the proposed estimator for inference and perform a simulation study to compare our estimator to the MLE and the LSE with different models. We also set sufficient conditions on composite models for our estimator to be consistent, that are easy to verify. We use the new estimator to estimate the stochastic error's parameters of the sum of three first order Gauss-Markov processes by means of a sample of over 800,000 issued from gyroscopes that compose inertial navigation systems. Supplementary materials for this article are available online. PMID:24174689
Wavelet-Variance-Based Estimation for Composite Stochastic Processes
Guerrier, Stéphane; Skaloud, Jan; Stebler, Yannick; Victoria-Feser, Maria-Pia
2013-01-01
This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is the case in engineering and natural sciences. The proposed estimation method offers an alternative to classical estimation based on the likelihood, that is straightforward to implement and often the only feasible estimation method with complex models. The estimator furnishes results as the optimization of a criterion based on a standardized distance between the sample wavelet variances (WV) estimates and the model-based WV. Indeed, the WV provides a decomposition of the variance process through different scales, so that they contain the information about different features of the stochastic model. We derive the asymptotic properties of the proposed estimator for inference and perform a simulation study to compare our estimator to the MLE and the LSE with different models. We also set sufficient conditions on composite models for our estimator to be consistent, that are easy to verify. We use the new estimator to estimate the stochastic error's parameters of the sum of three first order Gauss-Markov processes by means of a sample of over 800,000 issued from gyroscopes that compose inertial navigation systems. Supplementary materials for this article are available online. PMID:24174689
Wavelet-Based DFT calculations on Massively Parallel Hybrid Architectures
NASA Astrophysics Data System (ADS)
Genovese, Luigi
2011-03-01
In this contribution, we present an implementation of a full DFT code that can run on massively parallel hybrid CPU-GPU clusters. Our implementation is based on modern GPU architectures which support double-precision floating-point numbers. This DFT code, named BigDFT, is delivered within the GNU-GPL license either in a stand-alone version or integrated in the ABINIT software package. Hybrid BigDFT routines were initially ported with NVidia's CUDA language, and recently more functionalities have been added with new routines writeen within Kronos' OpenCL standard. The formalism of this code is based on Daubechies wavelets, which is a systematic real-space based basis set. As we will see in the presentation, the properties of this basis set are well suited for an extension on a GPU-accelerated environment. In addition to focusing on the implementation of the operators of the BigDFT code, this presentation also relies of the usage of the GPU resources in a complex code with different kinds of operations. A discussion on the interest of present and expected performances of Hybrid architectures computation in the framework of electronic structure calculations is also adressed.
Wavelet Based Analysis of Airborne Gravity Data For Interpretation of Geological Boundaries
NASA Astrophysics Data System (ADS)
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
ICER-3D: A Progressive Wavelet-Based Compressor for Hyperspectral Images
NASA Technical Reports Server (NTRS)
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.
ENSO forecast using a wavelet-based decomposition
NASA Astrophysics Data System (ADS)
Deliège, Adrien; Nicolay, Samuel; Fettweis, Xavier
2015-04-01
The aim of this work is to introduce a new method for forecasting major El Niño/ La Niña events with the use of a wavelet-based mode decomposition. These major events are related to sea surface temperature anomalies in the tropical Pacific Ocean: anomalous warmings are known as El Niño events, while excessive coolings are referred as La Niña episodes. These climatological phenomena are of primary importance since they are involved in many teleconnections; predicting them long before they occur is therefore a crucial concern. First, we perform a wavelet transform (WT) of the monthly sampled El Niño Southern Oscillation 3.4 index (from 1950 to present) and compute the associated scale spectrum, which can be seen as the energy carried in the WT as a function of the scale. It can be observed that the spectrum reaches five peaks, corresponding to time scales of about 7, 20, 31, 43 and 61 months respectively. Therefore, the Niño 3.4 signal can be decomposed into five dominant oscillating components with time-varying amplitudes, these latter being given by the modulus of the WT at the associated pseudo-periods. The reconstruction of the index based on these five components is accurate since more than 93% of the El Niño/ La Niña events of the last 60 years are recovered and no major event is erroneously predicted. Then, the components are smoothly extrapolated using polynomials and added together, giving so several years forecasts of the Niño 3.4 index. In order to increase the reliability of the forecasts, we perform several months hindcasts (i.e. retroactive probing forecasts) which can be validated with the existing data. It turns out that most of the major events can be accurately predicted up to three years in advance, which makes our methodology competitive for such forecasts. Finally, we discuss the El Niño conditions currently undergone and give indications about the next La Niña event.
Dong, Xiao-Ling; Sun, Xu-Dong
2013-12-01
The feasibility was explored in determination of reducing sugar content of potato granules based on wavelet compression algorithm combined with near-infrared spectroscopy. The spectra of 250 potato granules samples were recorded by Fourier transform near-infrared spectrometer in the range of 4000- 10000 cm-1. The three parameters of vanishing moments, wavelet coefficients and principal component factor were optimized. The optimization results of three parameters were 10, 100 and 20, respectively. The original spectra of 1501 spectral variables were transfered to 100 wavelet coefficients using db wavelet function. The partial least squares (PLS) calibration models were developed by 1501 spectral variables and 100 wavelet coefficients. Sixty two unknown samples of prediction set were applied to evaluate the performance of PLS models. By comparison, the optimal result was obtained by wavelet compression combined with PLS calibration model. The correlation coefficient of prediction and root mean square error of prediction were 0.98 and 0.181%, respectively. Experimental results show that the dimensions of spectral data were reduced, scarcely losing effective information by wavelet compression algorithm combined with near-infrared spectroscopy technology in determination of reducing sugar in potato granules. The PLS model is simplified, and the predictive ability is improved. PMID:24611373
Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization
NASA Astrophysics Data System (ADS)
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.
Classification of Histological Images Based on the Stationary Wavelet Transform
NASA Astrophysics Data System (ADS)
Nascimento, M. Z.; Neves, L.; Duarte, S. C.; Duarte, Y. A. S.; Ramos Batista, V.
2015-01-01
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.
Wavelet-based multiresolution analysis of Wivenhoe Dam water temperatures
NASA Astrophysics Data System (ADS)
Percival, D. B.; Lennox, S. M.; Wang, Y.-G.; Darnell, R. E.
2011-05-01
Water temperature measurements from Wivenhoe Dam offer a unique opportunity for studying fluctuations of temperatures in a subtropical dam as a function of time and depth. Cursory examination of the data indicate a complicated structure across both time and depth. We propose simplifying the task of describing these data by breaking the time series at each depth into physically meaningful components that individually capture daily, subannual, and annual (DSA) variations. Precise definitions for each component are formulated in terms of a wavelet-based multiresolution analysis. The DSA components are approximately pairwise uncorrelated within a given depth and between different depths. They also satisfy an additive property in that their sum is exactly equal to the original time series. Each component is based upon a set of coefficients that decomposes the sample variance of each time series exactly across time and that can be used to study both time-varying variances of water temperature at each depth and time-varying correlations between temperatures at different depths. Each DSA component is amenable for studying a certain aspect of the relationship between the series at different depths. The daily component in general is weakly correlated between depths, including those that are adjacent to one another. The subannual component quantifies seasonal effects and in particular isolates phenomena associated with the thermocline, thus simplifying its study across time. The annual component can be used for a trend analysis. The descriptive analysis provided by the DSA decomposition is a useful precursor to a more formal statistical analysis.
Wavelet-based stereo images reconstruction using depth images
NASA Astrophysics Data System (ADS)
Jovanov, Ljubomir; Pižurica, Aleksandra; Philips, Wilfried
2007-09-01
It is believed by many that three-dimensional (3D) television will be the next logical development toward a more natural and vivid home entertaiment experience. While classical 3D approach requires the transmission of two video streams, one for each view, 3D TV systems based on depth image rendering (DIBR) require a single stream of monoscopic images and a second stream of associated images usually termed depth images or depth maps, that contain per-pixel depth information. Depth map is a two-dimensional function that contains information about distance from camera to a certain point of the object as a function of the image coordinates. By using this depth information and the original image it is possible to reconstruct a virtual image of a nearby viewpoint by projecting the pixels of available image to their locations in 3D space and finding their position in the desired view plane. One of the most significant advantages of the DIBR is that depth maps can be coded more efficiently than two streams corresponding to left and right view of the scene, thereby reducing the bandwidth required for transmission, which makes it possible to reuse existing transmission channels for the transmission of 3D TV. This technique can also be applied for other 3D technologies such as multimedia systems. In this paper we propose an advanced wavelet domain scheme for the reconstruction of stereoscopic images, which solves some of the shortcommings of the existing methods discussed above. We perform the wavelet transform of both the luminance and depth images in order to obtain significant geometric features, which enable more sensible reconstruction of the virtual view. Motion estimation employed in our approach uses Markov random field smoothness prior for regularization of the estimated motion field. The evaluation of the proposed reconstruction method is done on two video sequences which are typically used for comparison of stereo reconstruction algorithms. The results demonstrate
Hierarchical wavelet-based image model for pattern analysis and synthesis
NASA Astrophysics Data System (ADS)
Scott, Clayton D.; Nowak, Robert D.
2000-12-01
Despite their success in other areas of statistical signal processing, current wavelet-based image models are inadequate for modeling patterns in images, due to the presence of unknown transformations inherent in most pattern observations. In this paper we introduce a hierarchical wavelet-based framework for modeling patterns in digital images. This framework takes advantage of the efficient image representations afforded by wavelets, while accounting for unknown pattern transformations. Given a trained model, we can use this framework to synthesize pattern observations. If the model parameters are unknown, we can infer them from labeled training data using TEMPLAR, a novel template learning algorithm with linear complexity. TEMPLAR employs minimum description length complexity regularization to learn a template with a sparse representation in the wavelet domain. We illustrate template learning with examples, and discuss how TEMPLAR applies to pattern classification and denoising from multiple, unaligned observations.
R-peaks detection based on stationary wavelet transform.
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
Wavelets-based clustering of air quality monitoring sites.
Gouveia, Sónia; Scotto, Manuel G; Monteiro, Alexandra; Alonso, Andres M
2015-11-01
This paper aims at providing a variance/covariance profile of a set of 36 monitoring stations measuring ozone (O3) and nitrogen dioxide (NO2) hourly concentrations, collected over the period 2005-2013, in Portugal mainland. The resulting individual profiles are embedded in a wavelet decomposition-based clustering algorithm in order to identify groups of stations exhibiting similar profiles. The results of the cluster analysis identify three groups of stations, namely urban, suburban/urban/rural, and a third group containing all but one rural stations. The results clearly indicate a geographical pattern among urban stations, distinguishing those located in Lisbon area from those located in Oporto/North. Furthermore, for urban stations, intra-diurnal and daily time scales exhibit the highest variance. This is due to the more relevant chemical activity occurring in high NO2 emissions areas which are responsible for high variability on daily profiles. These chemical processes also explain the reason for NO2 and O3 being highly negatively cross-correlated in suburban and urban sites as compared with rural stations. Finally, the clustering analysis also identifies sites which need revision concerning classification according to environment/influence type. PMID:26483085
Wavelet-based laser-induced ultrasonic inspection in pipes
NASA Astrophysics Data System (ADS)
Baltazar-López, Martín E.; Suh, Steve; Chona, Ravinder; Burger, Christian P.
2006-02-01
The feasibility of detecting localized defects in tubing using Wavelet based laser-induced ultrasonic-guided waves as an inspection method is examined. Ultrasonic guided waves initiated and propagating in hollow cylinders (pipes and/or tubes) are studied as an alternative, robust nondestructive in situ inspection method. Contrary to other traditional methods for pipe inspection, in which contact transducers (electromagnetic, piezoelectric) and/or coupling media (submersion liquids) are used, this method is characterized by its non-contact nature. This characteristic is particularly important in applications involving Nondestructive Evaluation (NDE) of materials because the signal being detected corresponds only to the induced wave. Cylindrical guided waves are generated using a Q-switched Nd:YAG laser and a Fiber Tip Interferometry (FTI) system is used to acquire the waves. Guided wave experimental techniques are developed for the measurement of phase velocities to determine elastic properties of the material and the location and geometry of flaws including inclusions, voids, and cracks in hollow cylinders. As compared to the traditional bulk wave methods, the use of guided waves offers several important potential advantages. Some of which includes better inspection efficiency, the applicability to in-situ tube inspection, and fewer evaluation fluctuations with increased reliability.
Usefulness of wavelet-based features as global descriptors of VHR satellite images
NASA Astrophysics Data System (ADS)
Pyka, Krystian; Drzewiecki, Wojciech; Bernat, Katarzyna; Wawrzaszek, Anna; Krupiński, Michal
2014-10-01
In this paper we present the results of research carried out to assess the usefulness of wavelet-based measures of image texture for classification of panchromatic VHR satellite image content. The study is based on images obtained from EROS-A satellite. Wavelet-based features are calculated according to two approaches. In first one the wavelet energy is calculated for each components from every level of decomposition using Haar wavelet. In second one the variance and kurtosis are calculated as mean values of detail components with filters belonging to the D, LA, MB groups of various lengths. The results indicate that both approaches are useful and complement one another. Among the most useful wavelet-based features are present not only those calculated with short or long filters, but also with the filters of intermediate length. Usage of filters of different type and length as well as different statistical parameters (variance, kurtosis) calculated as means for each decomposition level improved the discriminative properties of the feature vector consisted initially of wavelet energies of each component.
Yang, Zijing; Cai, Ligang; Gao, Lixin; Wang, Huaqing
2012-01-01
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. PMID:22666035
Infrared image guidance for ground vehicle based on fast wavelet image focusing and tracking
NASA Astrophysics Data System (ADS)
Akiyama, Akira; Kobayashi, Nobuaki; Mutoh, Eiichiro; Kumagai, Hideo; Yamada, Hirofumi; Ishii, Hiromitsu
2009-08-01
We studied the infrared image guidance for ground vehicle based on the fast wavelet image focusing and tracking. Here we uses the image of the uncooled infrared imager mounted on the two axis gimbal system and the developed new auto focusing algorithm on the Daubechies wavelet transform. The developed new focusing algorithm on the Daubechies wavelet transform processes the result of the high pass filter effect to meet the direct detection of the objects. This new focusing gives us the distance information of the outside world smoothly, and the information of the gimbal system gives us the direction of objects in the outside world to match the sense of the spherical coordinate system. We installed this system on the hand made electric ground vehicle platform powered by 24VDC battery. The electric vehicle equips the rotary encoder units and the inertia rate sensor units to make the correct navigation process. The image tracking also uses the developed newt wavelet focusing within several image processing. The size of the hand made electric ground vehicle platform is about 1m long, 0.75m wide, 1m high, and 50kg weight. We tested the infrared image guidance for ground vehicle based on the new wavelet image focusing and tracking using the electric vehicle indoor and outdoor. The test shows the good results by the developed infrared image guidance for ground vehicle based on the new wavelet image focusing and tracking.
Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction
NASA Astrophysics Data System (ADS)
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)
A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data.
Chiarelli, Antonio M; Maclin, Edward L; Fabiani, Monica; Gratton, Gabriele
2015-05-15
Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real data. However, when the SNR is high, it can lead to a reduction of signal amplitude. This may occur because standardized scores inherently adapt to the noise level, independently of the shape of the distribution of the wavelet coefficients. Higher-order moments of the wavelet coefficient distribution may provide a more diagnostic index of wavelet distribution abnormality than its variance. Here we introduce a new procedure that relies on eliminating wavelets that contribute to generate a large fourth-moment (i.e., kurtosis) of the coefficient distribution to define "outliers" wavelets (kurtosis-based Wavelet Filtering, kbWF). We tested kbWF by comparing it with other existing procedures, using simulated functional hemodynamic responses added to real resting-state fNIRS recordings. These simulations show that kbWF is highly effective in eliminating transient noise, yielding results with higher SNR than other existing methods over a wide range of signal and noise amplitudes. This is because: (1) the procedure is iterative; and (2) kurtosis is more diagnostic than variance in identifying outliers. However, kbWF does not eliminate slow components of artifacts whose duration is comparable to the total recording time. PMID:25747916
Correlation Analysis of Automobile Crash Responses Based on Wavelet Decompositions
NASA Astrophysics Data System (ADS)
Cheng, Z. Q.; Pellettiere, J. A.
2003-11-01
Wavelets are used to analyse automobile crash responses. Crash signals are decomposed into a wavelet or wavelet packet basis, which provide an intuitive vision of impact behaviour of the vehicle structure and occupants. The decomposed signals are further divided into segments that represent vibrations occurring in certain time spans. A correlation analysis is then performed on the decomposed and segmented signals in order to determine the dynamic relationship between different parts of the structure or different segments of the body. The structural responses and the occupant responses in a full frontal impact test are analysed. It is shown that when the gross motions of the structural components are superimposed with significant short time vibrations, the occupant forward motion is basically a rigid body motion. Published by Elsevier Science Ltd.
Image superresolution of cytology images using wavelet based patch search
NASA Astrophysics Data System (ADS)
Vargas, Carlos; García-Arteaga, Juan D.; Romero, Eduardo
2015-01-01
Telecytology is a new research area that holds the potential of significantly reducing the number of deaths due to cervical cancer in developing countries. This work presents a novel super-resolution technique that couples high and low frequency information in order to reduce the bandwidth consumption of cervical image transmission. The proposed approach starts by decomposing into wavelets the high resolution images and transmitting only the lower frequency coefficients. The transmitted coefficients are used to reconstruct an image of the original size. Additional details are added by iteratively replacing patches of the wavelet reconstructed image with equivalent high resolution patches from a previously acquired image database. Finally, the original transmitted low frequency coefficients are used to correct the final image. Results show a higher signal to noise ratio in the proposed method over simply discarding high frequency wavelet coefficients or replacing directly down-sampled patches from the image-database.
Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition
NASA Astrophysics Data System (ADS)
Cabrera, Diego; Sancho, Fernando; Sánchez, René-Vinicio; Zurita, Grover; Cerrada, Mariela; Li, Chuan; Vásquez, Rafael E.
2015-09-01
This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.
Quantisation-based video watermarking in the wavelet domain with spatial and temporal redundancy
NASA Astrophysics Data System (ADS)
Preda, Radu O.; Vizireanu, Nicolae D.
2011-03-01
In this article we introduce a new public digital watermarking technique for video copyright protection working in the discrete wavelet transform domain. The scheme uses binary images as watermarks. These are embedded in the detail wavelet coefficients of the middle wavelet sub-bands. The method is a combination of spread spectrum and quantisation-based watermarking. Every bit of the watermark is spread over a number of wavelet coefficients with the use of a secret key. The resilience of the watermarking algorithm was tested against a series of eight different attacks using different videos. To improve the resilience of the algorithm we use error correction codes and embed the watermark with spatial and temporal redundancy. The proposed method achieves a very good perceptual quality with mean peak signal-to-noise ratio values of the watermarked videos of more than 40 dB and high resistance to a large spectrum of attacks.
Classification of Spectra of Emission-line Stars Using Feature Extraction Based on Wavelet Transform
NASA Astrophysics Data System (ADS)
Bromová P.; Bařina, D.; Škoda, P.; Vážný, J.; Zendulka, J.
2014-05-01
Our goal is to automatically identify spectra of emission (Be) stars in large archives and classify their types based on a typical shape of the Hα emission line. Due to the length of spectra, of the original data is very time-consuming. In order to lower computational requirements and enhance the separability of the classes, we have to find a reduced representation of spectral features, however conserving most of the original information content. As the Be stars show a number of different shapes of emission lines, it is not easy to construct simple criteria (like e.g. Gaussian fits) to distinguish the emission lines in an automatic manner. We proposed to perform the wavelet transform of the spectra, calculate statistical metrics from the wavelet coefficients, and use them as feature vectors for classification. In this paper, we compare different wavelet transforms, different wavelets, and different statistical metrics in an attempt to identify the best method.
NASA Astrophysics Data System (ADS)
Li, Hao; Ma, Yong; Liang, Kun; Tian, Yong; Wang, Rui
2012-01-01
Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.
Based on the wavelet neural network analysis and forecast of deformation monitoring data
NASA Astrophysics Data System (ADS)
Zhou, Conglin; Tang, Shihua; Tang, Changzeng; Huang, Qing; Liu, Yintao; Zhong, Xinying; Li, Feida; Xu, Hongwei
2015-12-01
Combines the wavelet analysis and neural network, this paper will be processed the data and the traditional BP neural network and kalman filter are analyzed and compared. First of all to obtain data of dam deformation wavelet denoising, excluding the contaminated data, obtain the optimal data set. Threshold denoising is generally adopted. Then based on the BP neural network, wavelet analysis to improve the traditional neural network model. Improve the underlying layer upon layer number and the number of nodes. Combined with the optimized dam deformation data, using the improved network model, the results to the regression model, ordinary kalman filter, this paper compares and analyzes the prediction effect evaluation.Comparison result is more ideal, which indicates that the combination of wavelet neural network model for deformation data processing has a good precision.
NASA Astrophysics Data System (ADS)
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.
Estimation of Modal Parameters Using a Wavelet-Based Approach
NASA Technical Reports Server (NTRS)
Lind, Rick; Brenner, Marty; Haley, Sidney M.
1997-01-01
Modal stability parameters are extracted directly from aeroservoelastic flight test data by decomposition of accelerometer response signals into time-frequency atoms. Logarithmic sweeps and sinusoidal pulses are used to generate DAST closed loop excitation data. Novel wavelets constructed to extract modal damping and frequency explicitly from the data are introduced. The so-called Haley and Laplace wavelets are used to track time-varying modal damping and frequency in a matching pursuit algorithm. Estimation of the trend to aeroservoelastic instability is demonstrated successfully from analysis of the DAST data.
Generalized Local-to-Global Shape Feature Detection Based on Graph Wavelets.
Li, Nannan; Wang, Shengfa; Zhong, Ming; Su, Zhixun; Qin, Hong
2016-09-01
Informative and discriminative feature descriptors are vital in qualitative and quantitative shape analysis for a large variety of graphics applications. Conventional feature descriptors primarily concentrate on discontinuity of certain differential attributes at different orders that naturally give rise to their discriminative power in depicting point, line, small patch features, etc. This paper seeks novel strategies to define generalized, user-specified features anywhere on shapes. Our new region-based feature descriptors are constructed primarily with the powerful spectral graph wavelets (SGWs) that are both multi-scale and multi-level in nature, incorporating both local (differential) and global (integral) information. To our best knowledge, this is the first attempt to organize SGWs in a hierarchical way and unite them with the bi-harmonic diffusion field towards quantitative region-based shape analysis. Furthermore, we develop a local-to-global shape feature detection framework to facilitate a host of graphics applications, including partial matching without point-wise correspondence, coarse-to-fine recognition, model recognition, etc. Through the extensive experiments and comprehensive comparisons with the state-of-the-art, our framework has exhibited many attractive advantages such as being geometry-aware, robust, discriminative, isometry-invariant, etc. PMID:26561459
Wavelet-based coherence measures of global seismic noise properties
NASA Astrophysics Data System (ADS)
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.
Wavelet based R-peak detection for heart rate variability studies.
Sunkaria, R K; Saxena, S C; Kumar, V; Singhal, A M
2010-02-01
Detection of QRS complex in electrocardiogram (ECG) signals is of immense importance in cardiac health prognosis. In this paper a new symmetric wavelet for detection of R-peak is presented, which has been designed based on spectral characteristics and morphology of QRS complex. The detection of R-peak was carried out using this designed wavelet, and with existing symmetric wavelets such as db3, db6, haar and bior2.2. The detection accuracy with this wavelet is 99.99%, which is higher than those with existing symmetric wavelets. The algorithm has been tested on standard databases such as Fantasia database of normal and healthy subjects, MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital) arrhythmia database, and on self-recorded electrocardiograms of normal subjects and patients under diseased stress. The study of heart rate variability (HRV) through computation of RR-tachogram using the new wavelet has proved to be effective in reliably evaluating HRV parameters. PMID:20059305
Acoustic emission detection for mass fractions of materials based on wavelet packet technology.
Wang, Xianghong; Xiang, Jianjun; Hu, Hongwei; Xie, Wei; Li, Xiongbing
2015-07-01
Materials are often damaged during the process of detecting mass fractions by traditional methods. Acoustic emission (AE) technology combined with wavelet packet analysis is used to evaluate the mass fractions of microcrystalline graphite/polyvinyl alcohol (PVA) composites in this study. Attenuation characteristics of AE signals across the composites with different mass fractions are investigated. The AE signals are decomposed by wavelet packet technology to obtain the relationships between the energy and amplitude attenuation coefficients of feature wavelet packets and mass fractions as well. Furthermore, the relationship is validated by a sample. The larger proportion of microcrystalline graphite will correspond to the higher attenuation of energy and amplitude. The attenuation characteristics of feature wavelet packets with the frequency range from 125 kHz to 171.85 kHz are more suitable for the detection of mass fractions than those of the original AE signals. The error of the mass fraction of microcrystalline graphite calculated by the feature wavelet packet (1.8%) is lower than that of the original signal (3.9%). Therefore, AE detection base on wavelet packet analysis is an ideal NDT method for evaluate mass fractions of composite materials. PMID:25737229
Diagnostic methodology for incipient system disturbance based on a neural wavelet approach
NASA Astrophysics Data System (ADS)
Won, In-Ho
Since incipient system disturbances are easily mixed up with other events or noise sources, the signal from the system disturbance can be neglected or identified as noise. Thus, as available knowledge and information is obtained incompletely or inexactly from the measurements; an exploration into the use of artificial intelligence (AI) tools to overcome these uncertainties and limitations was done. A methodology integrating the feature extraction efficiency of the wavelet transform with the classification capabilities of neural networks is developed for signal classification in the context of detecting incipient system disturbances. The synergistic effects of wavelets and neural networks present more strength and less weakness than either technique taken alone. A wavelet feature extractor is developed to form concise feature vectors for neural network inputs. The feature vectors are calculated from wavelet coefficients to reduce redundancy and computational expense. During this procedure, the statistical features based on the fractal concept to the wavelet coefficients play a role as crucial key in the wavelet feature extractor. To verify the proposed methodology, two applications are investigated and successfully tested. The first involves pump cavitation detection using dynamic pressure sensor. The second pertains to incipient pump cavitation detection using signals obtained from a current sensor. Also, through comparisons between three proposed feature vectors and with statistical techniques, it is shown that the variance feature extractor provides a better approach in the performed applications.
Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.
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
Compressed sensing based on the improved wavelet transform for image processing
NASA Astrophysics Data System (ADS)
Pang, Peng; Gao, Wei; Song, Zongxi; XI, Jiang-bo
2014-09-01
Compressed sensing theory is a new sampling theory that can sample signal in a below sampling rate than the traditional Nyquist sampling theory. Compressed sensing theory that has given a revolutionary solution is a novel sampling and processing theory under the condition that the signal is sparse or compressible. This paper investigates how to improve the theory of CS and its application in imaging system. According to the properties of wavelet transform sub-bands, an improved compressed sensing algorithm based on the single layer wavelet transform was proposed. Based on the feature that the most information was preserved on the low-pass layer after the wavelet transform, the improved compressed sensing algorithm only measured the low-pass wavelet coefficients of the image but preserving the high-pass wavelet coefficients. The signal can be restricted exactly by using the appropriate reconstruction algorithms. The reconstruction algorithm is the key point that most researchers focus on and significant progress has been made. For the reconstruction, in order to improve the orthogonal matching pursuit (OMP) algorithm, increased the iteration layers make sure low-pass wavelet coefficients could be recovered by measurements exactly. Then the image could be reconstructed by using the inverse wavelet transform. Compared the original compressed sensing algorithm, simulation results demonstrated that the proposed algorithm decreased the processed data, signal processed time decreased obviously and the recovered image quality improved to some extent. The PSNR of the proposed algorithm was improved about 2 to 3 dB. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed.
Salau, J; Haas, J H; Thaller, G; Leisen, M; Junge, W
2016-09-01
Camera-based systems in dairy cattle were intensively studied over the last years. Different from this study, single camera systems with a limited range of applications were presented, mostly using 2D cameras. This study presents current steps in the development of a camera system comprising multiple 3D cameras (six Microsoft Kinect cameras) for monitoring purposes in dairy cows. An early prototype was constructed, and alpha versions of software for recording, synchronizing, sorting and segmenting images and transforming the 3D data in a joint coordinate system have already been implemented. This study introduced the application of two-dimensional wavelet transforms as method for object recognition and surface analyses. The method was explained in detail, and four differently shaped wavelets were tested with respect to their reconstruction error concerning Kinect recorded depth maps from different camera positions. The images' high frequency parts reconstructed from wavelet decompositions using the haar and the biorthogonal 1.5 wavelet were statistically analyzed with regard to the effects of image fore- or background and of cows' or persons' surface. Furthermore, binary classifiers based on the local high frequencies have been implemented to decide whether a pixel belongs to the image foreground and if it was located on a cow or a person. Classifiers distinguishing between image regions showed high (⩾0.8) values of Area Under reciever operation characteristic Curve (AUC). The classifications due to species showed maximal AUC values of 0.69. PMID:26837672
Identification of structural damage using wavelet-based data classification
NASA Astrophysics Data System (ADS)
Koh, Bong-Hwan; Jeong, Min-Joong; Jung, Uk
2008-03-01
Predicted time-history responses from a finite-element (FE) model provide a baseline map where damage locations are clustered and classified by extracted damage-sensitive wavelet coefficients such as vertical energy threshold (VET) positions having large silhouette statistics. Likewise, the measured data from damaged structure are also decomposed and rearranged according to the most dominant positions of wavelet coefficients. Having projected the coefficients to the baseline map, the true localization of damage can be identified by investigating the level of closeness between the measurement and predictions. The statistical confidence of baseline map improves as the number of prediction cases increases. The simulation results of damage detection in a truss structure show that the approach proposed in this study can be successfully applied for locating structural damage even in the presence of a considerable amount of process and measurement noise.
NASA Astrophysics Data System (ADS)
Preda, Radu O.; Vizireanu, Dragos Nicolae
2011-01-01
The development of the information technology and computer networks facilitates easy duplication, manipulation, and distribution of digital data. Digital watermarking is one of the proposed solutions for effectively safeguarding the rightful ownership of digital images and video. We propose a public digital watermarking technique for video copyright protection in the discrete wavelet transform domain. The scheme uses binary images as watermarks. These are embedded in the detail wavelet coefficients of the middle wavelet subbands. The method is a combination of spread spectrum and quantization-based watermarking. Every bit of the watermark is spread over a number of wavelet coefficients with the use of a secret key by means of quantization. The selected wavelet detail coefficients from different subbands are quantized using an optimal quantization model, based on the characteristics of the human visual system (HVS). Our HVS-based scheme is compared to a non-HVS approach. The resilience of the watermarking algorithm is tested against a series of different spatial, temporal, and compression attacks. To improve the robustness of the algorithm, we use error correction codes and embed the watermark with spatial and temporal redundancy. The proposed method achieves a good perceptual quality and high resistance to a large spectrum of attacks.
Detection method of flexion relaxation phenomenon based on wavelets for patients with low back pain
NASA Astrophysics Data System (ADS)
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.
Iterative edge- and wavelet-based image registration of AVHRR and GOES satellite imagery
NASA Technical Reports Server (NTRS)
LeMoigne, Jacqueline; El-Saleous, Nazmi; Vermote, Eric
1997-01-01
Most automatic registration methods are either correlation-based, feature-based, or a combination of both. Examples of features which can be utilized for automatic image registration are edges, regions, corners, or wavelet-extracted features. In this paper, we describe two proposed approaches, based on edge or edge-like features, which are very appropriate to highlight regions of interest such as coastlines. The two iterative methods utilize the Normalized Cross-Correlation of edge and wavelet features and are applied to such problems as image-to-map registration, landmarking, and channel-to-channel co-registration, utilizing test data, AVHRR data, as well as GOES image data.
A wavelet based approach to Solar-Terrestrial Coupling
NASA Astrophysics Data System (ADS)
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.
Value at risk estimation with entropy-based wavelet analysis in exchange markets
NASA Astrophysics Data System (ADS)
He, Kaijian; Wang, Lijun; Zou, Yingchao; Lai, Kin Keung
2014-08-01
In recent years, exchange markets are increasingly integrated together. Fluctuations and risks across different exchange markets exhibit co-moving and complex dynamics. In this paper we propose the entropy-based multivariate wavelet based approaches to analyze the multiscale characteristic in the multidimensional domain and improve further the Value at Risk estimation reliability. Wavelet analysis has been introduced to construct the entropy-based Multiscale Portfolio Value at Risk estimation algorithm to account for the multiscale dynamic correlation. The entropy measure has been proposed as the more effective measure with the error minimization principle to select the best basis when determining the wavelet families and the decomposition level to use. The empirical studies conducted in this paper have provided positive evidence as to the superior performance of the proposed approach, using the closely related Chinese Renminbi and European Euro exchange market.
NASA Technical Reports Server (NTRS)
Sjoegreen, B.; Yee, H. C.
2001-01-01
The recently developed essentially fourth-order or higher low dissipative shock-capturing scheme of Yee, Sandham and Djomehri (1999) aimed at minimizing nu- merical dissipations for high speed compressible viscous flows containing shocks, shears and turbulence. To detect non smooth behavior and control the amount of numerical dissipation to be added, Yee et al. employed an artificial compression method (ACM) of Harten (1978) but utilize it in an entirely different context than Harten originally intended. The ACM sensor consists of two tuning parameters and is highly physical problem dependent. To minimize the tuning of parameters and physical problem dependence, new sensors with improved detection properties are proposed. The new sensors are derived from utilizing appropriate non-orthogonal wavelet basis functions and they can be used to completely switch to the extra numerical dissipation outside shock layers. The non-dissipative spatial base scheme of arbitrarily high order of accuracy can be maintained without compromising its stability at all parts of the domain where the solution is smooth. Two types of redundant non-orthogonal wavelet basis functions are considered. One is the B-spline wavelet (Mallat & Zhong 1992) used by Gerritsen and Olsson (1996) in an adaptive mesh refinement method, to determine regions where re nement should be done. The other is the modification of the multiresolution method of Harten (1995) by converting it to a new, redundant, non-orthogonal wavelet. The wavelet sensor is then obtained by computing the estimated Lipschitz exponent of a chosen physical quantity (or vector) to be sensed on a chosen wavelet basis function. Both wavelet sensors can be viewed as dual purpose adaptive methods leading to dynamic numerical dissipation control and improved grid adaptation indicators. Consequently, they are useful not only for shock-turbulence computations but also for computational aeroacoustics and numerical combustion. In addition, these
Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain
Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo
2012-01-01
An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. PMID:23049544
[Detection of R-wave in Fetal EGG Based on Wavelet Transform and Matched Filtering].
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
Serial identification of EEG patterns using adaptive wavelet-based analysis
NASA Astrophysics Data System (ADS)
Nazimov, A. I.; Pavlov, A. N.; Nazimova, A. A.; Grubov, V. V.; Koronovskii, A. A.; Sitnikova, E.; Hramov, A. E.
2013-10-01
A problem of recognition specific oscillatory patterns in the electroencephalograms with the continuous wavelet-transform is discussed. Aiming to improve abilities of the wavelet-based tools we propose a serial adaptive method for sequential identification of EEG patterns such as sleep spindles and spike-wave discharges. This method provides an optimal selection of parameters based on objective functions and enables to extract the most informative features of the recognized structures. Different ways of increasing the quality of patterns recognition within the proposed serial adaptive technique are considered.
Optimal block boundary pre/postfiltering for wavelet-based image and video compression.
Liang, Jie; Tu, Chengjie; Tran, Trac D
2005-12-01
This paper presents a pre/postfiltering framework to reduce the reconstruction errors near block boundaries in wavelet-based image and video compression. Two algorithms are developed to obtain the optimal filter, based on boundary filter bank and polyphase structure, respectively. A low-complexity structure is employed to approximate the optimal solution. Performances of the proposed method in the removal of JPEG 2000 tiling artifact and the jittering artifact of three-dimensional wavelet video coding are reported. Comparisons with other methods demonstrate the advantages of our pre/postfiltering framework. PMID:16370467
Eyebrows Identity Authentication Based on Wavelet Transform and Support Vector Machines
NASA Astrophysics Data System (ADS)
Jun-bin, CAO; Haitao, Yang; Lili, Ding
In order to study the novel biometric of eyebrow,,this paper presents an Eyebrows identity authentication based on wavelet transform and support vector machines. The features of the eyebrows image are extracted by wavelet transform, and then classifies them based on SVM. Verification results of the experiment on an eyebrow database taken from 100 of self-built personal demonstrate the effectiveness of the system. The system has a lower FAR 0.22%and FRR 28% Therefore, eyebrow recongnition may possibly apply to personal identification.
NASA Astrophysics Data System (ADS)
Zhang, Hongbo; Yi, Xingwen; Chen, Lei; Zhang, Jing; Deng, Mingliang; Qiu, Kun
2012-10-01
As an alternate to fast Fourier transform-based orthogonal frequency-division multiplexing (OFDM), wavelet packet transform (WPT)-based OFDM (WPT-OFDM) does not require cyclic prefix to avoid inter-symbol-interference. The wavelet has many varieties and therefore, it can provide more freedom for system design to suit different applications. We propose a real-valued WPT-OFDM that uses intensity modulation/direct detection. We also conduct an experiment to verify its performance through a 75-km standard single-mode fiber.
Joint wavelet-based coding and packetization for video transport over packet-switched networks
NASA Astrophysics Data System (ADS)
Lee, Hung-ju
1996-02-01
In recent years, wavelet theory applied to image, and audio and video compression has been extensively studied. However, only gaining compression ratio without considering the underlying networking systems is unrealistic, especially for multimedia applications over networks. In this paper, we present an integrated approach, which attempts to preserve the advantages of wavelet-based image coding scheme and to provide robustness to a certain extent for lost packets over packet-switched networks. Two different packetization schemes, called the intrablock-oriented (IAB) and interblock-oriented (IRB) schemes, in conjunction with wavelet-based coding, are presented. Our approach is evaluated under two different packet loss models with various packet loss probabilities through simulations which are driven by real video sequences.
A wavelet based algorithm for the identification of oscillatory event-related potential components.
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
NASA Astrophysics Data System (ADS)
Jemai, Olfa; Ejbali, Ridha; Zaied, Mourad; Ben Amar, Chokri
2015-02-01
This paper aims at developing a novel approach for speech recognition based on wavelet network learnt by fast wavelet transform (FWN) including a fuzzy decision support system (FDSS). Our contributions reside in, first, proposing a novel learning algorithm for speech recognition based on the fast wavelet transform (FWT) which has many advantages compared to other algorithms and in which major problems of the previous works to compute connection weights were solved. They were determined by a direct solution which requires computing matrix inversion, which may be intensive. However, the new algorithm was realized by the iterative application of FWT to compute connection weights. Second, proposing a new classification way for this speech recognition system. It operated a human reasoning mode employing a FDSS to compute similarity degrees between test and training signals. Extensive empirical experiments were conducted to compare the proposed approach with other approaches. Obtained results show that the new speech recognition system has a better performance than previously established ones.
[Ultrasound image de-noising based on nonlinear diffusion of complex wavelet transform].
Hou, Wen; Wu, Yiquan
2012-04-01
Ultrasound images are easily corrupted by speckle noise, which limits its further application in medical diagnoses. An image de-noising method combining dual-tree complex wavelet transform (DT-CWT) with nonlinear diffusion is proposed in this paper. Firstly, an image is decomposed by DT-CWT. Then adaptive-contrast-factor diffusion and total variation diffusion are applied to high-frequency component and low-frequency component, respectively. Finally the image is synthesized. The experimental results are given. The comparisons of the image de-noising results are made with those of the image de-noising methods based on the combination of wavelet shrinkage with total variation diffusion, the combination of wavelet/multiwavelet with nonlinear diffusion. It is shown that the proposed image de-noising method based on DT-CWT and nonlinear diffusion can obtain superior results. It can both remove speckle noise and preserve the original edges and textural features more efficiently. PMID:22616185
An investigation of time efficiency in wavelet-based Markov parameter extraction methods
Robertson, A.N.; Park, K.C.
1998-07-01
This paper investigates the time efficiency of using a wavelet transform-based method to extract the impulse response characteristics of a structural dynamic system. Traditional time domain procedures utilize the measured disturbances and response histories of a system to develop a set of auto and cross correlation functions. Through deconvolution of these functions, or matrix inversion, the Markov parameters of the system may be found. By transforming these functions into a wavelet basis, the size of the problem to be solved can be reduced as well as the computation time decreased. Fourier transforms are also used in this capacity as they may increase the time efficiency even more, but at the cost of accuracy. This paper will therefore compare the time requirements associated with a time, wavelet, and Fourier-based method of Markov parameter extraction, as well as their relative accuracy in modeling the system.
NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
Radiation dose reduction in digital radiography using wavelet-based image processing methods
NASA Astrophysics Data System (ADS)
Watanabe, Haruyuki; Tsai, Du-Yih; Lee, Yongbum; Matsuyama, Eri; Kojima, Katsuyuki
2011-03-01
In this paper, we investigate the effect of the use of wavelet transform for image processing on radiation dose reduction in computed radiography (CR), by measuring various physical characteristics of the wavelet-transformed images. Moreover, we propose a wavelet-based method for offering a possibility to reduce radiation dose while maintaining a clinically acceptable image quality. The proposed method integrates the advantages of a previously proposed technique, i.e., sigmoid-type transfer curve for wavelet coefficient weighting adjustment technique, as well as a wavelet soft-thresholding technique. The former can improve contrast and spatial resolution of CR images, the latter is able to improve the performance of image noise. In the investigation of physical characteristics, modulation transfer function, noise power spectrum, and contrast-to-noise ratio of CR images processed by the proposed method and other different methods were measured and compared. Furthermore, visual evaluation was performed using Scheffe's pair comparison method. Experimental results showed that the proposed method could improve overall image quality as compared to other methods. Our visual evaluation showed that an approximately 40% reduction in exposure dose might be achieved in hip joint radiography by using the proposed method.
A new methodology to map double-cropping croplands based on continuous wavelet transform
NASA Astrophysics Data System (ADS)
Qiu, Bingwen; Zhong, Ming; Tang, Zhenghong; Wang, Chongyang
2014-02-01
Cropping intensity is one of the major factors in crop production and agricultural intensification. A new double-cropping croplands mapping methodology using Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets through continuous wavelet transform was proposed in this study. This methodology involved four steps. First, daily continuous MODIS Enhanced Vegetation Index (EVI) time series datasets were developed for the study year. Next, the EVI time series datasets were transformed into a two dimensional (time-frequency) wavelet scalogram based on continuous wavelet transform. Third, a feature extraction process was conducted on the wavelet scalogram, where the characteristic spectra were calculated from the wavelet scalogram and the feature peak within two skeleton lines was obtained. Finally, a threshold was determined for feature peak values to discriminate double-cropping croplands within each pixel. The application of the proposed procedure to China's Henan Province in 2010 produced an objective and accurate spatial distribution map, which correlated well with in situ observation data (over 90% agreement). The proposed new methodology efficiently handled complex variability that might be caused by regional variation in climate, management practices, growth peaks by winter weed or winter wheat, and data noise. Therefore, the methodology shows promise for future studies at regional and global scales.
A Wavelet-Based Method for Simulation of Seismic Wave Propagation
NASA Astrophysics Data System (ADS)
Hong, T.; Kennett, B. L.
2001-12-01
Seismic wave propagation (e.g., both P-SV and SH in 2-D) can be modeled using wavelets. The governing elastic wave equations are transformed to a first-order differential equation system in time with a displacement-velocity formulation. Spatial derivatives are represented with a wavelet expansion using a semigroup approach. The evolution equations in time are derived from a Taylor expansion in terms of wavelet operators. The wavelet representation allows high accuracy for the spatial derivatives. Absorbing boundary conditions are implemented by including attenuation terms in the formulation of the equations. The traction-free condition at a free surface can be introduced with an equivalent force system. Irregular boundaries can be handled through a remapping of the coordinate system. The method is based on a displacement-velocity scheme which reduces memory requirements by about 30% compared to the use of velocity-stress. The new approach gives excellent agreement with analytic results for simple models including the Rayleigh waves at a free surface. A major strength of the wavelet approach is that the formulation can be employed for highly heterogeneous media and so can be used for complex situations.
A wavelet-based approach to detecting liveness in fingerprint scanners
NASA Astrophysics Data System (ADS)
Abhyankar, Aditya S.; Schuckers, Stephanie C.
2004-08-01
In this work, a method to provide fingerprint vitality authentication, in order to improve vulnerability of fingerprint identification systems to spoofing is introduced. The method aims at detecting 'liveness' in fingerprint scanners by using the physiological phenomenon of perspiration. A wavelet based approach is used which concentrates on the changing coefficients using the zoom-in property of the wavelets. Multiresolution analysis and wavelet packet analysis are used to extract information from low frequency and high frequency content of the images respectively. Daubechies wavelet is designed and implemented to perform the wavelet analysis. A threshold is applied to the first difference of the information in all the sub-bands. The energy content of the changing coefficients is used as a quantified measure to perform the desired classification, as they reflect a perspiration pattern. A data set of approximately 30 live, 30 spoof, and 14 cadaver fingerprint images was divided with first half as a training data while the other half as the testing data. The proposed algorithm was applied to the training data set and was able to completely classify 'live' fingers from 'not live' fingers, thus providing a method for enhanced security and improved spoof protection.
Deng, Jun-Min; Yue, Hai-Zhen; Zhuo, Zhi-Zheng; Yan, Hua-Gang; Liu, Di; Li, Hai-Yun
2014-05-01
Image registration between planning CT images and cone beam-CT (CBCT) images is one of the key technologies of image guided radiotherapy (IGRT). Current image registration methods fall roughly into two categories: geometric features-based and image grayscale-based. Mutual information (MI) based registration, which belongs to the latter category, has been widely applied to multi-modal and mono-modal image registration. However, the standard mutual information method only focuses on the image intensity information and overlooks spatial information, leading to the instability of intensity interpolation. Due to its use of positional information, wavelet transform has been applied to image registration recently. In this study, we proposed an approach to setup CT and cone beam-CT (CBCT) image registration in radiotherapy based on the combination of mutual information (MI) and stationary wavelet transform (SWT). Firstly, SWT was applied to generate gradient images and low frequency components produced in various levels of image decomposition were eliminated. Then inverse SWT was performed on the remaining frequency components. Lastly, the rigid registration of gradient images and original images was implemented using a weighting function with the normalized mutual information (NMI) being the similarity measure, which compensates for the lack of spatial information in mutual information based image registration. Our experiment results showed that the proposed method was highly accurate and robust, and indicated a significant clinical potential in improving the accuracy of target localization in image guided radiotherapy (IGRT). PMID:24729043
The decoding method based on wavelet image En vector quantization
NASA Astrophysics Data System (ADS)
Liu, Chun-yang; Li, Hui; Wang, Tao
2013-12-01
With the rapidly progress of internet technology, large scale integrated circuit and computer technology, digital image processing technology has been greatly developed. Vector quantization technique plays a very important role in digital image compression. It has the advantages other than scalar quantization, which possesses the characteristics of higher compression ratio, simple algorithm of image decoding. Vector quantization, therefore, has been widely used in many practical fields. This paper will combine the wavelet analysis method and vector quantization En encoder efficiently, make a testing in standard image. The experiment result in PSNR will have a great improvement compared with the LBG algorithm.
NASA Astrophysics Data System (ADS)
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.
The EM Method in a Probabilistic Wavelet-Based MRI Denoising.
Martin-Fernandez, Marcos; Villullas, Sergio
2015-01-01
Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images. PMID:26089959
NASA Astrophysics Data System (ADS)
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.
The EM Method in a Probabilistic Wavelet-Based MRI Denoising
2015-01-01
Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images. PMID:26089959
SVM algorithm based on wavelet kernel function for medical image segmentation
NASA Astrophysics Data System (ADS)
Yang, Jun; Tian, Jinwen; Liu, Jian; Wei, Fang
2009-10-01
Along with more demand for 3D reconstruction, quantitative analysis and visualization, the more precise segmentation of medical image is required, especially MR head image. But the segmentation of MRI will be much more complex and difficult because of indistinct boundaries between brain tissues due to their overlapping and penetrating with each other, intrinsic uncertainty of MR images induced by heterogeneity of magnetic field, partial volume effect and noise. After studying the kernel function conditions of support vector, we constructed wavelet SVM algorithm based on wavelet kernel function. Its convergence and commonality as well as generalization are analyzed. The comparative experiments are made using the different number of training samples and the different scans, and it .The wavelet SVM can be extended easily and experiment results show that the SVM classifier offers lower computational time and better classification precision and it has good function approximation ability.
Multi-scale Analysis of DSCOVR Data Using Wavelet Cross Correlation
NASA Astrophysics Data System (ADS)
Hegedus, A. M.; Kasper, J. C.; Stevens, M. L.; Alterman, B. L.; Case, A. W.; Szabo, A.; Koval, A.
2015-12-01
The Deep Space Climate Observatory (DSCOVR), launched February 11th 2015, makes the fastest combined measurements of solar wind magnetic field vectors and ion velocity distribution functions ever. These data allow us to search for correlation between ion and magnetic field fluctuations at kinetic ion scales for the first time. We present first results of a wavelet correlation analysis, which allows us to search for wave-particle interactions while accounting for different sampling cadences and data gaps. Using different wavelet algorithms we circumvent these issues and decompose the covariance and correlation between these two data streams on a scale by scale basis. We then generalize these quantities to wavelet cross-correlation and cross-covariance to identify interactions between charged particles and magnetic fields on kinetic scales. The techniques developed in this work will be directly applicable to plasma and magnetic field observations in the corona on the upcoming Solar Probe Plus mission.
Diagnosis System Based on Wavelet Transform, Fractal Dimension and Neural Network
NASA Astrophysics Data System (ADS)
El-Ramsisi, Abdallah M.; Khalil, Hassan A.
In this study we introduce a diagnosis system based on wavelet and fractal dimension for diagnose the Heart Mitral Valve Diseases. This study deals with the feature extraction from the Doppler signal waveform at heart mitral valve using ultrasound. Wavelet packet transforms, Fourier transform and Fractal Dimension methods are used for feature extraction from the DHS signals. The back-propagation neural network is used to classify the extracted features. The system has been evaluated in 162 samples that contain 89 normal and 73 abnormal. The results showed that the classification was about 91% for normal and abnormal cases.
A wavelet-based image quality metric for the assessment of 3D synthesized views
NASA Astrophysics Data System (ADS)
Bosc, Emilie; Battisti, Federica; Carli, Marco; Le Callet, Patrick
2013-03-01
In this paper we present a novel image quality assessment technique for evaluating virtual synthesized views in the context of multi-view video. In particular, Free Viewpoint Videos are generated from uncompressed color views and their compressed associated depth maps by means of the View Synthesis Reference Software, provided by MPEG. Prior to the synthesis step, the original depth maps are encoded with different coding algorithms thus leading to the creation of additional artifacts in the synthesized views. The core of proposed wavelet-based metric is in the registration procedure performed to align the synthesized view and the original one, and in the skin detection that has been applied considering that the same distortion is more annoying if visible on human subjects rather than on other parts of the scene. The effectiveness of the metric is evaluated by analyzing the correlation of the scores obtained with the proposed metric with Mean Opinion Scores collected by means of subjective tests. The achieved results are also compared against those of well known objective quality metrics. The experimental results confirm the effectiveness of the proposed metric.
Gerasimova, Evgeniya; Audit, Benjamin; Roux, Stephane G.; Khalil, André; Gileva, Olga; Argoul, Françoise; Naimark, Oleg; Arneodo, Alain
2014-01-01
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. PMID:24860510
Predictability of nonstationary time series using wavelet and EMD based ARMA models
NASA Astrophysics Data System (ADS)
Karthikeyan, L.; Nagesh Kumar, D.
2013-10-01
Research has been undertaken to ascertain the predictability of non-stationary time series using wavelet and Empirical Mode Decomposition (EMD) based time series models. Methods have been developed in the past to decompose a time series into components. Forecasting of these components combined with random component could yield predictions. Using this ideology, wavelet and EMD analyses have been incorporated separately which decomposes a time series into independent orthogonal components with both time and frequency localizations. The component series are fit with specific auto-regressive models to obtain forecasts which are later combined to obtain the actual predictions. Four non-stationary streamflow sites (USGS data resources) of monthly total volumes and two non-stationary gridded rainfall sites (IMD) of monthly total rainfall are considered for the study. The predictability is checked for six and twelve months ahead forecasts across both the methodologies. Based on performance measures, it is observed that wavelet based method has better prediction capabilities over EMD based method despite some of the limitations of time series methods and the manner in which decomposition takes place. Finally, the study concludes that the wavelet based time series algorithm can be used to model events such as droughts with reasonable accuracy. Also, some modifications that can be made in the model have been discussed that could extend the scope of applicability to other areas in the field of hydrology.
Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models
NASA Astrophysics Data System (ADS)
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
Mitra, S; Yang, S; Kustov, V
1998-11-01
Compression of medical images has always been viewed with skepticism, since the loss of information involved is thought to affect diagnostic information. However, recent research indicates that some wavelet-based compression techniques may not effectively reduce the image quality, even when subjected to compression ratios up to 30:1. The performance of a recently designed wavelet-based adaptive vector quantization is compared with a well-known wavelet-based scalar quantization technique to demonstrate the superiority of the former technique at compression ratios higher than 30:1. The use of higher compression with high fidelity of the reconstructed images allows fast transmission of images over the Internet for prompt inspection by radiologists at remote locations in an emergency situation, while higher quality images follow in a progressive manner if desired. Such fast and progressive transmission can also be used for downloading large data sets such as the Visible Human at a quality desired by the users for research or education. This new adaptive vector quantization uses a neural networks-based clustering technique for efficient quantization of the wavelet-decomposed subimages, yielding minimal distortion in the reconstructed images undergoing high compression. Results of compression up to 100:1 are shown for 24-bit color and 8-bit monochrome medical images. PMID:9848058
Symplectic wavelet transformation.
Fan, Hong-Yi; Lu, Hai-Liang
2006-12-01
Usually a wavelet transform is based on dilated-translated wavelets. We propose a symplectic-transformed-translated wavelet family psi(*)(r,s)(z-kappa) (r,s are the symplectic transform parameters, |s|(2)-|r|(2)=1, kappa is a translation parameter) generated from the mother wavelet psi and the corresponding wavelet transformation W(psi)f(r,s;kappa)=integral(infinity)(-infinity)(d(2)z/pi)f(z)psi(*)(r,s)(z-kappa). This new transform possesses well-behaved properties and is related to the optical Fresnel transform in quantum mechanical version. PMID:17099740
GPU-based 3D lower tree wavelet video encoder
NASA Astrophysics Data System (ADS)
Galiano, Vicente; López-Granado, Otoniel; Malumbres, Manuel P.; Drummond, Leroy Anthony; Migallón, Hector
2013-12-01
The 3D-DWT is a mathematical tool of increasing importance in those applications that require an efficient processing of huge amounts of volumetric info. Other applications like professional video editing, video surveillance applications, multi-spectral satellite imaging, HQ video delivery, etc, would rather use 3D-DWT encoders to reconstruct a frame as fast as possible. In this article, we introduce a fast GPU-based encoder which uses 3D-DWT transform and lower trees. Also, we present an exhaustive analysis of the use of GPU memory. Our proposal shows good trade off between R/D, coding delay (as fast as MPEG-2 for High definition) and memory requirements (up to 6 times less memory than x264).
Gait recognition based on Gabor wavelets and modified gait energy image for human identification
NASA Astrophysics Data System (ADS)
Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang
2013-10-01
This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.
Sun, Shao-bo; Du, Hua-qiangl; Li, Ping-heng; Zhou, Guo-mo; Xu, Xiao-juni; Gao, Guo-long; Li, Xue-jian
2016-01-01
This study focused on retrieval of net photosynthetic rate (Pn) of moso bamboo forest based on analysis of wavelet transform on hyperspectral reflectance data of moso bamboo forest leaf. The result showed that the accuracy of Pn retrieved by the ideal high frequency wavelet vegetation index ( VI) was higher than that retrieved by low frequency wavelet VI and spectral VI. Normalized difference vegetation index of wavelet (NDVIw), simple ratio vegetation index of wavelet (SRw) and difference vegetation index of wavelet (Dw) constructed by the first layer of high frequency coefficient through wavelet decomposition had the highest relationship with Pn, with the R² of 0.7 and RMSE of 0.33; low frequency wavelet VI had no advantage compared with spectral VI. Significant correlation existed between Pn estimated by multivariate linear model constructed by the ideal wavelet VI and the measured Pn, with the R² of 0.77 and RMSE of 0.29, and the accuracy was significantly higher than that of using the spectral VI. Compared with the fact that sensitive spectral bands of the retrieval through spectral VI were limited in the range of visible light, the wavelength of sensitive bands of wavelet VI ranged more widely from visible to infrared bands. The results illustrated that spectrum of wavelet transform could reflect the Pn of moso bamboo more in detail, and the overall accuracy was significantly improved than that using the original spectral data, which provided a new alternative method for retrieval of Pn of moso bamboo forest using hyper spectral remotely sensed data. PMID:27228592
Avci, Derya; Leblebicioglu, Mehmet Kemal; Poyraz, Mustafa; Dogantekin, Esin
2014-02-01
So far, analysis and classification of urine cells number has become an important topic for medical diagnosis of some diseases. Therefore, in this study, we suggest a new technique based on Adaptive Discrete Wavelet Entropy Energy and Neural Network Classifier (ADWEENN) for Recognition of Urine Cells from Microscopic Images Independent of Rotation and Scaling. Some digital image processing methods such as noise reduction, contrast enhancement, segmentation, and morphological process are used for feature extraction stage of this ADWEENN in this study. Nowadays, the image processing and pattern recognition topics have come into prominence. The image processing concludes operation and design of systems that recognize patterns in data sets. In the past years, very difficulty in classification of microscopic images was the deficiency of enough methods to characterize. Lately, it is seen that, multi-resolution image analysis methods such as Gabor filters, discrete wavelet decompositions are superior to other classic methods for analysis of these microscopic images. In this study, the structure of the ADWEENN method composes of four stages. These are preprocessing stage, feature extraction stage, classification stage and testing stage. The Discrete Wavelet Transform (DWT) and adaptive wavelet entropy and energy is used for adaptive feature extraction in feature extraction stage to strengthen the premium features of the Artificial Neural Network (ANN) classifier in this study. Efficiency of the developed ADWEENN method was tested showing that an avarage of 97.58% recognition succes was obtained. PMID:24493072
Wavelet-based techniques for the gamma-ray sky
NASA Astrophysics Data System (ADS)
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.
Electroencephalographic compression based on modulated filter banks and wavelet transform.
Bazán-Prieto, Carlos; Cárdenas-Barrera, Julián; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando
2011-01-01
Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet Packets transformation, seeking the best value for compression, best quality and more efficient real time implementation. Due to specific properties of EEG signals, we propose a quantization stage adapted to the dynamic range of each band, looking for higher quality. The results show that the compressor with filter bank performs better than transform methods. Quantization adapted to the dynamic range significantly enhances the quality. PMID:22255966
Wavelet-based fractal analysis of airborne pollen
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Rastigejev, Y.; Semakin, A. N.
2012-12-01
In this work we present a multilevel Wavelet-based Adaptive Mesh Refinement (WAMR) method for numerical modeling of global atmospheric chemical transport problems. An accurate numerical simulation of such problems presents an enormous challenge. Atmospheric Chemical Transport Models (CTMs) combine chemical reactions with meteorologically predicted atmospheric advection and turbulent mixing. The resulting system of multi-scale advection-reaction-diffusion equations is extremely stiff, nonlinear and involves a large number of chemically interacting species. As a consequence, the need for enormous computational resources for solving these equations imposes severe limitations on the spatial resolution of the CTMs implemented on uniform or quasi-uniform grids. In turn, this relatively crude spatial resolution results in significant numerical diffusion introduced into the system. This numerical diffusion is shown to noticeably distort the pollutant mixing and transport dynamics for typically used grid resolutions. The developed WAMR method for numerical modeling of atmospheric chemical evolution equations presented in this work provides a significant reduction in the computational cost, without upsetting numerical accuracy, therefore it addresses the numerical difficulties described above. WAMR method introduces a fine grid in the regions where sharp transitions occur and cruder grid in the regions of smooth solution behavior. Therefore WAMR results in much more accurate solutions than conventional numerical methods implemented on uniform or quasi-uniform grids. The algorithm allows one to provide error estimates of the solution that are used in conjunction with appropriate threshold criteria to adapt the non-uniform grid. The method has been tested for a variety of problems including numerical simulation of traveling pollution plumes. It was shown that pollution plumes in the remote troposphere can propagate as well-defined layered structures for two weeks or more as
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
Mouse EEG spike detection based on the adapted continuous wavelet transform
NASA Astrophysics Data System (ADS)
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.
Dual tree complex wavelet transform based denoising of optical microscopy images.
Bal, Ufuk
2012-12-01
Photon shot noise is the main noise source of optical microscopy images and can be modeled by a Poisson process. Several discrete wavelet transform based methods have been proposed in the literature for denoising images corrupted by Poisson noise. However, the discrete wavelet transform (DWT) has disadvantages such as shift variance, aliasing, and lack of directional selectivity. To overcome these problems, a dual tree complex wavelet transform is used in our proposed denoising algorithm. Our denoising algorithm is based on the assumption that for the Poisson noise case threshold values for wavelet coefficients can be estimated from the approximation coefficients. Our proposed method was compared with one of the state of the art denoising algorithms. Better results were obtained by using the proposed algorithm in terms of image quality metrics. Furthermore, the contrast enhancement effect of the proposed method on collagen fıber images is examined. Our method allows fast and efficient enhancement of images obtained under low light intensity conditions. PMID:23243573
Fourier-, Hilbert- and wavelet-based signal analysis: are they really different approaches?
Bruns, Andreas
2004-08-30
Spectral signal analysis constitutes one of the most important and most commonly used analytical tools for the evaluation of neurophysiological signals. It is not only the spectral parameters per se (amplitude and phase) which are of interest, but there is also a variety of measures derived from them, including important coupling measures like coherence or phase synchrony. After reviewing some of these measures in order to underline the widespread relevance of spectral analysis, this report compares the three classical spectral analysis approaches: Fourier, Hilbert and wavelet transform. Recently, there seems to be increasing acceptance of the notion that Hilbert- or wavelet-based analyses be in some way superior to Fourier-based analyses. The present article counters such views by demonstrating that the three techniques are in fact formally (i.e. mathematically) equivalent when using the class of wavelets that is typically applied in spectral analyses. Moreover, spectral amplitude serves as an example to show that Fourier, Hilbert and wavelet analysis also yield equivalent results in practical applications to neuronal signals. PMID:15262077
Wavelet Analysis for Acoustic Phased Array
NASA Astrophysics Data System (ADS)
Kozlov, Inna; Zlotnick, Zvi
2003-03-01
Wavelet spectrum analysis is known to be one of the most powerful tools for exploring quasistationary signals. In this paper we use wavelet technique to develop a new Direction Finding (DF) Algorithm for the Acoustic Phased Array (APA) systems. Utilising multi-scale analysis of libraries of wavelets allows us to work with frequency bands instead of individual frequency of an acoustic source. These frequency bands could be regarded as features extracted from quasistationary signals emitted by a noisy object. For detection, tracing and identification of a sound source in a noisy environment we develop smart algorithm. The essential part of this algorithm is a special interacting procedure of the above-mentioned DF-algorithm and the wavelet-based Identification (ID) algorithm developed in [4]. Significant improvement of the basic properties of a receiving APA pattern is achieved.
An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing
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 data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter-bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks. PMID:25426428
An Undecimated Wavelet-based Method for Cochlear Implant Speech Processing.
Hajiaghababa, Fatemeh; Kermani, Saeed; Marateb, Hamid R
2014-10-01
A cochlear implant is an implanted electronic device used to provide a sensation of hearing to a person who is hard of hearing. The cochlear implant is often referred to as a bionic ear. This paper presents an undecimated wavelet-based speech coding strategy for cochlear implants, which gives a novel speech processing strategy. The undecimated wavelet packet transform (UWPT) is computed like the wavelet packet transform except that it does not down-sample the output at each level. The speech data used for the current study consists of 30 consonants, sampled at 16 kbps. The performance of our proposed UWPT method was compared to that of infinite impulse response (IIR) filter in terms of mean opinion score (MOS), short-time objective intelligibility (STOI) measure and segmental signal-to-noise ratio (SNR). Undecimated wavelet had better segmental SNR in about 96% of the input speech data. The MOS of the proposed method was twice in comparison with that of the IIR filter-bank. The statistical analysis revealed that the UWT-based N-of-M strategy significantly improved the MOS, STOI and segmental SNR (P < 0.001) compared with what obtained with the IIR filter-bank based strategies. The advantage of UWPT is that it is shift-invariant which gives a dense approximation to continuous wavelet transform. Thus, the information loss is minimal and that is why the UWPT performance was better than that of traditional filter-bank strategies in speech recognition tests. Results showed that the UWPT could be a promising method for speech coding in cochlear implants, although its computational complexity is higher than that of traditional filter-banks. PMID:25426428
A millimeter wave image fusion algorithm design and optimization based on CDF97 wavelet transform
NASA Astrophysics Data System (ADS)
Yu, Jian-cheng; Chen, Bo-yang; Xia, A.-lin; Liu, Xin-guang
2011-08-01
Millimeter wave imaging technology provides a new detection method for security, fast and safe. But the wave of the images is its own shortcomings, such as noise and low sensitivity. Systems used for security, since only the corresponding specific objects to retain the information, and other information missing, so the actual image is difficult to locate in the millimeter wave . Image fusion approach can be used to effectively solve this problem. People usually use visible and millimeter-wave image fusion. The use of visible image contains the visual information. The fused image can be more convenient site for the detection of concealed weapons and to provide accurate positioning. The integration of information from different detectors, and there are different between the two levels of signal to noise ratio and pixel resolution, so traditional pixel-level fusion methods often cannot satisfy the fusion. Many experts and scholars apply wavelet transform approach to deal with some remote sensing image fusion, and the performance has been greatly improved. Due to these wavelet transform algorithm with complexity and large amount of computation, many algorithms are still in research stage. In order to improve the fusion performance and gain the real-time image fusion, an Integer Wavelet Transform CDF97 based with regional energy enhancement fusion algorithm is proposed in this paper. First, this paper studies of choice of wavelet operator. The paper invites several characteristics to evaluate the performance of wavelet operator used in image fusion. Results show that CDF97 wavelet fusion performance is better than traditional wavelet wavelets such as db wavelet, the vanishing moment longer the better. CDF97 wavelet has good energy concentration characteristic. The low frequency region of the transformed image contains almost the whole image energy. The target in millimeter wave image often has the low-pass characteristics and with a higher energy compare to the ambient
A FPGA system for QRS complex detection based on Integer Wavelet Transform
NASA Astrophysics Data System (ADS)
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.
Gradolewski, Dawid; Redlarski, Grzegorz
2014-09-01
The main obstacle in development of intelligent autodiagnosis medical systems based on the analysis of phonocardiography (PCG) signals is noise. The noise can be caused by digestive and respiration sounds, movements or even signals from the surrounding environment and it is characterized by wide frequency and intensity spectrum. This spectrum overlaps the heart tones spectrum, which makes the problem of PCG signal filtrating complex. The most common method for filtering such signals are wavelet denoising algorithms. In previous studies, in order to determine the optimum wavelet denoising parameters the disturbances were simulated by Gaussian white noise. However, this paper shows that this noise has a variable character. Therefore, the purpose of this paper is adaptation of a wavelet denoising algorithm for the filtration of real PCG signal disturbances from signals recorded by a mobile devices in a noisy environment. The best results were obtained for Coif 5 wavelet at the 10th decomposition level with the use of a minimaxi threshold selection algorithm and mln rescaling function. The performance of the algorithm was tested on four pathological heart sounds: early systolic murmur, ejection click, late systolic murmur and pansystolic murmur. PMID:25038586
[Analysis of spectral characteristics of oil film on water based on wavelet transform].
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
NASA Astrophysics Data System (ADS)
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.
Medical image compression based on a morphological representation of wavelet coefficients.
Phelan, N C; Ennis, J T
1999-08-01
Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting. PMID:10501061
Comparison of neuron selection algorithms of wavelet-based neural network
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Li, Xinzhong; Chen, Qingdong; Zhen, Zhiqiang; Yan, Haitao; Liu, Huihui; Li, Liben
2009-11-01
The process of thermal denaturation of the albumin was investigated using dynamic speckle method based on wavelet entropy and analyzed by light scattering theory. In experiments, the dynamic speckle patterns sequences generated by albumin colloid during denaturing were acquired using a CCD camera. By analyzing the variations of wavelet entropy values of the THSPs (the time history of speckle patterns), the thermal denaturation process of albumin could be divided into two stages. At former heating process, the values of wavelet entropy were bigger; correspondingly, the protein particles were aggregated and flocculated quickly. Conversely, at latter heating process, the wavelet entropy values decreased drastically, which meant there was slow aggregation. According to those, the movement properties of the protein molecule ensemble were analyzed during thermal denaturation of the albumin. The results show that this method is effective to analyze the process of movement and aggregation of protein molecules quantitatively. The experiment proved that this method is an useful tool to investigate the particles motion in solution.
Theory and application of frequency-selective wavelets
Tomas, B.
1992-01-01
Orthonormal compactly supported wavelets have been successfully applied to generate sparse representations of piecewise-smooth functions, yielding fast numerical algorithms. The authors consider the case of case of piecewise oscillatory functions, and construct a variation of the original Daubechies family of wavelets which efficiently represents the oscillations. This new family is constructed by moving some of the zeros of the underlying symbol away from [pi], shifting the approximation properties of the wavelets. The zeros may be chosen to give a sparse representation of an oscillatory function whose spectrum is known. In this sense, these wavelets are frequency-selective. Existence, uniqueness, and regularity results are proved for this family of wavelets. A natural application is the numerical solution of the electric field integral equation in two spatial dimensions: The kernel is singular on the diagonal, and oscillatory within a narrow frequency spectrum away from the diagonal. Applying frequency selective wavelets with the discrete wavelet transform, the discrete equations are transformed into a sparse linear system which is economically solved by a multi-grid scheme based upon the discrete wavelet transform. Substantial computational savings are obtained over the same method using the original Daubechies family of wavelets, and a factor of 10 savings is obtained over standard LU-factorization.
NASA Astrophysics Data System (ADS)
Ren, Zhong; Liu, Guodong; Huang, Zhen
2014-10-01
Real-time monitoring of blood glucose concentration (BGC) is a great important procedure in controlling diabetes mellitus and preventing the complication for diabetic patients. Noninvasive measurement of BGC has already become a research hotspot because it can overcome the physical and psychological harm. Photoacoustic spectroscopy is a well-established, hybrid and alternative technique used to determine the BGC. According to the theory of photoacoustic technique, the blood is irradiated by plused laser with nano-second repeation time and micro-joule power, the photoacoustic singals contained the information of BGC are generated due to the thermal-elastic mechanism, then the BGC level can be interpreted from photoacoustic signal via the data analysis. But in practice, the time-resolved photoacoustic signals of BGC are polluted by the varities of noises, e.g., the interference of background sounds and multi-component of blood. The quality of photoacoustic signal of BGC directly impacts the precision of BGC measurement. So, an improved wavelet denoising method was proposed to eliminate the noises contained in BGC photoacoustic signals. To overcome the shortcoming of traditional wavelet threshold denoising, an improved dual-threshold wavelet function was proposed in this paper. Simulation experimental results illustrated that the denoising result of this improved wavelet method was better than that of traditional soft and hard threshold function. To varify the feasibility of this improved function, the actual photoacoustic BGC signals were test, the test reslut demonstrated that the signal-to-noises ratio(SNR) of the improved function increases about 40-80%, and its root-mean-square error (RMSE) decreases about 38.7-52.8%.
Gu Junhua; Xu Haiguang; Wang Jingying; Chen Wen; An Tao
2013-08-10
We propose a continuous wavelet transform based non-parametric foreground subtraction method for the detection of redshifted 21 cm signal from the epoch of reionization. This method works based on the assumption that the foreground spectra are smooth in frequency domain, while the 21 cm signal spectrum is full of saw-tooth-like structures, thus their characteristic scales are significantly different. We can distinguish them in the wavelet coefficient space easily and perform the foreground subtraction. Compared with the traditional spectral fitting based method, our method is more tolerant to complex foregrounds. Furthermore, we also find that when the instrument has uncorrected response error, our method can also work significantly better than the spectral fitting based method. Our method can obtain similar results with the Wp smoothing method, which is also a non-parametric method, but our method consumes much less computing time.
NASA Astrophysics Data System (ADS)
Gu, Junhua; Xu, Haiguang; Wang, Jingying; An, Tao; Chen, Wen
2013-08-01
We propose a continuous wavelet transform based non-parametric foreground subtraction method for the detection of redshifted 21 cm signal from the epoch of reionization. This method works based on the assumption that the foreground spectra are smooth in frequency domain, while the 21 cm signal spectrum is full of saw-tooth-like structures, thus their characteristic scales are significantly different. We can distinguish them in the wavelet coefficient space easily and perform the foreground subtraction. Compared with the traditional spectral fitting based method, our method is more tolerant to complex foregrounds. Furthermore, we also find that when the instrument has uncorrected response error, our method can also work significantly better than the spectral fitting based method. Our method can obtain similar results with the Wp smoothing method, which is also a non-parametric method, but our method consumes much less computing time.
Wavelet based mobile video watermarking: spread spectrum vs. informed embedding
NASA Astrophysics Data System (ADS)
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.
Ultrasonic Periodontal Probing Based on the Dynamic Wavelet Fingerprint
NASA Astrophysics Data System (ADS)
Hinders, Mark K.; Hou, Jidong
2005-04-01
Manual pocket depth probing has been widely used as a retrospective diagnosis method in periodontics. However, numerous studies have questioned its ability to accurately measure the anatomic pocket depth. In this paper, an ultrasonic periodontal probing method is described, which involves using a hollow water-filled probe to focus a narrow beam of ultrasound energy into and out of the periodontal pocket, followed by automatic processing of pulse-echo signals to obtain the periodontal pocket depth. The signal processing algorithm consists of three steps: peak detection/characterization, peak classification and peak identification. A dynamic wavelet fingerprint (DWFP) technique was first applied to detect suspected scatterers in the A-scan signal and generate a two-dimensional black and white pattern to characterize the local transient signal corresponding to each scatterer. These DWFP patterns were then classified by a two-dimensional FFT procedure and mapped to an inclination index curve. The location of the pocket bottom was identified as the third broad peak in the inclination index curve. The algorithm was tested on full mouth probing data from two sequential visits of 14 patients. Its performance was evaluated by comparing ultrasonic probing results with that of full-mouth manual probing at the same sites, which was taken as the `gold standard'.
Ultrasonic Periodontal Probing Based on the Dynamic Wavelet Fingerprint
NASA Astrophysics Data System (ADS)
Hou, Jidong; Rose, S. Timothy; Hinders, Mark K.
2005-12-01
Manual pocket depth probing has been widely used as a retrospective diagnosis method in periodontics. However, numerous studies have questioned its ability to accurately measure the anatomic pocket depth. In this paper, an ultrasonic periodontal probing method is described, which involves using a hollow water-filled probe to focus a narrow beam of ultrasound energy into and out of the periodontal pocket, followed by automatic processing of pulse-echo signals to obtain the periodontal pocket depth. The signal processing algorithm consists of three steps: peak detection/characterization, peak classification, and peak identification. A dynamic wavelet fingerprint (DWFP) technique is first applied to detect suspected scatterers in the A-scan signal and generate a two-dimensional black and white pattern to characterize the local transient signal corresponding to each scatterer. These DWFP patterns are then classified by a two-dimensional FFT procedure and mapped to an inclination index curve. The location of the pocket bottom was identified as the third broad peak in the inclination index curve. The algorithm is tested on full-mouth probing data from two sequential visits of 14 patients. Its performance is evaluated by comparing ultrasonic probing results with that of full-mouth manual probing at the same sites, which is taken as the "gold standard."
Kang, S C; Hong, S H
2001-01-01
One of the most significant features of diagnostic echocardiographic images is to reduce speckle noise and make better image quality. In this paper we proposed a simple and effective filter design for image denoising and contrast enhancement based on multiscale wavelet denoising method. Wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. After we estimate distribution of noise within echocardiographic image, then apply to fitness Wavelet threshold algorithm. A common way of the estimating the speckle noise level in coherent imaging is to calculate the mean-to-standard-deviation ratio of the pixel intensity, often termed the Equivalent Number of Looks(ENL), over a uniform image area. Unfortunately, we found this measure not very robust mainly because of the difficulty to identify a uniform area in a real image. For this reason, we will only use here the S/MSE ratio and which corresponds to the standard SNR in case of additivie noise. We have simulated some echocardiographic images by specialized hardware for real-time application;processing of a 512*512 images takes about 1 min. Our experiments show that the optimal threshold level depends on the spectral content of the image. High spectral content tends to over-estimate the noise standard deviation estimation performed at the finest level of the DWT. As a result, a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends on the number of signal samples only. PMID:11604864
Hu, Shan; Xu, Chao; Guan, Weiqiao; Tang, Yong; Liu, Yana
2014-01-01
Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing. PMID:24211892
NASA Astrophysics Data System (ADS)
Xu, Zhonghua
The magnetosphere-ionosphere contains a number of current systems. These currents vary on a wide range of spatial and temporal scales and physically couple with each other. To study the complicated behaviors of these coupled current systems, the ground-based magnetometer has been a useful tool, but the recorded magnetometer data are always multi-scaled and intermittent due to the nature of these current systems. To distinguish these geomagnetic effects with multiple temporal and frequency scales, the wavelet analysis technique is especially suitable because of its special abilities of presenting information in both temporal and frequency domains. In this dissertation, the geomagnetic disturbances and the ring current variability during storm and quiet times are studied by using wavelet analysis and ground-based magnetic data from multiple stations. The first part of this dissertation investigates the strengths of applying the wavelet procedure to geomagnetic data for ring current study during storm and quiet periods. The second part of this dissertation characterizes the geomagnetic effects caused by symmetric and asymmetric components of ring currents during storm and quiet times by applying wavelet analysis to geomagnetic data from multiple stations. The third part of this dissertation studies the spatial variability of the symmetric ring current by applying the wavelet analysis technique to multiple components of magnetic data from multiple stations. The results show the unique strengths of the wavelet method allow us to quantitatively distinguish the geomagnetic effects on ring current variations from other M-I current systems. The unique strengths of wavelet method also allow us to separate the magnetic effects of the symmetric ring current from those caused by the asymmetric ring current. Quantitative information of the spatial variability of the ring currents is essential for understanding the dynamics of the ring currents, as well as the magnetic storm
Wavelet-based surrogate time series for multiscale simulation of heterogeneous catalysis
Savara, Aditya Ashi; Daw, C. Stuart; Xiong, Qingang; Gur, Sourav; Danielson, Thomas L.; Hin, Celine N.; Pannala, Sreekanth; Frantziskonis, George N.
2016-01-28
We propose a wavelet-based scheme that encodes the essential dynamics of discrete microscale surface reactions in a form that can be coupled with continuum macroscale flow simulations with high computational efficiency. This makes it possible to simulate the dynamic behavior of reactor-scale heterogeneous catalysis without requiring detailed concurrent simulations at both the surface and continuum scales using different models. Our scheme is based on the application of wavelet-based surrogate time series that encodes the essential temporal and/or spatial fine-scale dynamics at the catalyst surface. The encoded dynamics are then used to generate statistically equivalent, randomized surrogate time series, which canmore » be linked to the continuum scale simulation. As a result, we illustrate an application of this approach using two different kinetic Monte Carlo simulations with different characteristic behaviors typical for heterogeneous chemical reactions.« less
Corrosion in Reinforced Concrete Panels: Wireless Monitoring and Wavelet-Based Analysis
Qiao, Guofu; Sun, Guodong; Hong, Yi; Liu, Tiejun; Guan, Xinchun
2014-01-01
To realize the efficient data capture and accurate analysis of pitting corrosion of the reinforced concrete (RC) structures, we first design and implement a wireless sensor and network (WSN) to monitor the pitting corrosion of RC panels, and then, we propose a wavelet-based algorithm to analyze the corrosion state with the corrosion data collected by the wireless platform. We design a novel pitting corrosion-detecting mote and a communication protocol such that the monitoring platform can sample the electrochemical emission signals of corrosion process with a configured period, and send these signals to a central computer for the analysis. The proposed algorithm, based on the wavelet domain analysis, returns the energy distribution of the electrochemical emission data, from which close observation and understanding can be further achieved. We also conducted test-bed experiments based on RC panels. The results verify the feasibility and efficiency of the proposed WSN system and algorithms. PMID:24556673
A linear quality control design for high efficient wavelet-based ECG data compression.
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
NASA Astrophysics Data System (ADS)
Yang, J.-S.; Yu, S.-P.; Liu, G.-M.
2013-12-01
In order to increase the accuracy of serial-propagated long-range multi-step-ahead (MSA) prediction, which has high practical value but also great implementary difficulty because of huge error accumulation, a novel wavelet neural network hybrid model - CDW-NN - combining continuous and discrete wavelet transforms (CWT and DWT) and neural networks (NNs), is designed as the MSA predictor for the effective long-term forecast of hydrological signals. By the application of 12 types of hybrid and pure models in estuarine 1096-day river stages forecasting, the different forecast performances and the superiorities of CDW-NN model with corresponding driving mechanisms are discussed. One type of CDW-NN model, CDW-NF, which uses neuro-fuzzy as the forecast submodel, has been proven to be the most effective MSA predictor for the prominent accuracy enhancement during the overall 1096-day long-term forecasts. The special superiority of CDW-NF model lies in the CWT-based methodology, which determines the 15-day and 28-day prior data series as model inputs by revealing the significant short-time periodicities involved in estuarine river stage signals. Comparing the conventional single-step-ahead-based long-term forecast models, the CWT-based hybrid models broaden the prediction range in each forecast step from 1 day to 15 days, and thus reduce the overall forecasting iteration steps from 1096 steps to 74 steps and finally create significant decrease of error accumulations. In addition, combination of the advantages of DWT method and neuro-fuzzy system also benefits filtering the noisy dynamics in model inputs and enhancing the simulation and forecast ability for the complex hydro-system.
NASA Astrophysics Data System (ADS)
Yang, J.-S.; Yu, S.-P.; Liu, G.-M.
2013-07-01
In order to increase the accuracy of serial-propagated long-range multi-step-ahead (MSA) prediction, which has high practical value but also great difficulty to conduct because of huge error accumulation, a novel wavelet-NN hybrid model CDW-NN, combining continuous and discrete wavelet transforms (CWT and DWT) and neural networks (NN), is designed as the MSA predictor for effective long-term forecast of hydrological signals. By the application of 12 types of hybrid and pure models in estuarine 1096 day river stage series forecasting, different forecast performances and the superiorities of CDW-NN model with corresponding driving mechanisms are discussed, and one type of CDW-NN model (CDW-NF), which uses Neuro-Fuzzy as the forecast submodel, has been proven to be the most effective MSA predictor for the accuracy enhancement in the overall 1096 days long-term forecast. The special superiority of CDW-NF model lies in the CWT based methodology, which determines the 15 and 28 day prior data series as model inputs by revealing the significant short-time periodicities involved in estuarine river stage signals. Comparing conventional single-step-ahead based long-term forecast models, the CWT based hybrid models broaden the prediction range in each forecast step from 1 day to 15 days, thus reduce the overall forecasting iteration steps from 1096 steps to 74 steps and finally creates significant decrease of error accumulations. In addition, combination of the advantages of DWT method and Neuro-Fuzzy system also very benefit filtering the noisy dynamics for model inputs and enhancing the simulation and forecast ability of the complex hydro-system.
Application of Wavelet Based Denoising for T-Wave Alternans Analysis in High Resolution ECG Maps
NASA Astrophysics Data System (ADS)
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.
Pipe crack identification based on finite element method of second generation wavelets
NASA Astrophysics Data System (ADS)
Ye, Junjie; He, Yumin; Chen, Xuefeng; Zhai, Zhi; Wang, Youming; He, Zhengjia
2010-02-01
In this paper, a new method is presented to identify crack location and size, which is based on stress intensity factor suitable for pipe structure and finite element method of second generation wavelets (SGW-FEM). Pipe structure is dispersed into a series of nested thin-walled pipes. By making use of stress intensity factor of the thin-walled pipe, a new calculation method of crack equivalent stiffness is proposed to solve the stress intensity factor of the pipe structure. On this basis, finite element method of second generation wavelets is used to establish the dynamic model of cracked pipe. Then we combine forward problem with inverse problem in order to establish quantitative identification method of the crack based on frequency change, which provides a non-destructive testing technology with vibration for the pipe structure. The efficiency of the proposed method is verified by experiments.
Conjugate Event Study of Geomagnetic ULF Pulsations with Wavelet-based Indices
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
Ultrasonic test of resistance spot welds based on wavelet package analysis.
Liu, Jing; Xu, Guocheng; Gu, Xiaopeng; Zhou, Guanghao
2015-02-01
In this paper, ultrasonic test of spot welds for stainless steel sheets has been studied. It is indicated that traditional ultrasonic signal analysis in either time domain or frequency domain remains inadequate to evaluate the nugget diameter of spot welds. However, the method based on wavelet package analysis in time-frequency domain can easily distinguish the nugget from the corona bond by extracting high-frequency signals in different positions of spot welds, thereby quantitatively evaluating the nugget diameter. The results of ultrasonic test fit the actual measured value well. Mean value of normal distribution of error statistics is 0.00187, and the standard deviation is 0.1392. Furthermore, the quality of spot welds was evaluated, and it is showed ultrasonic nondestructive test based on wavelet packet analysis can be used to evaluate the quality of spot welds, and it is more reliable than single tensile destructive test. PMID:25454096
Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy
NASA Astrophysics Data System (ADS)
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.
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
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
An adaptive wavelet-based deblocking algorithm for MPEG-4 codec
NASA Astrophysics Data System (ADS)
Truong, Trieu-Kien; Chen, Shi-Huang; Jhang, Rong-Yi
2005-08-01
This paper proposed an adaptive wavelet-based deblocking algorithm for MPEG-4 video coding standard. The novelty of this method is that the deblocking filter uses a wavelet-based threshold to detect and analyze artifacts on coded block boundaries. This threshold value is based on the difference between the wavelet transform coefficients of image blocks and the coefficients of the entire image. Therefore, the threshold value is made adaptive to different images and characteristics of blocking artifacts. Then one can attenuate those artifacts by applying a selected filter based on the above threshold value. It is shown in this paper that the proposed method is robust, fast, and works remarkably well for MPEG-4 codec at low bit rates. Another advantage of the new method is that it retains sharp features in the decoded frames since it only removes artifacts. Experimental results show that the proposed method can achieve a significantly improved visual quality and increase the PSNR in the decoded video frame.
NASA Astrophysics Data System (ADS)
Pavlov, Alexey N.; Sindeeva, Olga A.; Sindeev, Sergey S.; Pavlova, Olga N.; Rybalova, Elena V.; Semyachkina-Glushkovskaya, Oxana V.
2016-03-01
In this paper we address the problem of revealing and recognition transitions between distinct physiological states using quite short fragments of experimental recordings. With the wavelet-based multifractal analysis we characterize changes of complexity and correlation properties in the stress-induced dynamics of arterial blood pressure in rats. We propose an approach for association revealed changes with distinct physiological regulatory mechanisms and for quantifying the influence of each mechanism.
Wavelet-based analysis of gastric microcirculation in rats with ulcer bleedings
NASA Astrophysics Data System (ADS)
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.
Wavelet-based clustering of resting state MRI data in the rat.
Medda, Alessio; Hoffmann, Lukas; Magnuson, Matthew; Thompson, Garth; Pan, Wen-Ju; Keilholz, Shella
2016-01-01
While functional connectivity has typically been calculated over the entire length of the scan (5-10min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas. PMID:26481903
Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review
NASA Astrophysics Data System (ADS)
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.
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
The use of wavelet transformations in the solution of two-phase flow problems
Moridis, G.J.; Nikolaou, M.; You, Y.
1995-12-31
In this paper the authors present the use of wavelets to solve the non-linear Partial Differential Equation (PDE) of two-phase flow in one dimension. The wavelet transforms allow a drastically different approach in the discretization of space. In contrast to the traditional trigonometric basis functions, wavelets approximate a function not by cancellation but by placement of wavelets at appropriate locations. When an abrupt change, such as a shock wave or a spike, occurs in a function, only local coefficients in a wavelet approximation will be affected. The unique feature of wavelets is their Multi-Resolution Analysis (MRA) property, which allows seamless investigation at nay spatial resolution. The use of wavelets is tested in the solution of the one-dimensional Buckley-Leverett problem against analytical solutions and solutions obtained from standard numerical models. Two classes of wavelet bases (Daubechies and Chui-Wang) and two methods (Galerkin and collocation) are investigated. The authors determine that the Chui-Wang wavelets and a collection method provide the optimum wavelet solution for this type of problem. Increasing the resolution level improves the accuracy of the solution, but the order of the basis function seems to be far less important. The results indicate that wavelet transforms are an effective and accurate method which does not suffer from oscillations or numerical smearing in the presence of steep fronts.
The use of wavelet transforms in the solution of two-phase flow problems
Moridis, G.J.; Nikolaou, M.; You, Yong
1994-10-01
In this paper we present the use of wavelets to solve the nonlinear Partial Differential.Equation (PDE) of two-phase flow in one dimension. The wavelet transforms allow a drastically different approach in the discretization of space. In contrast to the traditional trigonometric basis functions, wavelets approximate a function not by cancellation but by placement of wavelets at appropriate locations. When an abrupt chance, such as a shock wave or a spike, occurs in a function, only local coefficients in a wavelet approximation will be affected. The unique feature of wavelets is their Multi-Resolution Analysis (MRA) property, which allows seamless investigational any spatial resolution. The use of wavelets is tested in the solution of the one-dimensional Buckley-Leverett problem against analytical solutions and solutions obtained from standard numerical models. Two classes of wavelet bases (Daubechies and Chui-Wang) and two methods (Galerkin and collocation) are investigated. We determine that the Chui-Wang, wavelets and a collocation method provide the optimum wavelet solution for this type of problem. Increasing the resolution level improves the accuracy of the solution, but the order of the basis function seems to be far less important. Our results indicate that wavelet transforms are an effective and accurate method which does not suffer from oscillations or numerical smearing in the presence of steep fronts.
Nasif, Hesham R.; Neyama, Atsushi
2002-07-01
This work represents a WIRS code developed using wavelet Galerkin method to solve radionuclide transport model in near field and far field of a repository for high-level radioactive waste. After overpack failure, radionuclides diffuse through the bentonite buffer material to the water bearing fracture around the repository transport horizontally through this geosphere then transport vertically through the major water conducting fault (MWCF) reach the biosphere. The radionuclides transport barriers considered in this model are engineered barrier system (EBS), geosphere, and MWCF. Hydraulic conductivity of the bentonite is more than three orders of magnitude smaller than that of the surrounding host rock, so the only transport mechanism through EBS is diffusion. In the host rock, the problem is of advection-diffusion type with highly varying parameters from one medium to other due to the variability in length, transmissivity and other transport-relevant properties of the transport paths. Daubechies' wavelet is used as a basis function to solve the nonlinear partial differential equations arising from the model formulation of the radionuclides transport. Since the scaling functions are compactly supported, only a finite number of the connection coefficients are nonzero. The resultant matrix has a block diagonal structure, which can be inverted easily. We tested our WGM algorithm with several problems to verify the model. The solutions are very accurate with a proper selection of Daubechies' order and dilation order. The solution is very accurate at the interfaces where the radionuclide concentration exhibits very steep gradients. (authors)
NASA Astrophysics Data System (ADS)
Erkyihun, S. T.
2013-12-01
Understanding streamflow variability and the ability to generate realistic scenarios at multi-decadal time scales is important for robust water resources planning and management in any River Basin - more so on the Colorado River Basin with its semi-arid climate and highly stressed water resources It is increasingly evident that large scale climate forcings such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) are known to modulate the Colorado River Basin hydrology at multi-decadal time scales. Thus, modeling these large scale Climate indicators is important to then conditionally modeling the multi-decadal streamflow variability. To this end, we developed a simulation model that combines the wavelet-based time series method, Wavelet Auto Regressive Moving Average (WARMA) with a K-nearest neighbor (K-NN) bootstrap approach. In this, for a given time series (climate forcings), dominant periodicities/frequency bands are identified from the wavelet spectrum that pass the 90% significant test. The time series is filtered at these frequencies in each band to create ';components'; the components are orthogonal and when added to the residual (i.e., noise) results in the original time series. The components, being smooth, are easily modeled using parsimonious Auto Regressive Moving Average (ARMA) time series models. The fitted ARMA models are used to simulate the individual components which are added to obtain simulation of the original series. The WARMA approach is applied to all the climate forcing indicators which are used to simulate multi-decadal sequences of these forcing. For the current year, the simulated forcings are considered the ';feature vector' and K-NN of this are identified; one of the neighbors (i.e., one of the historical year) is resampled using a weighted probability metric (with more weights to nearest neighbor and least to the farthest) and the corresponding streamflow is the
Lagrange wavelets for signal processing.
Shi, Z; Wei, G W; Kouri, D J; Hoffman, D K; Bao, Z
2001-01-01
This paper deals with the design of interpolating wavelets based on a variety of Lagrange functions, combined with novel signal processing techniques for digital imaging. Halfband Lagrange wavelets, B-spline Lagrange wavelets and Gaussian Lagrange (Lagrange distributed approximating functional (DAF)) wavelets are presented as specific examples of the generalized Lagrange wavelets. Our approach combines the perceptually dependent visual group normalization (VGN) technique and a softer logic masking (SLM) method. These are utilized to rescale the wavelet coefficients, remove perceptual redundancy and obtain good visual performance for digital image processing. PMID:18255493
NASA Astrophysics Data System (ADS)
González Gómez, Dulce I.; Moreno Barbosa, E.; Martínez Hernández, Mario Iván; Ramos Méndez, José; Hidalgo Tobón, Silvia; Dies Suarez, Pilar; Barragán Pérez, Eduardo; De Celis Alonso, Benito
2014-11-01
The main goal of this project was to create a computer algorithm based on wavelet analysis of region of homogeneity images obtained during resting state studies. Ideally it would automatically diagnose ADHD. Because the cerebellum is an area known to be affected by ADHD, this study specifically analysed this region. Male right handed volunteers (infants with ages between 7 and 11 years old) were studied and compared with age matched controls. Statistical differences between the values of the absolute integrated wavelet spectrum were found and showed significant differences (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.
González Gómez Dulce, I. E-mail: emoreno@fcfm.buap.mx E-mail: joserm84@gmail.com; Moreno Barbosa, E. E-mail: emoreno@fcfm.buap.mx E-mail: joserm84@gmail.com; Hernández, Mario Iván Martínez E-mail: emoreno@fcfm.buap.mx E-mail: joserm84@gmail.com; Méndez, José Ramos E-mail: emoreno@fcfm.buap.mx E-mail: joserm84@gmail.com; Silvia, Hidalgo Tobón; Pilar, Dies Suarez E-mail: neurodoc@prodigy.net.mx; Eduardo, Barragán Pérez E-mail: neurodoc@prodigy.net.mx; Benito, De Celis Alonso
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.
Blind watermark algorithm on 3D motion model based on wavelet transform
NASA Astrophysics Data System (ADS)
Qi, Hu; Zhai, Lang
2013-12-01
With the continuous development of 3D vision technology, digital watermark technology, as the best choice for copyright protection, has fused with it gradually. This paper proposed a blind watermark plan of 3D motion model based on wavelet transform, and made it loaded into the Vega real-time visual simulation system. Firstly, put 3D model into affine transform, and take the distance from the center of gravity to the vertex of 3D object in order to generate a one-dimensional discrete signal; then make this signal into wavelet transform to change its frequency coefficients and embed watermark, finally generate 3D motion model with watermarking. In fixed affine space, achieve the robustness in translation, revolving and proportion transforms. The results show that this approach has better performances not only in robustness, but also in watermark- invisibility.
Wavelet-based fast time-resolved magnetic sensing with electronic spins in diamond
NASA Astrophysics Data System (ADS)
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.
CHARACTERIZING COMPLEXITY IN SOLAR MAGNETOGRAM DATA USING A WAVELET-BASED SEGMENTATION METHOD
Kestener, P.; Khalil, A.; Arneodo, A.
2010-07-10
The multifractal nature of solar photospheric magnetic structures is studied using the two-dimensional wavelet transform modulus maxima (WTMM) method. This relies on computing partition functions from the wavelet transform skeleton defined by the WTMM method. This skeleton provides an adaptive space-scale partition of the fractal distribution under study, from which one can extract the multifractal singularity spectrum. We describe the implementation of a multiscale image processing segmentation procedure based on the partitioning of the WT skeleton, which allows the disentangling of the information concerning the multifractal properties of active regions from the surrounding quiet-Sun field. The quiet Sun exhibits an average Hoelder exponent {approx}-0.75, with observed multifractal properties due to the supergranular structure. On the other hand, active region multifractal spectra exhibit an average Hoelder exponent {approx}0.38, similar to those found when studying experimental data from turbulent flows.
Detecting laser-range-finding signals in surveying converter lining based on wavelet transform
NASA Astrophysics Data System (ADS)
Li, Hongsheng; Yang, Xiaofei; Shi, Tielin; Yang, Shuzi
1998-08-01
The precision of the laser range finding subsystem has important influences on the performances of the whole measurement system applied to survey the steelmaking converter lining erosion state. In the system, the object of laser beams is some rough lighting surfaces in high temperature. the laser range finding signals to reach the microcomputer system would be submerged in intense disturb environments. Common laser range finding devices could not work normally. This paper presents a method based on the wavelet transform to test solving the problem. The idea of this method includes encoding the measuring signals, decomposing the encoded received signals of components in different frequency scales and time domains by the wavelet transform method, extracting the features of encoded signals according to queer points to confirm the arrival of signals, and accurately calculating out the measured distances. In addition, the method is also helpful to adopt some digital filter algorithms in time. It could make further in improvement on the precision.
Wavelet-based adaptive numerical simulation of unsteady 3D flow around a bluff body
NASA Astrophysics Data System (ADS)
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.
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
Depth migration with Gaussian wave packets based on Poincaré wavelets
NASA Astrophysics Data System (ADS)
Gorodnitskiy, Evgeny; Perel, Maria; Geng, Yu; Wu, Ru-Shan
2016-04-01
An approach to depth migration, based on an integral representation of seismic data, that is, wavefields recorded on the boundary, is presented in terms of Poincaré wavelets. Each wavelet is taken as a boundary datum for a high-frequency asymptotic solution of the wave equation. This solution, which we call the quasiphoton or the Gaussian wave packet, decreases in a Gaussian manner away from a point running along a ray that is launched from the surface. The deformation of the propagating packet is taken into account in the migration algorithm. A numerical example of zero-offset migration with synthetic seismograms calculated for the 2-D SEG/EAGE salt model is presented. The result, which uses only 3.9 per cent of the total number of coefficients, is a satisfactory image, with a threshold of 0.75 per cent.
Wavelet-based Poisson Solver for use in Particle-In-CellSimulations
Terzic, B.; Mihalcea, D.; Bohn, C.L.; Pogorelov, I.V.
2005-05-13
We report on a successful implementation of a wavelet based Poisson solver for use in 3D particle-in-cell (PIC) simulations. One new aspect of our algorithm is its ability to treat the general(inhomogeneous) Dirichlet boundary conditions (BCs). 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 modeling of the Fermilab/NICADD and AES/JLab photoinjectors.
Research on temporal features of LEMP based on Laplace wavelet in time and frequency domain
NASA Astrophysics Data System (ADS)
Li, Qin; Zhong, Jianwei; Ai, Qing; Gao, Shihong
2015-12-01
In this paper, the fine-structures of lightning electromagnetic pulse (LEMP) including 19 pulses in preliminary breakdown, 37 stepped leaders, 8 dart leaders, 73 first return strokes, and 52 subsequent return strokes have been analyzed based on Laplace wavelet. The main characteristics of field waveforms are presented: the correlation coefficient, the dominant frequency, the peak energy and the spread distribution of the power spectrum. The instantaneous field peak pulse can be precisely located by the value of the correlation coefficient. The pulses of preliminary breakdown and leaders are found to radiate in the dominant frequency in the range 100 kHz to 1 MHz. The field radiated by the first return strokes dominantly lies under 100 kHz, whereas the subsequent return strokes under 50 kHz. The statistical results show that the Laplace wavelet is effective and can accurately determine time and frequency of the electromagnetic field of first and subsequent return strokes.
Defective pixel map creation based on wavelet analysis in digital radiography detectors
NASA Astrophysics Data System (ADS)
Park, Chun Joo; Lee, Hyoung Koo; Song, William Y.; Achterkirchen, Thorsten Graeve; Kim, Ho Kyung
2011-04-01
The application of digital radiography detectors has attracted increasing attention in both medicine and industry. Since the imaging detectors are fabricated by semiconductor manufacturing process over large areas, defective pixels in the detectors are unavoidable. Moreover, the radiation damage due to the routine use of the detectors progressively increases the density of defective pixels. In this study, we present a method of identifying defective pixels in digital radiography detectors based on wavelet analysis. Artifacts generated due to wavelet transformations have been prevented by an additional local threshold method. The proposed method was applied to a sample digital radiography and the result was promising. The proposed method uses a single pair of dark and white images and does not require them to be corrected in gain-and-offset properties. This method will be helpful for the reliable use of digital radiography detectors through the working lifetime.
A 64-channel neural signal processor/ compressor based on Haar wavelet transform.
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
NASA Astrophysics Data System (ADS)
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.
Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions.
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
Best tree wavelet packet transform based copyright protection scheme for digital images
NASA Astrophysics Data System (ADS)
Rawat, Sanjay; Raman, Balasubramanian
2012-05-01
In this paper, a dual watermarking scheme based on discrete wavelet transform (DWT), wavelet packet transform (WPT) with best tree, and singular value decomposition (SVD) is proposed. In our algorithm, the cover image is sub-sampled into four sub-images and then two sub-images, having the highest sum of singular values are selected. Two different gray scale images are embedded in the selected sub-images. For embedding first watermark, one of the selected sub-image is decomposed via WPT. The entropy based algorithm is adopted to find the best tree of WPT. Watermark is embedded in all frequency sub-bands of the best tree. For embedding second watermark, l-level discrete wavelet transform (DWT) is performed on the second selected sub-image. The watermark is embedded by modifying the singular values of the transformed image. To enhance the security of the scheme, Zig-Zag scan in applied on the second watermark before embedding. The robustness of the proposed scheme is demonstrated through a series of attack simulations. Experimental results demonstrate that the proposed scheme has good perceptual invisibility and is also robust against various image processing operations, geometric attacks and JPEG Compression.
Wavelet Based Method for Congestive Heart Failure Recognition by Three Confirmation Functions
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
Performance Analysis of Satellite Clock Bias Based on Wavelet Analysis and Neural Network
NASA Astrophysics Data System (ADS)
Guo, C. J.; Teng, Y. L.
2010-10-01
In the field of the real-time GPS precise point positioning (PPP), the real-time and reliable prediction of satellite clock bias (SCB) is one key to realize the real-time GPS PPP with high accuracy. The satellite borne GPS atomic clock has high frequency, is very sensitive and extremely easy to be influenced by the outside world and its own factors. So it is very difficult to master its complicated and detailed law of change. With the above characters, a novel four-stage method for SCB prediction based on wavelet analysis and neural network is proposed. The basic ideas, prediction models and steps of clock bias prediction based on wavelet analysis and radial basis function (RBF) network are discussed, respectively. This model adopts "sliding window" to compartmentalize data and utilizes neural network to prognosticate coefficients of clock bias sequence at each layer after wavelet analysis and wiping off noise. As a result, the intricate and meticulous diversification rule of clock bias sequence is obtained more accurately and the clock bias sequence is better approached. Compared with the grey system model and neural network model, a careful precision analysis of SCB prediction is made to verify the feasibility and validity of this proposed method by using the performance parameters of GPS satellite clocks. The simulation results show that the prediction precision of this novel model is much better. This model can afford the SCB prediction with relatively high precision for real-time GPS PPP.
Wavelet transform-based fault diagnosis and line selection method of small current grounding system
NASA Astrophysics Data System (ADS)
Yang, Ni; Zhang, Shuqing; Zhang, Liguo; Zhang, Kexin; Sun, Lingyun
2008-12-01
Small current grounding system is the system that the neutral point doesn't ground or grounds across the arc suppressing coils, which has been applied commonly in distribution system of many countries. As the grounding fault occurs, current is the one caused by capacity of circuit to ground only and it is rather small. The status of fault is complexity, e.g., the electromagnet interferes together with the amplified impact of zero-order loops to high-order singularity waves and various temporary variables. All these result in the lower ratio of the fault element signal to noise caused by zero-order current. In this paper, the position of signal singularity and the magnitude of the singularity degree are analyzed based on the variable focus character of wavelet, and the time fault occurs is then determined. The series db wavelet with close sustain is adopted, and the line selection is according to the zero-order voltage of the generatrix and the current of various outlet line. It is proved by the experiment that the fault circuit diagnosis method based on wavelet analysis to the character of temporary status of single-phase grounding fault plays an important role to a finer line selection.
A wavelet-based data pre-processing analysis approach in mass spectrometry.
Li, Xiaoli; Li, Jin; Yao, Xin
2007-04-01
Recently, mass spectrometry analysis has a become an effective and rapid approach in detecting early-stage cancer. To identify proteomic patterns in serum to discriminate cancer patients from normal individuals, machine-learning methods, such as feature selection and classification, have already been involved in the analysis of mass spectrometry (MS) data with some success. However, the performance of existing machine learning methods for MS data analysis still needs improving. The study in this paper proposes a wavelet-based pre-processing approach to MS data analysis. The approach applies wavelet-based transforms to MS data with the aim of de-noising the data that are potentially contaminated in acquisition. The effects of the selection of wavelet function and decomposition level on the de-noising performance have also been investigated in this study. Our comparative experimental results demonstrate that the proposed de-noising pre-processing approach has potentials to remove possible noise embedded in MS data, which can lead to improved performance for existing machine learning methods in cancer detection. PMID:16982045
Krug, R; Carballido-Gamio, J; Burghardt, A; Haase, S; Sedat, J W; Moss, W C; Majumdar, S
2005-04-11
Trabecular bone structure and bone density contribute to the strength of bone and are important in the study of osteoporosis. Wavelets are a powerful tool to characterize and quantify texture in an image. In this study the thickness of trabecular bone was analyzed in 8 cylindrical cores of the vertebral spine. Images were obtained from 3 Tesla (T) magnetic resonance imaging (MRI) and micro-computed tomography ({micro}CT). Results from the wavelet based analysis of trabecular bone were compared with standard two-dimensional structural parameters (analogous to bone histomorphometry) obtained using mean intercept length (MR images) and direct 3D distance transformation methods ({micro}CT images). Additionally, the bone volume fraction was determined from MR images. We conclude that the wavelet based analyses delivers comparable results to the established MR histomorphometric measurements. The average deviation in trabecular thickness was less than one pixel size between the wavelet and the standard approach for both MR and {micro}CT analysis. Since the wavelet based method is less sensitive to image noise, we see an advantage of wavelet analysis of trabecular bone for MR imaging when going to higher resolution.
Multiscale seismic characterization of marine sediments by using a wavelet-based approach
NASA Astrophysics Data System (ADS)
Ker, Stephan; Le Gonidec, Yves; Gibert, Dominique
2015-04-01
We propose a wavelet-based method to characterize acoustic impedance discontinuities from a multiscale analysis of reflected seismic waves. This method is developed in the framework of the wavelet response (WR) where dilated wavelets are used to sound a complex seismic reflector defined by a multiscale impedance structure. In the context of seismic imaging, we use the WR as a multiscale seismic attributes, in particular ridge functions which contain most of the information that quantifies the complex geometry of the reflector. We extend this approach by considering its application to analyse seismic data acquired with broadband but frequency limited source signals. The band-pass filter related to such actual sources distort the WR: in order to remove these effects, we develop an original processing based on fractional derivatives of Lévy alpha-stable distributions in the formalism of the continuous wavelet transform (CWT). We demonstrate that the CWT of a seismic trace involving such a finite frequency bandwidth can be made equivalent to the CWT of the impulse response of the subsurface and is defined for a reduced range of dilations, controlled by the seismic source signal. In this dilation range, the multiscale seismic attributes are corrected from distortions and we can thus merge multiresolution seismic sources to increase the frequency range of the mutliscale analysis. As a first demonstration, we perform the source-correction with the high and very high resolution seismic sources of the SYSIF deep-towed seismic device and we show that both can now be perfectly merged into an equivalent seismic source with an improved frequency bandwidth (220-2200 Hz). Such multiresolution seismic data fusion allows reconstructing the acoustic impedance of the subseabed based on the inverse wavelet transform properties extended to the source-corrected WR. We illustrate the potential of this approach with deep-water seismic data acquired during the ERIG3D cruise and we compare
NASA Astrophysics Data System (ADS)
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
A wavelet-based neural model to optimize and read out a temporal population code
Luvizotto, Andre; Rennó-Costa, César; Verschure, Paul F. M. J.
2012-01-01
wavelet-based decoders. PMID:22563314
A multi component wavelet analysis of the B5 molecular cloud
NASA Astrophysics Data System (ADS)
Langer, W. D.; Andersson, B.-G.
1993-12-01
As we have previously shown, the molecular cloud B5 is surrounded by an almost complete HI halo (Andersson, Roger & Wannier, 1992). Here we present a multiscale decomposition of the HI emission associated with the B5 molecular cloud using Laplacian Pyramid Transforms (Langer et al. 1993). We analyze the fractal structure of HI around B5 and derive the global wavelet energy spectrum. We discuss the scale size distribution of the atomic emission and compare it to the distribution in the molecular gas as traced by CO. Andersson, B-G, Roger, R.S. & Wannier, P.G., 1992, A&A 260, 355. Langer, W D, Wilson, R W, & Anderson, C H 1993, Ap. J. Letters, 408, L45.
A new approach to pre-processing digital image for wavelet-based watermark
NASA Astrophysics Data System (ADS)
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.
An efficient and robust 3D mesh compression based on 3D watermarking and wavelet transform
NASA Astrophysics Data System (ADS)
Zagrouba, Ezzeddine; Ben Jabra, Saoussen; Didi, Yosra
2011-06-01
The compression and watermarking of 3D meshes are very important in many areas of activity including digital cinematography, virtual reality as well as CAD design. However, most studies on 3D watermarking and 3D compression are done independently. To verify a good trade-off between protection and a fast transfer of 3D meshes, this paper proposes a new approach which combines 3D mesh compression with mesh watermarking. This combination is based on a wavelet transformation. In fact, the used compression method is decomposed to two stages: geometric encoding and topologic encoding. The proposed approach consists to insert a signature between these two stages. First, the wavelet transformation is applied to the original mesh to obtain two components: wavelets coefficients and a coarse mesh. Then, the geometric encoding is done on these two components. The obtained coarse mesh will be marked using a robust mesh watermarking scheme. This insertion into coarse mesh allows obtaining high robustness to several attacks. Finally, the topologic encoding is applied to the marked coarse mesh to obtain the compressed mesh. The combination of compression and watermarking permits to detect the presence of signature after a compression of the marked mesh. In plus, it allows transferring protected 3D meshes with the minimum size. The experiments and evaluations show that the proposed approach presents efficient results in terms of compression gain, invisibility and robustness of the signature against of many attacks.
A real-time wavelet-based video decoder using SIMD technology
NASA Astrophysics Data System (ADS)
Klepko, Robert; Wang, Demin
2008-02-01
This paper presents a fast implementation of a wavelet-based video codec. The codec consists of motion-compensated temporal filtering (MCTF), 2-D spatial wavelet transform, and SPIHT for wavelet coefficient coding. It offers compression efficiency that is competitive to H.264. The codec is implemented in software running on a general purpose PC, using C programming language and streaming SIMD extensions intrinsics, without assembly language. This high-level software implementation allows the codec to be portable to other general-purpose computing platforms. Testing with a Pentium 4 HT at 3.6GHz (running under Linux and using the GCC compiler, version 4), shows that the software decoder is able to decode 4CIF video in real-time, over 2 times faster than software written only in C language. This paper describes the structure of the codec, the fast algorithms chosen for the most computationally intensive elements in the codec, and the use of SIMD to implement these algorithms.
NASA Astrophysics Data System (ADS)
Harikumar, Rajaguru; Vijayakumar, Thangavel
2014-12-01
The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.
The Parabolic Variance (PVAR): A Wavelet Variance Based on the Least-Square Fit.
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
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
Wavelet-based identification of localized turbulent regions in a transitional boundary layer
NASA Astrophysics Data System (ADS)
Yoshikawa, Joe; Nishio, Yu; Izawa, Seiichiro; Fukunishi, Yu
2014-11-01
A numerical study in order to develop a method to identify localized turbulent regions in a transitional boundary layer is carried out using a wavelet transformation. Finding the onset of turbulence is quite difficult because it is not easy to distinguish the localized turbulent regions from ``non-active'' groups of vortices. The base flow with low-speed streaks is generated by placing an array of obstacles. Then a short duration jet is ejected from the wall into the low-speed streak. First, a hairpin vortex appears in the laminar boundary layer which travels downstream growing up. Downstream, localized turbulent regions appear in the boundary layer, where a lot of vortices are entangled with each other. A wavelet analysis is applied to the spatial waveforms of streamwise velocity fluctuations obtained from these two flow fields. It is shown that the hairpin vortex appears as a high amplitude spot in the wavelet spectrum, which is small in both wavenumber-wise and streamwise scales. On the other hand, the isolated turbulent region appears more wide spread in the wavenumber-wise scale. So, using this method, localized turbulent regions can be identified.
NASA Astrophysics Data System (ADS)
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.
Edge extraction of CT medical image based on wavelet transform algorithm
NASA Astrophysics Data System (ADS)
Wang, Xiaojun; Li, Xinzheng; Lai, Weidong
2011-06-01
Since computer tomography (CT) image has been widely applied in clinic diagnostics, while for many applications the information directly provided by CT images is incomplete corrupted by noise or instrument defect, there has great demand to further the processing methods for improving the CT image quality. Among all image features, the edge profile of clinic focus has obvious influence on accurately translating CT image. In this paper, the wavelet filtering algorithm based on modulus maximum method is put forward to extract and enhance the CT image edges. Edges in the brain lobe CT image can be outlined after wavelet transform, during which the wavelet assigned as the first order derivative of Gauss function. Further manipulation through maximum threshold checking to the modulus have been attenuated the pseudo-edges. After segmented with the original CT image, the edge structure has been distinctly enhanced, and high contrast is achieved between the brain lobe microstructure and the artificially established edges. The proposed algorithm is more efficient than the common first order differential operator, for the latter it even deteriorates the edge features. The algorithm proposed in this article can be integrated in medical image analyzing software to obtain higher accuracy for symptom interpretation.
NASA Astrophysics Data System (ADS)
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
Kim, Won Hwa; Singh, Vikas; Chung, Moo K.; Hinrichs, Chris; Pachauri, Deepti; Okonkwo, Ozioma C.; Johnson, Sterling C.
2014-01-01
Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer’s disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer’s Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer’s Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation. PMID:24614060
NASA Astrophysics Data System (ADS)
Fadili, Jalal M.; Bullmore, Edward T.
2003-11-01
Wavelet-based methods for multiple hypothesis testing are described and their potential for activation mapping of human functional magnetic resonance imaging (fMRI) data is investigated. In this approach, we emphasize convergence between methods of wavelet thresholding or shrinkage and the problem of multiple hypothesis testing in both classical and Bayesian contexts. Specifically, our interest will be focused on ensuring a trade off between type I probability error control and power dissipation. We describe a technique for controlling the false discovery rate at an arbitrary level of type 1 error in testing multiple wavelet coefficients generated by a 2D discrete wavelet transform (DWT) of spatial maps of {fMRI} time series statistics. We also describe and apply recursive testing methods that can be used to define a threshold unique to each level and orientation of the 2D-DWT. Bayesian methods, incorporating a formal model for the anticipated sparseness of wavelet coefficients representing the signal or true image, are also tractable. These methods are comparatively evaluated by analysis of "null" images (acquired with the subject at rest), in which case the number of positive tests should be exactly as predicted under the hull hypothesis, and an experimental dataset acquired from 5 normal volunteers during an event-related finger movement task. We show that all three wavelet-based methods of multiple hypothesis testing have good type 1 error control (the FDR method being most conservative) and generate plausible brain activation maps.
NASA Astrophysics Data System (ADS)
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.
Hybrid-Thresholding based Image Super-Resolution Technique by the use of Triplet Half-Band Wavelets
NASA Astrophysics Data System (ADS)
Chopade, Pravin B.; Rahulkar, Amol D.; Patil, Pradeep M.
2016-06-01
This paper presents a modified image super-resolution scheme based on the wavelet coefficients hybrid-thresholding by the use of triplet half-band wavelets (THW) derived from the generalized half-band polynomial. At first, discrete wavelet transform (DWT) is obtained from triplet half-band kernels and it applied on the low-resolution image to obtain the high frequency sub-bands. These high frequency sub-bands and the original low-resolution image are interpolated to enhance the resolution. Second, stationary wavelet transform is obtained by using THW, which is employed to minimize the loss due to the use of DWT. In addition, hybrid thresholding scheme on wavelet coefficients scheme is proposed on these estimated high-frequency sub-bands in order to reduce the spatial domain noise. These sub-bands are combined together by inverse discrete wavelet transform obtained from THW to generate a high-resolution image. The proposed approach is validated by comparing the quality metrics with existing filter banks and well-known super-resolution scheme.
The Brera multi-scale wavelet Chandra survey. I. Serendipitous source catalogue
NASA Astrophysics Data System (ADS)
Romano, P.; Campana, S.; Mignani, R. P.; Moretti, A.; Mottini, M.; Panzera, M. R.; Tagliaferri, G.
2008-09-01
We present the BMW-Chandra source catalogue drawn from essentially all Chandra ACIS-I pointed observations with an exposure time in excess of 10 ks 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 21 325 sources. Among them, 16 758 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 ˜3× 10-16 to 9×10-12 erg cm-2 s-1 with a median of 7× 10-15 erg cm-2 s-1. The catalogue consists of count rates and relative errors in three energy bands (total, 0.5-7 keV; soft, 0.5-2 keV; and hard, 2-7 keV), and source positions relative to the highest signal-to-noise detection among the three bands. The wavelet algorithm also provides an estimate of the extension of the source. We include information drawn from the headers of the original files, as well, and extracted source counts in four additional energy bands, SB1 (0.5-1 keV), SB2 (1-2 keV), HB1 (2-4 keV), and HB2 (4-7 keV). We computed the sky coverage for the full catalogue and for a subset at high Galactic latitude (mid b mid > 20°). The complete catalogue provides a sky coverage in the soft band (0.5-2 keV, S/N =3) of 8 deg2 at a limiting flux of ˜10-13 erg cm-2 s-1, and 2 deg2 at a limiting flux of ˜10-15 erg cm-2 s-1. Furthermore, we present the results of the cross-match with existing catalogues at different wavelengths (FIRST, IRAS, 2MASS, GSC2, and ChaMP). The total numbers of matches with the FIRST, IRASPSC, 2MASS, and GSC2 catalogues obtained after a closest-distance selection are 13, 87, 6700, and 4485, respectively. The catalogue is only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130
The numerical performance of wavelets and reproducing kernels for PDE`s
Christon, M.A.; Roach, D.W.; Voth, T.E.
1998-08-01
The results presented here constitute a brief summary of an on-going multi-year effort to investigate hierarchical/wavelet bases for solving PDE`s and establish a rigorous foundation for these methods. A new, hierarchical, wavelet-Galerkin solution strategy based upon the Donovan-Geronimo-Hardin-Massopust (DGHM) compactly-supported multi-wavelet is presented for elliptic partial differential equations. This multi-scale wavelet-Galerkin method uses a wavelet transform to yield nearly mesh independent condition numbers for elliptic problems as opposed to the multi-scaling functions that yield condition numbers which increase as the square of the mesh size. In addition, the results of von Neumann analyses for the DGHM multi-wavelet element and the Reproducing Kernel Particle Method (RKPM) are presented for model hyperbolic partial differential equations. RKPM exhibits excellent dispersion characteristics using a consistent mass matrix with the proper choice of refinement parameter and mass matrix formulation. In comparison, the wavelet-Galerkin formulation using the DGHM element delivers a frequency response comparable to a Bubnov-Galerkin formulation with a quadratic element.
NASA Technical Reports Server (NTRS)
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.
Rotation and Scale Invariant Wavelet Feature for Content-Based Texture Image Retrieval.
ERIC Educational Resources Information Center
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…
Volumetric Rendering of Geophysical Data on Adaptive Wavelet Grid
NASA Astrophysics Data System (ADS)
Vezolainen, A.; Erlebacher, G.; Vasilyev, O.; Yuen, D. A.
2005-12-01
Numerical modeling of geological phenomena frequently involves processes across a wide range of spatial and temporal scales. In the last several years, transport phenomena governed by the Navier-Stokes equations have been simulated in wavelet space using second generation wavelets [1], and most recently on fully adaptive meshes. Our objective is to visualize this time-dependent data using volume rendering while capitalizing on the available sparse data representation. We present a technique for volumetric ray casting of multi-scale datasets in wavelet space. Rather of working with the wavelets at the finest possible resolution, we perform a partial inverse wavelet transform as a preprocessing step to obtain scaling functions on a uniform grid at a user-prescribed resolution. As a result, a function in physical space is represented by a superposition of scaling functions on a coarse regular grid and wavelets on an adaptive mesh. An efficient and accurate ray casting algorithm is based just on these scaling functions. Additional detail is added during the ray tracing by taking an appropriate number of wavelets into account based on support overlap with the interpolation point, wavelet amplitude, and other characteristics, such as opacity accumulation (front to back ordering) and deviation from frontal viewing direction. Strategies for hardware implementation will be presented if available, inspired by the work in [2]. We will pressent error measures as a function of the number of scaling and wavelet functions used for interpolation. Data from mantle convection will be used to illustrate the method. [1] Vasilyev, O.V. and Bowman, C., Second Generation Wavelet Collocation Method for the Solution of Partial Differential Equations. J. Comp. Phys., 165, pp. 660-693, 2000. [2] Guthe, S., Wand, M., Gonser, J., and Straßer, W. Interactive rendering of large volume data sets. In Proceedings of the Conference on Visualization '02 (Boston, Massachusetts, October 27 - November
Nie, Xinhua; Pan, Zhongming; Zhang, Dasha; Zhou, Han; Chen, Min; Zhang, Wenna
2014-01-01
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. PMID:25343484
G. S., Vijay; H. S., Kumar; Pai P., Srinivasa; N. S., Sriram; Rao, Raj B. K. N.
2012-01-01
The wavelet based denoising has proven its ability to denoise the bearing vibration signals by improving the signal-to-noise ratio (SNR) and reducing the root-mean-square error (RMSE). In this paper seven wavelet based denoising schemes have been evaluated based on the performance of the Artificial Neural Network (ANN) and the Support Vector Machine (SVM), for the bearing condition classification. The work consists of two parts, the first part in which a synthetic signal simulating the defective bearing vibration signal with Gaussian noise was subjected to these denoising schemes. The best scheme based on the SNR and the RMSE was identified. In the second part, the vibration signals collected from a customized Rolling Element Bearing (REB) test rig for four bearing conditions were subjected to these denoising schemes. Several time and frequency domain features were extracted from the denoised signals, out of which a few sensitive features were selected using the Fisher's Criterion (FC). Extracted features were used to train and test the ANN and the SVM. The best denoising scheme identified, based on the classification performances of the ANN and the SVM, was found to be the same as the one obtained using the synthetic signal. PMID:23213323
Parameter estimation of analog circuits based on the fractional wavelet method
NASA Astrophysics Data System (ADS)
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).
Lancia, Leonardo; Rausch, Philip; Morris, Jeffrey S
2015-02-01
This paper illustrates the application of wavelet-based functional mixed models to automatic quantification of differences between tongue contours obtained through ultrasound imaging. The reliability of this method is demonstrated through the analysis of tongue positions recorded from a female and a male speaker at the onset of the vowels /a/ and /i/ produced in the context of the consonants /t/ and /k/. The proposed method allows detection of significant differences between configurations of the articulators that are visible in ultrasound images during the production of different speech gestures and is compatible with statistical designs containing both fixed and random terms. PMID:25698047
Wavelet-based ECG compression by bit-field preserving and running length encoding.
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
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Li, Chun-Feng
2004-07-01
Traditional seismic interpretation of surface seismic data is focused primarily on seismic oscillation. Rich singularity information carried by, but deeply buried in, seismic data is often ignored. We show that wavelet-based singularity analysis reveals generic singularity information conducted from acoustic impedance to seismogram. The singularity exponents (known as Hölder exponent α) calculated from seismic data are independent of amplitude and robust to phase changes and noises. These unique properties of α offer potentially important application in many fields, especially in studying seismic data interpretation, processing, inversion, and wave attenuation.
A novel 3D wavelet based filter for visualizing features in noisy biological data
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.
Gabor-wavelet decomposition and integrated PCA-FLD method for texture based defect classification
NASA Astrophysics Data System (ADS)
Cheng, Xuemei; Chen, Yud-Ren; Yang, Tao; Chen, Xin
2005-11-01
In many hyperspectral applications, it is desirable to extract the texture features for pattern classification. Texture refers to replications, symmetry of certain patterns. In a set of hyperspectral images, the differences of image textures often imply changes in the physical and chemical properties on or underneath the surface. In this paper, we utilize Gabor wavelet based texture analysis method for textural pattern extraction, and combined with integrated PCA-FLD method for hyperspectral band selection in the application of classifying chilling damaged cucumbers from normal ones. The classification performances are compared and analyzed.
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
NASA Astrophysics Data System (ADS)
Xu, Chuang; Chen, Liang; Liu, Xi-kai
2016-04-01
Urban faults in Shenzhen are potential threat to the city security and sustainable development. To improve the knowledge of the Shenzhen fault zone, interpretation and inversion of gravity data were carried out. Bouguer gravity covering the whole Shenzhen city was calculated with a resolution of 1kmx1km. Wavelet multi-scale analysis (MSA) was applied to the Bouguer gravity data to obtain the multilayer residual anomalies corresponding to different depths. In addition, 2D gravity models were constructed along three profiles. The Bouguer gravity anomaly shows a NE-striking high-low-high pattern from northwest to southeast, strongly related to the main faults. According to the result of MSA, the correlation between gravity anomaly and faults is particularly significant from 4 to 12 km depth. The residual gravity with small amplitude in each layer indicates weak tectonic activity in the crust. In the upper layers, positive anomalies along most of faults reveal the upwelling of high-density materials during the past tectonic movements. The multilayer residual anomalies also implicate important information about the faults, such as the vertical extension and the dip direction. The maximum depth of the faults is about 20km. In general, NE-striking faults extend deeper than NW-striking Faults and have a larger dip angle. This study is supported by the National Natural Science Foundation of China (Grant No.41504015) and China Postdoctoral Science Foundation (Grant No.2015M572146).
NASA Astrophysics Data System (ADS)
Rastigejev, Y.; Semakin, A. N.
2013-12-01
Accurate numerical simulations of global scale three-dimensional atmospheric chemical transport models (CTMs) are essential for studies of many important atmospheric chemistry problems such as adverse effect of air pollutants on human health, ecosystems and the Earth's climate. These simulations usually require large CPU time due to numerical difficulties associated with a wide range of spatial and temporal scales, nonlinearity and large number of reacting species. In our previous work we have shown that in order to achieve adequate convergence rate and accuracy, the mesh spacing in numerical simulation of global synoptic-scale pollution plume transport must be decreased to a few kilometers. This resolution is difficult to achieve for global CTMs on uniform or quasi-uniform grids. To address the described above difficulty we developed a three-dimensional Wavelet-based Adaptive Mesh Refinement (WAMR) algorithm. The method employs a highly non-uniform adaptive grid with fine resolution over the areas of interest without requiring small grid-spacing throughout the entire domain. The method uses multi-grid iterative solver that naturally takes advantage of a multilevel structure of the adaptive grid. In order to represent the multilevel adaptive grid efficiently, a dynamic data structure based on indirect memory addressing has been developed. The data structure allows rapid access to individual points, fast inter-grid operations and re-gridding. The WAMR method has been implemented on parallel computer architectures. The parallel algorithm is based on run-time partitioning and load-balancing scheme for the adaptive grid. The partitioning scheme maintains locality to reduce communications between computing nodes. The parallel scheme was found to be cost-effective. Specifically we obtained an order of magnitude increase in computational speed for numerical simulations performed on a twelve-core single processor workstation. We have applied the WAMR method for numerical
Ning, J. G.; Chu, L.; Ren, H. L.
2014-08-28
We base a quantitative acoustic emission (AE) study on fracture processes in alumina ceramics on wavelet packet decomposition and AE source location. According to the frequency characteristics, as well as energy and ringdown counts of AE, the fracture process is divided into four stages: crack closure, nucleation, development, and critical failure. Each of the AE signals is decomposed by a 2-level wavelet package decomposition into four different (from-low-to-high) frequency bands (AA{sub 2}, AD{sub 2}, DA{sub 2}, and DD{sub 2}). The energy eigenvalues P{sub 0}, P{sub 1}, P{sub 2}, and P{sub 3} corresponding to these four frequency bands are calculated. By analyzing changes in P{sub 0} and P{sub 3} in the four stages, we determine the inverse relationship between AE frequency and the crack source size during ceramic fracture. AE signals with regard to crack nucleation can be expressed when P{sub 0} is less than 5 and P{sub 3} more than 60; whereas AE signals with regard to dangerous crack propagation can be expressed when more than 92% of P{sub 0} is greater than 4, and more than 95% of P{sub 3} is less than 45. Geiger location algorithm is used to locate AE sources and cracks in the sample. The results of this location algorithm are consistent with the positions of fractures in the sample when observed under a scanning electronic microscope; thus the locations of fractures located with Geiger's method can reflect the fracture process. The stage division by location results is in a good agreement with the division based on AE frequency characteristics. We find that both wavelet package decomposition and Geiger's AE source locations are suitable for the identification of the evolutionary process of cracks in alumina ceramics.
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
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
Content-based video indexing and searching with wavelet transformation
NASA Astrophysics Data System (ADS)
Stumpf, Florian; Al-Jawad, Naseer; Du, Hongbo; Jassim, Sabah
2006-05-01
Biometric databases form an essential tool in the fight against international terrorism, organised crime and fraud. Various government and law enforcement agencies have their own biometric databases consisting of combination of fingerprints, Iris codes, face images/videos and speech records for an increasing number of persons. In many cases personal data linked to biometric records are incomplete and/or inaccurate. Besides, biometric data in different databases for the same individual may be recorded with different personal details. Following the recent terrorist atrocities, law enforcing agencies collaborate more than before and have greater reliance on database sharing. In such an environment, reliable biometric-based identification must not only determine who you are but also who else you are. In this paper we propose a compact content-based video signature and indexing scheme that can facilitate retrieval of multiple records in face biometric databases that belong to the same person even if their associated personal data are inconsistent. We shall assess the performance of our system using a benchmark audio visual face biometric database that has multiple videos for each subject but with different identity claims. We shall demonstrate that retrieval of relatively small number of videos that are nearest, in terms of the proposed index, to any video in the database results in significant proportion of that individual biometric data.
Goffin, Mark A.; Buchan, Andrew G.; Dargaville, Steven; Pain, Christopher C.; Smith, Paul N.; Smedley-Stevenson, Richard P.
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.
NASA Astrophysics Data System (ADS)
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.
Seshadrinath, Jeevanand; Singh, Bhim; Panigrahi, Bijaya Ketan
2014-05-01
Interturn fault diagnosis of induction machines has been discussed using various neural network-based techniques. The main challenge in such methods is the computational complexity due to the huge size of the network, and in pruning a large number of parameters. In this paper, a nearly shift insensitive complex wavelet-based probabilistic neural network (PNN) model, which has only a single parameter to be optimized, is proposed for interturn fault detection. The algorithm constitutes two parts and runs in an iterative way. In the first part, the PNN structure determination has been discussed, which finds out the optimum size of the network using an orthogonal least squares regression algorithm, thereby reducing its size. In the second part, a Bayesian classifier fusion has been recommended as an effective solution for deciding the machine condition. The testing accuracy, sensitivity, and specificity values are highest for the product rule-based fusion scheme, which is obtained under load, supply, and frequency variations. The point of overfitting of PNN is determined, which reduces the size, without compromising the performance. Moreover, a comparative evaluation with traditional discrete wavelet transform-based method is demonstrated for performance evaluation and to appreciate the obtained results. PMID:24808044
Liu, Runna; Xu, Shanshan; Hu, Hong; Huo, Rui; Wang, Supin; Wan, Mingxi
2016-08-01
Cavitation detection and imaging are essential for monitoring high-intensity focused ultrasound (HIFU) therapies. In this paper, an active cavitation imaging method based on wavelet transform is proposed to enhance the contrast between the cavitation bubbles and surrounding tissues. The Yang-Church model, which is a combination of the Keller-Miksis equation with the Kelvin-Voigt equation for the pulsations of gas bubbles in simple linear viscoelastic solids, is utilized to construct the bubble wavelet. Experiments with porcine muscles demonstrate that image quality is associated with the initial radius of the bubble wavelet and the scale. Moreover, the Yang-Church model achieves a somewhat better performance compared with the Rayleigh-Plesset-Noltingk-Neppiras-Poritsky model. Furthermore, the pulse inversion (PI) technique is combined with bubble wavelet transform to achieve further improvement. The cavitation-to-tissue ratio (CTR) of the best tissue bubble wavelet transform (TBWT) mode image is improved by 5.1 dB compared with that of the B-mode image, while the CTR of the best PI-based TBWT mode image is improved by 7.9 dB compared with that of the PI-based B-mode image. This work will be useful for better monitoring of cavitation in HIFU-induced therapies. PMID:27586712
Multiresolution With Super-Compact Wavelets
NASA Technical Reports Server (NTRS)
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
Wavelet-Based Real-Time Diagnosis of Complex Systems
NASA Technical Reports Server (NTRS)
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.
Wavelets based algorithm for the evaluation of enhanced liver areas
NASA Astrophysics Data System (ADS)
Alvarez, Matheus; Rodrigues de Pina, Diana; Giacomini, Guilherme; Gomes Romeiro, Fernando; Barbosa Duarte, Sérgio; Yamashita, Seizo; de Arruda Miranda, José Ricardo
2014-03-01
Hepatocellular carcinoma (HCC) is a primary tumor of the liver. After local therapies, the tumor evaluation is based on the mRECIST criteria, which involves the measurement of the maximum diameter of the viable lesion. This paper describes a computed methodology to measure through the contrasted area of the lesions the maximum diameter of the tumor by a computational algorithm. 63 computed tomography (CT) slices from 23 patients were assessed. Noncontrasted liver and HCC typical nodules were evaluated, and a virtual phantom was developed for this purpose. Optimization of the algorithm detection and quantification was made using the virtual phantom. After that, we compared the algorithm findings of maximum diameter of the target lesions against radiologist measures. Computed results of the maximum diameter are in good agreement with the results obtained by radiologist evaluation, indicating that the algorithm was able to detect properly the tumor limits. A comparison of the estimated maximum diameter by radiologist versus the algorithm revealed differences on the order of 0.25 cm for large-sized tumors (diameter > 5 cm), whereas agreement lesser than 1.0cm was found for small-sized tumors. Differences between algorithm and radiologist measures were accurate for small-sized tumors with a trend to a small increase for tumors greater than 5 cm. Therefore, traditional methods for measuring lesion diameter should be complemented with non-subjective measurement methods, which would allow a more correct evaluation of the contrast-enhanced areas of HCC according to the mRECIST criteria.
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
NASA Astrophysics Data System (ADS)
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.
Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu
2014-01-01
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. PMID:24473281
Li, Yong; Wang, Xiufeng; Lin, Jing; Shi, Shengyu
2014-01-01
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. PMID:24473281
Suganthi, S S; Ramakrishnan, S
2013-01-01
Transform-based spatial analyses of medical Infrared (IR) images are found to be useful to extract local information, which can be used to identify the abnormalities associated with in region of interest. In this work, human hand infrared images are analyzed by extracting local spatial features using wavelet transform method. The images for this study were acquired using uncooled micro bolometer with focal plane array technology based medical IR camera with dedicated software having high array resolution and spectral response under controlled protocol. The acquired images were decomposed into Intrinsic Mode Functions (IMFs) using bidimensional empirical mode decomposition. Extrema points were detected using eight connected neighbor window method and interpolated using thin plate spline interpolation technique to generate IMFs. The edge information were extracted from local phase of the first IMF. Edges were detected using phase congruency measure by applying Gabor function based wavelet transform. The results showed that it was possible to detect edges from only the first IMF without being influenced by other IMFs. It was further observed that the edge intermittence that arises due to noise component was reduced by treating images with local phase distributions. Hence, it appears that the edge information extraction could enhance the diagnostic relevance of thermal image analysis. PMID:23686173
NASA Astrophysics Data System (ADS)
Qian, Jinfang; Zhang, Changjiang
2014-11-01
An efficient algorithm based on continuous wavelet transform combining with pre-knowledge, which can be used to detect the defect of glass bottle mouth, is proposed. Firstly, under the condition of ball integral light source, a perfect glass bottle mouth image is obtained by Japanese Computar camera through the interface of IEEE-1394b. A single threshold method based on gray level histogram is used to obtain the binary image of the glass bottle mouth. In order to efficiently suppress noise, moving average filter is employed to smooth the histogram of original glass bottle mouth image. And then continuous wavelet transform is done to accurately determine the segmentation threshold. Mathematical morphology operations are used to get normal binary bottle mouth mask. A glass bottle to be detected is moving to the detection zone by conveyor belt. Both bottle mouth image and binary image are obtained by above method. The binary image is multiplied with normal bottle mask and a region of interest is got. Four parameters (number of connected regions, coordinate of centroid position, diameter of inner cycle, and area of annular region) can be computed based on the region of interest. Glass bottle mouth detection rules are designed by above four parameters so as to accurately detect and identify the defect conditions of glass bottle. Finally, the glass bottles of Coca-Cola Company are used to verify the proposed algorithm. The experimental results show that the proposed algorithm can accurately detect the defect conditions of the glass bottles and have 98% detecting accuracy.
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
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.
A wavelet based method for automatic detection of slow eye movements: a pilot study.
Magosso, Elisa; Provini, Federica; Montagna, Pasquale; Ursino, Mauro
2006-11-01
Electro-oculographic (EOG) activity during the wake-sleep transition is characterized by the appearance of slow eye movements (SEM). The present work describes an algorithm for the automatic localisation of SEM events from EOG recordings. The algorithm is based on a wavelet multiresolution analysis of the difference between right and left EOG tracings, and includes three main steps: (i) wavelet decomposition down to 10 detail levels (i.e., 10 scales), using Daubechies order 4 wavelet; (ii) computation of energy in 0.5s time steps at any level of decomposition; (iii) construction of a non-linear discriminant function expressing the relative energy of high-scale details to both high- and low-scale details. The main assumption is that the value of the discriminant function increases above a given threshold during SEM episodes due to energy redistribution toward higher scales. Ten EOG recordings from ten male patients with obstructive sleep apnea syndrome were used. All tracings included a period from pre-sleep wakefulness to stage 2 sleep. Two experts inspected the tracings separately to score SEMs. A reference set of SEM (gold standard) were obtained by joint examination by both experts. Parameters of the discriminant function were assigned on three tracings (design set) to minimize the disagreement between the system classification and classification by the two experts; the algorithm was then tested on the remaining seven tracings (test set). Results show that the agreement between the algorithm and the gold standard was 80.44+/-4.09%, the sensitivity of the algorithm was 67.2+/-7.37% and the selectivity 83.93+/-8.65%. However, most errors were not caused by an inability of the system to detect intervals with SEM activity against NON-SEM intervals, but were due to a different localisation of the beginning and end of some SEM episodes. The proposed method may be a valuable tool for computerized EOG analysis. PMID:16497535
Wavelet-based multiresolution with n-th-root-of-2 Subdivision
Linsen, L; Pascucci, V; Duchaineau, M A; Hamann, B; Joy, K I
2004-12-16
Multiresolution methods are a common technique used for dealing with large-scale data and representing it at multiple levels of detail. The authors present a multiresolution hierarchy construction based on n{radical}2 subdivision, which has all the advantages of a regular data organization scheme while reducing the drawback of coarse granularity. The n{radical}2-subdivision scheme only doubles the number of vertices in each subdivision step regardless of dimension n. They describe the construction of 2D, 3D, and 4D hierarchies representing surfaces, volume data, and time-varying volume data, respectively. The 4D approach supports spatial and temporal scalability. For high-quality data approximation on each level of detail, they use downsampling filters based on n-variate B-spline wavelets. They present a B-spline wavelet lifting scheme for n{radical}2-subdivision steps to obtain small or narrow filters. Narrow filters support adaptive refinement and out-of-core data exploration techniques.
Improving wavelet denoising based on an in-depth analysis of the camera color processing
NASA Astrophysics Data System (ADS)
Seybold, Tamara; Plichta, Mathias; Stechele, Walter
2015-02-01
While Denoising is an extensively studied task in signal processing research, most denoising methods are designed and evaluated using readily processed image data, e.g. the well-known Kodak data set. The noise model is usually additive white Gaussian noise (AWGN). This kind of test data does not correspond to nowadays real-world image data taken with a digital camera. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or suboptimal choices in research on real-time camera denoising algorithms. In this paper we derive a precise analysis of the noise characteristics for the different steps in the color processing. Based on real camera noise measurements and simulation of the processing steps, we obtain a good approximation for the noise characteristics. We further show how this approximation can be used in standard wavelet denoising methods. We improve the wavelet hard thresholding and bivariate thresholding based on our noise analysis results. Both the visual quality and objective quality metrics show the advantage of the proposed method. As the method is implemented using look-up-tables that are calculated before the denoising step, our method can be implemented with very low computational complexity and can process HD video sequences real-time in an FPGA.
Application of wavelet-based neural network on DNA microarray data.
Lee, Jack; Zee, Benny
2008-01-01
The advantage of using DNA microarray data when investigating human cancer gene expressions is its ability to generate enormous amount of information from a single assay in order to speed up the scientific evaluation process. The number of variables from the gene expression data coupled with comparably much less number of samples creates new challenges to scientists and statisticians. In particular, the problems include enormous degree of collinearity among genes expressions, likely violation of model assumptions as well as high level of noise with potential outliers. To deal with these problems, we propose a block wavelet shrinkage principal component (BWSPCA) analysis method to optimize the information during the noise reduction process. This paper firstly uses the National Cancer Institute database (NC160) as an illustration and shows a significant improvement in dimension reduction. Secondly we combine BWSPCA with an artificial neural network-based gene minimization strategy to establish a Block Wavelet-based Neural Network model in a robust and accurate cancer classification process (BWNN). Our extensive experiments on six public cancer datasets have shown that the method of BWNN for tumor classification performed well, especially on some difficult instances with large-class (more than two) expression data. This proposed method is extremely useful for data denoising and is competitiveness with respect to other methods such as BagBoost, RandomForest (RanFor), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN). PMID:19255638
Li, Qiyue; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Lai, Zongying; Ye, Jing; Chen, Zhong
2015-06-01
Compressed sensing MRI (CS-MRI) is a promising technology to accelerate magnetic resonance imaging. Both improving the image quality and reducing the computation time are important for this technology. Recently, a patch-based directional wavelet (PBDW) has been applied in CS-MRI to improve edge reconstruction. However, this method is time consuming since it involves extensive computations, including geometric direction estimation and numerous iterations of wavelet transform. To accelerate computations of PBDW, we propose a general parallelization of patch-based processing by taking the advantage of multicore processors. Additionally, two pertinent optimizations, excluding smooth patches and pre-arranged insertion sort, that make use of sparsity in MR images are also proposed. Simulation results demonstrate that the acceleration factor with the parallel architecture of PBDW approaches the number of central processing unit cores, and that pertinent optimizations are also effective to make further accelerations. The proposed approaches allow compressed sensing MRI reconstruction to be accomplished within several seconds. PMID:25620521
NASA Astrophysics Data System (ADS)
Soumia, Sid Ahmed; Messali, Zoubeida; Ouahabi, Abdeldjalil; Trepout, Sylvain; Messaoudi, Cedric; Marco, Sergio
2015-01-01
The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the
Soumia, Sid Ahmed; Messali, Zoubeida; Ouahabi, Abdeldjalil; Trepout, Sylvain E-mail: cedric.messaoudi@curie.fr Messaoudi, Cedric E-mail: cedric.messaoudi@curie.fr Marco, Sergio E-mail: cedric.messaoudi@curie.fr
2015-01-13
The 3D reconstruction of the Cryo-Transmission Electron Microscopy (Cryo-TEM) and Energy Filtering TEM images (EFTEM) hampered by the noisy nature of these images, so that their alignment becomes so difficult. This noise refers to the collision between the frozen hydrated biological samples and the electrons beam, where the specimen is exposed to the radiation with a high exposure time. This sensitivity to the electrons beam led specialists to obtain the specimen projection images at very low exposure time, which resulting the emergence of a new problem, an extremely low signal-to-noise ratio (SNR). This paper investigates the problem of TEM images denoising when they are acquired at very low exposure time. So, our main objective is to enhance the quality of TEM images to improve the alignment process which will in turn improve the three dimensional tomography reconstructions. We have done multiple tests on special TEM images acquired at different exposure time 0.5s, 0.2s, 0.1s and 1s (i.e. with different values of SNR)) and equipped by Golding beads for helping us in the assessment step. We herein, propose a structure to combine multiple noisy copies of the TEM images. The structure is based on four different denoising methods, to combine the multiple noisy TEM images copies. Namely, the four different methods are Soft, the Hard as Wavelet-Thresholding methods, Bilateral Filter as a non-linear technique able to maintain the edges neatly, and the Bayesian approach in the wavelet domain, in which context modeling is used to estimate the parameter for each coefficient. To ensure getting a high signal-to-noise ratio, we have guaranteed that we are using the appropriate wavelet family at the appropriate level. So we have chosen âĂIJsym8âĂİ wavelet at level 3 as the most appropriate parameter. Whereas, for the bilateral filtering many tests are done in order to determine the proper filter parameters represented by the size of the filter, the range parameter and the
Wavelet decomposition based principal component analysis for face recognition using MATLAB
NASA Astrophysics Data System (ADS)
Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish
2016-03-01
For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.
Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study
Sappa, Angel D.; Carvajal, Juan A.; Aguilera, Cristhian A.; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X.
2016-01-01
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). PMID:27294938
Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.
Sappa, Angel D; Carvajal, Juan A; Aguilera, Cristhian A; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X
2016-01-01
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). PMID:27294938
Wavelet multiresolution based multifractal analysis of electric fields by lightning return strokes
NASA Astrophysics Data System (ADS)
Gou, Xueqiang; Chen, Mingli; Zhang, Yijun; Dong, Wansheng; Qie, Xiushu
2009-02-01
Lightning can be seen as a large-scale cooperative phenomenon, which may evolve in a self-similar cascaded way. Using the electric field waveforms recorded by the slow antenna system, the mono- and multifractal behaviors of 115 first return strokes in negative cloud-to-ground discharges have been investigated with a wavelet multiresolution based multifractal method. The results show that the return stroke process, in term of its electric field waveform, has apparent fractality and strong degree of multifractality. The multifractal spectra obtained for the 115 cases are all well fitted to a modified version of the binomial cascade multifractal model. The width of the multifractal spectra, which measure the strength of multifractality, is 1.6 on average. The fractal dimension of the electric field waveforms ranges from 1.2 to 1.5 with an average of 1.3, a similar value to the fractal dimension of the lightning channel obtained by others. This suggests that the lightning-produced electric fields may have the same fractal dimension as its channel. The relationship between the peak current of a return stroke and the charge deposition in its channel is also discussed. The results suggest that the wavelet and scaling analysis may be a powerful tool in interpretation of the lightning-produced electric fields and therefore in understanding lightning.
Ibaida, Ayman; Khalil, Ibrahim
2013-12-01
With the growing number of aging population and a significant portion of that suffering from cardiac diseases, it is conceivable that remote ECG patient monitoring systems are expected to be widely used as point-of-care (PoC) applications in hospitals around the world. Therefore, huge amount of ECG signal collected by body sensor networks from remote patients at homes will be transmitted along with other physiological readings such as blood pressure, temperature, glucose level, etc., and diagnosed by those remote patient monitoring systems. It is utterly important that patient confidentiality is protected while data are being transmitted over the public network as well as when they are stored in hospital servers used by remote monitoring systems. In this paper, a wavelet-based steganography technique has been introduced which combines encryption and scrambling technique to protect patient confidential data. The proposed method allows ECG signal to hide its corresponding patient confidential data and other physiological information thus guaranteeing the integration between ECG and the rest. To evaluate the effectiveness of the proposed technique on the ECG signal, two distortion measurement metrics have been used: the percentage residual difference and the wavelet weighted PRD. It is found that the proposed technique provides high-security protection for patients data with low (less than 1%) distortion and ECG data remain diagnosable after watermarking (i.e., hiding patient confidential data) and as well as after watermarks (i.e., hidden data) are removed from the watermarked data. PMID:23708767
A clustering-based fuzzy wavelet neural network model for short-term load forecasting.
Kodogiannis, Vassilis S; Amina, Mahdi; Petrounias, Ilias
2013-10-01
Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models. PMID:23924415
Keissar, K; Maestri, R; Pinna, G D; La Rovere, M T; Gilad, O
2010-07-01
A novel approach for the estimation of baroreflex sensitivity (BRS) is introduced based on time-frequency analysis of the transfer function (TF). The TF method (TF-BRS) is a well-established non-invasive technique which assumes stationarity. This condition is difficult to meet, especially in cardiac patients. In this study, the classical TF was replaced with a wavelet transfer function (WTF) and the classical coherence was replaced with wavelet transform coherence (WTC), adding the time domain as an additional degree of freedom with dynamic error estimation. Error analysis and comparison between WTF-BRS and TF-BRS were performed using simulated signals with known transfer function and added noise. Similar comparisons were performed for ECG and blood pressure signals, in the supine position, of 19 normal subjects, 44 patients with a history of previous myocardial infarction (MI) and 45 patients with chronic heart failure. This yielded an excellent linear association (R > 0.94, p < 0.001) for time-averaged WTF-BRS, validating the new method as consistent with a known method. The additional advantage of dynamic analysis of coherence and TF estimates was illustrated in two physiological examples of supine rest and change of posture showing the evolution of BRS synchronized with its error estimations and sympathovagal balance. PMID:20585147
Damage Detection on Sudden Stiffness Reduction Based on Discrete Wavelet Transform
Chen, Bo; Chen, Zhi-wei; Wang, Gan-jun; Xie, Wei-ping
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 detail coefficients of the wavelet transform at damage instant is linearly proportional to the magnitude of the stiffness reduction. A new damage index is proposed and implemented to detect the damage time instant, location, and severity of a structure due to a sudden change of structural stiffness. Numerical simulation using a five-story shear building under different types of excitation is carried out to assess the effectiveness and reliability of the proposed damage index for the building at different damage levels. The sensitivity of the damage index to the intensity and frequency range of measurement noise is also investigated. The made observations demonstrate that the proposed damage index can accurately identify the sudden damage events if the noise intensity is limited. PMID:24991647
Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.
Athanasiadis, Emmanouil I; Cavouras, Dionisis A; Glotsos, Dimitris Th; Georgiadis, Pantelis V; Kalatzis, Ioannis K; Nikiforidis, George C
2009-11-01
A wavelet-based modification of the Markov random field (WMRF) model is proposed for segmenting complementary DNA (cDNA) microarray images. For evaluation purposes, five simulated and a set of five real microarray images were used. The one-level stationary wavelet transform (SWT) of each microarray image was used to form two images, a denoised image, using hard thresholding filter, and a magnitude image, from the amplitudes of the horizontal and vertical components of SWT. Elements from these two images were suitably combined to form the WMRF model for segmenting spots from their background. The WMRF was compared against the conventional MRF and the Fuzzy C means (FCM) algorithms on simulated and real microarray images and their performances were evaluated by means of the segmentation matching factor (SMF) and the coefficient of determination (r2). Additionally, the WMRF was compared against the SPOT and SCANALYZE, and performances were evaluated by the mean absolute error (MAE) and the coefficient of variation (CV). The WMRF performed more accurately than the MRF and FCM (SMF: 92.66, 92.15, and 89.22, r2 : 0.92, 0.90, and 0.84, respectively) and achieved higher reproducibility than the MRF, SPOT, and SCANALYZE (MAE: 497, 1215, 1180, and 503, CV: 0.88, 1.15, 0.93, and 0.90, respectively). PMID:19783509
Bivariate wavelet-based clustering of sea-level and atmospheric pressure time series
NASA Astrophysics Data System (ADS)
Barbosa, Susana; Gouveia, Sonia; Scotto, Manuel; Alonso, Andres
2015-04-01
The atmospheric pressure is responsible for a downward force acting on the sea surface which is compensated, to some extent, by corresponding sea-level variations. The static response of the sea surface can be linearly modelled, a decrease (increase) in atmospheric pressure of 1 mb raising (depressing) sea level by 1 cm. However, the dynamic sea surface response to atmospheric pressure loading, associated with ocean dynamics and wind effects, is scale-dependent and difficult to establish. The present study addresses the co-variability of sea-level and pressure time series in the Baltic Sea from the bivariate analysis of tide gauge and reanalysis records. The time series are normalised by the corresponding standard deviation and the wavelet covariance is computed as a measure of the association between sea-level and pressure across scales. A clustering procedure using a dissimilarity matrix based on the wavelet covariance is then implemented. Different classical clustering techniques, including average, single and complete linkage criteria are applied and the group linkage is selected in order to maximise the dendrogram's goodness-of-fit.
The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method
Zhang, Yanyan; Wang, Jue
2014-01-01
Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy. PMID:24868240
Heart Rate Variability and Wavelet-based Studies on ECG Signals from Smokers and Non-smokers
NASA Astrophysics Data System (ADS)
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 < 0.05). The frequency domain analysis showed an increase in the LF and LF/HF components with a subsequent decrease in the HF component. The HRV features were analyzed for classification of the smokers from the non-smokers. The results indicated that when RMSSD, SD1 and RR-mean features were used concurrently a classification efficiency of > 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.
NASA Astrophysics Data System (ADS)
Tanaka, Satoyuki; Okada, Hiroshi; Okazawa, Shigenobu
2012-07-01
This study develops a wavelet Galerkin method (WGM) that uses B-spline wavelet bases for application to solid mechanics problems. A fictitious domain is often adopted to treat general boundaries in WGMs. In the analysis, the body is extended to its exterior but very low stiffness is applied to the exterior region. The stiffness matrix in the WGM becomes singular without the use of a fictitious domain. The problem arises from the lack of linear independence of the basis functions. A technique to remove basis functions that can be represented by the superposition of the other basis functions is proposed. The basis functions are automatically eliminated in the pre conditioning step. An adaptive strategy is developed using the proposed technique. The solution is refined by superposing finer wavelet functions. Numerical examples of solid mechanics problems are presented to demonstrate the multiresolution properties of the WGM.
Fu, J C; Chai, J W; Wong, S T
2000-11-01
MRI is noninvasive and generates clear images, giving it great potential as a diagnostic instrument. However, current methods of image analysis are too time-consuming for dynamic systems such as the cardiovascular system. Since dynamic imagery generate a huge number of images, a computer aided machine vision diagnostic tool is essential for implementing MRI-based measurement. In this paper, a wavelet-based image technique is applied to enhance left ventricular endocardial and epicardial profiles as the preprocessor for a dynamic programming-based automatic border detection algorithm. Statistical tests are conducted to verify the performance of the enhancement technique by comparing borders manually drawn with 1. borders generated from the enhanced images, and 2. borders generated for the original images. PMID:11118768
NASA Astrophysics Data System (ADS)
Wu, Huijuan; Li, Xiaoyu; Li, Hanyu; Wu, Yu; Gong, Yuan; Rao, Yunjiang
2013-10-01
Phase-sensitive Optical-Time-Domain Reflectometry (OTDR) system is a typical distributed fiber-optic sensing technology to detect and locate multiple dynamic disturbances from the outside, which provides a cost-effective and highly sensitive solution especially for monitoring long or ultra-long perimeters. However, the system is liable to be interfered by laser frequency drifts and environmental noises due to its phase sensitivity. The fluctuant and time-varying backgrounds severely obscure real intrusion signals, which always cause bad detection results or high Nuisance Alarm Rates (NARs). In this paper, an effective signal separation method is proposed to extract true intrusion information from the complicated noisy backgrounds of phase-sensitive OTDR system. The sensing signal in the time-domain at each spatial point is obtained by accumulating the changing trails at different moments. Multi-scale wavelet decomposition is employed on the temporal signal to get the detailed components at different scales. By selectively recombining the scale components, it can easily extract the real intrusion signal, and separate the fluctuant frequency-drift induced phase noises, and the time-varying sound or other interferences caused by the air movements, which are respectively located at different time-frequency components. Moreover, the experimental results show that the event type could be divided and discerned from the time-frequency energy distribution at different scale. Thus nuisance and false alarms in practical applications of phase-sensitive OTDR system can be decreased significantly by this way of signal separation and extraction. This technique provides a useful solution for the intrusion detection and identification of the phase-sensitive OTDR in complicated environments, and paves the way for many important applications such as long perimeter security, oil or gas pipe safety monitoring, large-scale structure health detection and fault diagnosis and so on.
NASA Astrophysics Data System (ADS)
Alam Khan, Najeeb; Razzaq, Oyoon Abdul
2016-03-01
In the present work a wavelets approximation method is employed to solve fuzzy boundary value differential equations (FBVDEs). Essentially, a truncated Legendre wavelets series together with the Legendre wavelets operational matrix of derivative are utilized to convert FB- VDE into a simple computational problem by reducing it into a system of fuzzy algebraic linear equations. The capability of scheme is investigated on second order FB- VDE considered under generalized H-differentiability. Solutions are represented graphically showing competency and accuracy of this method.
A primal-dual method for total-variation-based wavelet domain inpainting.
Wen, You-Wei; Chan, Raymond H; Yip, Andy M
2012-01-01
Loss of information in a wavelet domain can occur during storage or transmission when the images are formatted and stored in terms of wavelet coefficients. This calls for image inpainting in wavelet domains. In this paper, a variational approach is used to formulate the reconstruction problem. We propose a simple but very efficient iterative scheme to calculate an optimal solution and prove its convergence. Numerical results are presented to show the performance of the proposed algorithm. PMID:21693425
NASA Astrophysics Data System (ADS)
Shoaib, Muhammad; Shamseldin, Asaad Y.; Melville, Bruce W.; Khan, Mudasser Muneer
2016-04-01
In order to predict runoff accurately from a rainfall event, the multilayer perceptron type of neural network models are commonly used in hydrology. Furthermore, the wavelet coupled multilayer perceptron neural network (MLPNN) models has also been found superior relative to the simple neural network models which are not coupled with wavelet. However, the MLPNN models are considered as static and memory less networks and lack the ability to examine the temporal dimension of data. Recurrent neural network models, on the other hand, have the ability to learn from the preceding conditions of the system and hence considered as dynamic models. This study for the first time explores the potential of wavelet coupled time lagged recurrent neural network (TLRNN) models for runoff prediction using rainfall data. The Discrete Wavelet Transformation (DWT) is employed in this study to decompose the input rainfall data using six of the most commonly used wavelet functions. The performance of the simple and the wavelet coupled static MLPNN models is compared with their counterpart dynamic TLRNN models. The study found that the dynamic wavelet coupled TLRNN models can be considered as alternative to the static wavelet MLPNN models. The study also investigated the effect of memory depth on the performance of static and dynamic neural network models. The memory depth refers to how much past information (lagged data) is required as it is not known a priori. The db8 wavelet function is found to yield the best results with the static MLPNN models and with the TLRNN models having small memory depths. The performance of the wavelet coupled TLRNN models with large memory depths is found insensitive to the selection of the wavelet function as all wavelet functions have similar performance.
NASA Astrophysics Data System (ADS)
Widjaja, Joewono
2015-11-01
A new method is proposed for recognizing noise corrupted low-contrast retinal images that employs joint wavelet transform correlator with compressed reference and target. Noise robustness is achieved by correlating wavelet-transformed retinal target and reference images. Simulation results show that besides being robust to noise, its recognition performance can become independent upon compression qualities when low spatial-frequency components of joint power spectrum are enhanced by appropriately dilated wavelet filter.
Salas-Boni, Rebeca; Bai, Yong; Harris, Patricia Rae Eileen; Drew, Barbara J; Hu, Xiao
2014-01-01
Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. We applied a multi resolution wavelet transform to the ECG data 20seconds prior to the alarm trigger, extracted features from appropriately chosen scales and combined them across all available leads. These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias. PMID:25172188
Lee, Boon-Giin; Lee, Boon-Leng; Chung, Wan-Young
2014-01-01
Driving drowsiness is a major cause of traffic accidents worldwide and has drawn the attention of researchers in recent decades. This paper presents an application for in-vehicle non-intrusive mobile-device-based automatic detection of driver sleep-onset in real time. The proposed application classifies the driving mental fatigue condition by analyzing the electroencephalogram (EEG) and respiration signals of a driver in the time and frequency domains. Our concept is heavily reliant on mobile technology, particularly remote physiological monitoring using Bluetooth. Respiratory events are gathered, and eight-channel EEG readings are captured from the frontal, central, and parietal (Fpz-Cz, Pz-Oz) regions. EEGs are preprocessed with a Butterworth bandpass filter, and features are subsequently extracted from the filtered EEG signals by employing the wavelet-packet-transform (WPT) method to categorize the signals into four frequency bands: α, β, θ, and δ. A mutual information (MI) technique selects the most descriptive features for further classification. The reduction in the number of prominent features improves the sleep-onset classification speed in the support vector machine (SVM) and results in a high sleep-onset recognition rate. Test results reveal that the combined use of the EEG and respiration signals results in 98.6% recognition accuracy. Our proposed application explores the possibility of processing long-term multi-channel signals. PMID:25264954
Improvement of impact noise in a passenger car utilizing sound metric based on wavelet transform
NASA Astrophysics Data System (ADS)
Lee, Sang-Kwon; Kim, Ho-Wuk; Na, Eun-Woo
2010-08-01
A new sound metric for impact sound is developed based on the continuous wavelet transform (CWT), a useful tool for the analysis of non-stationary signals such as impact noise. Together with new metric, two other conventional sound metrics related to sound modulation and fluctuation are also considered. In all, three sound metrics are employed to develop impact sound quality indexes for several specific impact courses on the road. Impact sounds are evaluated subjectively by 25 jurors. The indexes are verified by comparing the correlation between the index output and results of a subjective evaluation based on a jury test. These indexes are successfully applied to an objective evaluation for improvement of the impact sound quality for cases where some parts of the suspension system of the test car are modified.
Ravanfar, Seyed Alireza; Abdul Razak, Hashim; Ismail, Zubaidah; Monajemi, Hooman
2015-01-01
This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage. PMID:26371005
Ravanfar, Seyed Alireza; Razak, Hashim Abdul; Ismail, Zubaidah; Monajemi, Hooman
2015-01-01
This paper reports on a two-step approach for optimally determining the location and severity of damage in beam structures under flexural vibration. The first step focuses on damage location detection. This is done by defining the damage index called relative wavelet packet entropy (RWPE). The damage severities of the model in terms of loss of stiffness are assessed in the second step using the inverse solution of equations of motion of a structural system in the wavelet domain. For this purpose, the connection coefficient of the scaling function to convert the equations of motion in the time domain into the wavelet domain is applied. Subsequently, the dominant components based on the relative energies of the wavelet packet transform (WPT) components of the acceleration responses are defined. To obtain the best estimation of the stiffness parameters of the model, the least squares error minimization is used iteratively over the dominant components. Then, the severity of the damage is evaluated by comparing the stiffness parameters of the identified model before and after the occurrence of damage. The numerical and experimental results demonstrate that the proposed method is robust and effective for the determination of damage location and accurate estimation of the loss in stiffness due to damage. PMID:26371005
A wavelet phase filter for emission tomography
Olsen, E.T.; Lin, B.
1995-07-01
The presence of a high level of noise is a characteristic in some tomographic imaging techniques such as positron emission tomography (PET). Wavelet methods can smooth out noise while preserving significant features of images. Mallat et al. proposed a wavelet based denoising scheme exploiting wavelet modulus maxima, but the scheme is sensitive to noise. In this study, the authors explore the properties of wavelet phase, with a focus on reconstruction of emission tomography images. Specifically, they show that the wavelet phase of regular Poisson noise under a Haar-type wavelet transform converges in distribution to a random variable uniformly distributed on [0, 2{pi}). They then propose three wavelet-phase-based denoising schemes which exploit this property: edge tracking, local phase variance thresholding, and scale phase variation thresholding. Some numerical results are also presented. The numerical experiments indicate that wavelet phase techniques show promise for wavelet based denoising methods.
Schlossnagle, G.; Restrepo, J.M.; Leaf, G.K.
1993-12-01
The properties of periodized Daubechies wavelets on [0,1] are detailed and contrasted against their counterparts which form a basis for L{sup 2}(R). Numerical examples illustrate the analytical estimates for convergence and demonstrate by comparison with Fourier spectral methods the superiority of wavelet projection methods for approximations. The analytical solution to inner products of periodized wavelets and their derivatives, which are known as connection coefficients, is presented, and several tabulated values are included.
NASA Astrophysics Data System (ADS)
Martin, Roland; Monteiller, Vadim; Komatitsch, Dimitri; Perrouty, Stéphane; Jessell, Mark; Bonvalot, Sylvain; Lindsay, Mark
2013-12-01
We solve the 3-D gravity inverse problem using a massively parallel voxel (or finite element) implementation on a hybrid multi-CPU/multi-GPU (graphics processing units/GPUs) cluster. This allows us to obtain information on density distributions in heterogeneous media with an efficient computational time. In a new software package called TOMOFAST3D, the inversion is solved with an iterative least-square or a gradient technique, which minimizes a hybrid L1-/L2-norm-based misfit function. It is drastically accelerated using either Haar or fourth-order Daubechies wavelet compression operators, which are applied to the sensitivity matrix kernels involved in the misfit minimization. The compression process behaves like a pre-conditioning of the huge linear system to be solved and a reduction of two or three orders of magnitude of the computational time can be obtained for a given number of CPU processor cores. The memory storage required is also significantly reduced by a similar factor. Finally, we show how this CPU parallel inversion code can be accelerated further by a factor between 3.5 and 10 using GPU computing. Performance levels are given for an application to Ghana, and physical information obtained after 3-D inversion using a sensitivity matrix with around 5.37 trillion elements is discussed. Using compression the whole inversion process can last from a few minutes to less than an hour for a given number of processor cores instead of tens of hours for a similar number of processor cores when compression is not used.
Block based image compression technique using rank reduction and wavelet difference reduction
NASA Astrophysics Data System (ADS)
Bolotnikova, Anastasia; Rasti, Pejman; Traumann, Andres; Lusi, Iiris; Daneshmand, Morteza; Noroozi, Fatemeh; Samuel, Kadri; Sarkar, Suman; Anbarjafari, Gholamreza
2015-12-01
In this paper a new block based lossy image compression technique which is using rank reduction of the image and wavelet difference reduction (WDR) technique, is proposed. Rank reduction is obtained by applying singular value decomposition (SVD). The input image is divided into blocks of equal sizes after which quantization by SVD is carried out on each block followed by WDR technique. Reconstruction is carried out by decompressing each blocks bit streams and then merging all of them to obtain the decompressed image. The visual and quantitative experimental results of the proposed image compression technique are shown and also compared with those of the WDR technique and JPEG2000. From the results of the comparison, the proposed image compression technique outperforms the WDR and JPEG2000 techniques.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.
Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping
2015-01-01
Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately. PMID:26413090
Wavelet based edge detection algorithm for web surface inspection of coated board web
NASA Astrophysics Data System (ADS)
Barjaktarovic, M.; Petricevic, S.
2010-07-01
This paper presents significant improvement of the already installed vision system. System was designed for real time coated board inspection. The improvement is achieved with development of a new algorithm for edge detection. The algorithm is based on the redundant (undecimated) wavelet transform. Compared to the existing algorithm better delineation of edges is achieved. This yields to better defect detection probability and more accurate geometrical classification, which will provide additional reduction of waste. Also, algorithm will provide detailed classification and more reliably tracking of defects. This improvement requires minimal changes in processing hardware, only a replacement of the graphic card would be needed, adding only negligibly to the system cost. Other changes are accomplished entirely in the image processing software.
Research on power-law acoustic transient signal detection based on wavelet transform
NASA Astrophysics Data System (ADS)
Han, Jian-hui; Yang, Ri-jie; Wang, Wei
2007-11-01
Aiming at the characteristics of acoustic transient signal emitted from antisubmarine weapon which is being dropped into water (torpedo, aerial sonobuoy and rocket assisted depth charge etc.), such as short duration, low SNR, abruptness and instability, based on traditional power-law detector, a new method to detect acoustic transient signal is proposed. Firstly wavelet transform is used to de-noise signal, removes random spectrum components and improves SNR. Then Power- Law detector is adopted to detect transient signal. The simulation results show the method can effectively extract envelop characteristic of transient signal on the condition of low SNR. The performance of WT-Power-Law markedly outgoes that of traditional Power-Law detection method.
Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM
Zhang, Chaolong; He, Yigang; Yuan, Lifeng; Xiang, Sheng; Wang, Jinping
2015-01-01
Lithium-ion batteries are widely used in many electronic systems. Therefore, it is significantly important to estimate the lithium-ion battery's remaining useful life (RUL), yet very difficult. One important reason is that the measured battery capacity data are often subject to the different levels of noise pollution. In this paper, a novel battery capacity prognostics approach is presented to estimate the RUL of lithium-ion batteries. Wavelet denoising is performed with different thresholds in order to weaken the strong noise and remove the weak noise. Relevance vector machine (RVM) improved by differential evolution (DE) algorithm is utilized to estimate the battery RUL based on the denoised data. An experiment including battery 5 capacity prognostics case and battery 18 capacity prognostics case is conducted and validated that the proposed approach can predict the trend of battery capacity trajectory closely and estimate the battery RUL accurately. PMID:26413090
A wavelet transform based feature extraction and classification of cardiac disorder.
Sumathi, S; Beaulah, H Lilly; Vanithamani, R
2014-09-01
This paper approaches an intellectual diagnosis system using hybrid approach of Adaptive Neuro-Fuzzy Inference System (ANFIS) model for classification of Electrocardiogram (ECG) signals. This method is based on using Symlet Wavelet Transform for analyzing the ECG signals and extracting the parameters related to dangerous cardiac arrhythmias. In these particular parameters were used as input of ANFIS classifier, five most important types of ECG signals they are Normal Sinus Rhythm (NSR), Atrial Fibrillation (AF), Pre-Ventricular Contraction (PVC), Ventricular Fibrillation (VF), and Ventricular Flutter (VFLU) Myocardial Ischemia. The inclusion of ANFIS in the complex investigating algorithms yields very interesting recognition and classification capabilities across a broad spectrum of biomedical engineering. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies. The results give importance to that the proposed ANFIS model illustrates potential advantage in classifying the ECG signals. The classification accuracy of 98.24 % is achieved. PMID:25023652
Fuzzy-Wavelet Based Double Line Transmission System Protection Scheme in the Presence of SVC
NASA Astrophysics Data System (ADS)
Goli, Ravikumar; Shaik, Abdul Gafoor; Tulasi Ram, Sankara S.
2014-07-01
Increasing the power transfer capability and efficient utilization of available transmission lines, improving the power system controllability and stability, power oscillation damping and voltage compensation have made strides and created Flexible AC Transmission (FACTS) devices in recent decades. Shunt FACTS devices can have adverse effects on distance protection both in steady state and transient periods. Severe under reaching is the most important problem of relay which is caused by current injection at the point of connection to the system. Current absorption of compensator leads to overreach of relay. This work presents an efficient method based on wavelet transforms, fault detection, classification and location using Fuzzy logic technique which is almost independent of fault impedance, fault distance and fault inception angle. The proposed protection scheme is found to be fast, reliable and accurate for various types of faults on transmission lines with and without Static Var compensator at different locations and with various incidence angles.
Wang Fei; Zhao Xuezeng; Li Ning
2010-10-15
We introduce a multiscale characterization method for line edge roughness (LER) based on redundant second generation wavelet transform. This method involves decomposing LER characteristics into independent bands with different spatial frequency components at different scales, and analyzing the reconstructed signals to work out the roughness exponent, the spatial frequency distribution characteristics, as well as the rms value. The effect of noise can be predicted using detailed signals in the minimum space of scale. This method was applied to numerical profiles for validation. Results show that according to the line edge profiles with similar amplitudes, the roughness exponent R can effectively reflect the degree of irregularity of LER and intuitively provide information on LER spatial frequency distribution.
Lahmiri, Salim; Gargour, Christian S; Gabrea, Marcel
2014-10-01
An automated diagnosis system that uses complex continuous wavelet transform (CWT) to process retina digital images and support vector machines (SVMs) for classification purposes is presented. In particular, each retina image is transformed into two one-dimensional signals by concatenating image rows and columns separately. The mathematical norm of phase angles found in each one-dimensional signal at each level of CWT decomposition are relied on to characterise the texture of normal images against abnormal images affected by exudates, drusen and microaneurysms. The leave-one-out cross-validation method was adopted to conduct experiments and the results from the SVM show that the proposed approach gives better results than those obtained by other methods based on the correct classification rate, sensitivity and specificity. PMID:26609393
Zhou, Long; Rauh, Cornelia; Delgado, Antonio
2015-07-01
The discrete wavelet is introduced to construct the turbulent velocity fields. The simple binary cascade model p model is served as the inertial range model for velocity increments. The dissipation model, which follows Foias et al. [Phys. Fluids A 2, 464 (1990)] takes the form of exp(-gk). The length of inertial and dissipation ranges is computed according to the different construction levels. Based on the binary cascade theory and the proposed dissipation model, the Reynolds number regarding to the cascade process can be estimated. The dissipation rate calculated from the proposed model not only agrees with the existing experiment data, but also suggests that the dissipation rate is not an independent variable with respect to the Reynolds number. PMID:26274272
Daniel, Ebenezer; Anitha, J
2016-04-01
Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature. PMID:26945462
Discrete wavelet transform to improve guided-wave-based health monitoring of tendons and cables
NASA Astrophysics Data System (ADS)
Rizzo, Piervincenzo; Lanza di Scalea, Francesco
2004-07-01
Multi-wire steel strands are used in civil structures as pre-stressing tendons in prestressed concrete and as stay-cables in cable-stayed and suspension bridges. Monitoring the structural performance of these components is important to ensure the proper functioning and safety of the entire structure. Among the various NDE techniques that are under investigation for monitoring tendons and cables, the use of ultrasonic guided waves shows good promises. The main advantage of this approach is the possibility for the simultaneous monitoring of loads and detection of defects, such as corrosion and broken wires, by using the same ultrasonic setup. Load monitoring is achieved by measuring the travel time of the wave across a given length of the cable. Defect detection is achieved by measuring the reflections of the wave from the geometrical discontinuities. The new contributions of the current paper are two-fold. First, the study identifies those ultrasonic frequencies propagating with low attenuation for long-range defect detection. Second, the technique is substantially improved by implementing the Discrete Wavelet Transform (DWT) as a data post-processing tool. The data de-noising and data compression abilities of the DWT allow for greater sensitivity, larger ranges and higher monitoring speed. It is shown that the implementation of the DWT in the ultrasonic guided-wave technique becomes necessary for monitoring tendons and cables in the field.
The signal extraction of fetal heart rate based on wavelet transform and BP neural network
NASA Astrophysics Data System (ADS)
Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai
2005-04-01
This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.
Forecasting future estuarine hypoxia using a wavelet based neural network model
NASA Astrophysics Data System (ADS)
Muller, Andrew C.; Muller, Diana Lynn
2015-12-01
Ecosystem based modeling and predictions of hypoxia in estuaries and their adjacent coastal areas have become increasingly of interest to researchers and coastal zone managers. Although progress has been made in modeling oxygen dynamics and short-term predictions, there is still a lack of long-term forecasts that incorporate multiple inputs including climatological effects such as El Niño-Southern Oscillation (ENSO) events. In this study, we first develop a hypoxic volume index (HVI) using 26-years of hypoxic volume (<62.5 μm g l-1) measurements from the main-stem of the Chesapeake Bay. Then a cross-wavelet analysis is used to identify and weight input parameters in order to build a neural network model of future hypoxic volume. The time-forward dynamic model uses cross-bay winds along with the Oceanic Niño Index (ONI), and Susquehanna River flow indexes to predict a hypoxic volume index over the next several years. Wavelet analysis indicates an anti-phase relationship between southwesterly winds and hypoxic volume index, and an 18-month phase lag between Susquehanna River index and hypoxic volume index. The neural network model results yield R-values of 0.99, and 0.91 for training, and validation and an R2 of 0.68 for predictions illustrating the usefulness and promise of these types of models for long-term predictions of hypoxic volume. Model results could be used as a climatologically based hypoxic volume baseline for comparing actual hypoxic volume response to nutrient load reductions.
Wavelet-Based Spatial Scaling of Coupled Reaction-Diffusion Fields
Mishra, Sudib; Muralidharan, Krishna; Deymier, Pierre; Frantziskonis, G.; Pannala, Sreekanth; Simunovic, Srdjan
2008-01-01
Multiscale schemes for transferring information from fine to coarse scales are typically based on homogenization techniques. Such schemes smooth the fine scale features of the underlying fields, often resulting in the inability to accurately retain the fine scale correlations. In addition, higher-order statistical moments (beyond mean) of the relevant field variables are not necessarily preserved. As a superior alternative to averaging homogenization methods, a wavelet-based scheme for the exchange of information between a reactive and diffusive field in the context of multiscale reaction-diffusion problems is proposed and analyzed. The scheme is shown to be efficient in passing information along scales, from fine to coarse, i.e., upscaling as well as from coarse to fine, i.e., downscaling. It incorporates fine scale statistics (higher-order moments beyond mean), mainly due to the capability of wavelets to represent fields hierarchically. Critical to the success of the scheme is the identification of dominant scales containing the majority of the useful information. The dominant scales in effect specify the coarsest resolution possible. The scheme is applied in detail to the analysis of a diffusive system with a chemically reacting boundary. Reactions are simulated using kinetic Monte Carlo (kMC) and diffusion is solved by finite differences (FDs). Spatial scale differences are present at the interface of the kMC sites and the diffusion grid. The computational efficiency of the scheme is compared to results obtained by averaging homogenization, and to results from a benchmark scheme that ensures spatial scale parity between kMC and FD.
NASA Astrophysics Data System (ADS)
Wu, Fu-Chun; Chang, Ching-Fu; Shiau, Jenq-Tzong
2015-05-01
The full range of natural flow regime is essential for sustaining the riverine ecosystems and biodiversity, yet there are still limited tools available for assessment of flow regime alterations over a spectrum of temporal scales. Wavelet analysis has proven useful for detecting hydrologic alterations at multiple scales via the wavelet power spectrum (WPS) series. The existing approach based on the global WPS (GWPS) ratio tends to be dominated by the rare high-power flows so that alterations of the more frequent low-power flows are often underrepresented. We devise a new approach based on individual deviations between WPS (DWPS) that are root-mean-squared to yield the global DWPS (GDWPS). We test these two approaches on the three reaches of the Feitsui Reservoir system (Taiwan) that are subjected to different classes of anthropogenic interventions. The GDWPS reveal unique features that are not detected with the GWPS ratios. We also segregate the effects of individual subflow components on the overall flow regime alterations using the subflow GDWPS. The results show that the daily hydropeaking waves below the reservoir not only intensified the flow oscillations at daily scale but most significantly eliminated subweekly flow variability. Alterations of flow regime were most severe below the diversion weir, where the residual hydropeaking resulted in a maximum impact at daily scale while the postdiversion null flows led to large hydrologic alterations over submonthly scales. The smallest impacts below the confluence reveal that the hydrologic alterations at scales longer than 2 days were substantially mitigated with the joining of the unregulated tributary flows, whereas the daily-scale hydrologic alteration was retained because of the hydropeaking inherited from the reservoir releases. The proposed DWPS approach unravels for the first time the details of flow regime alterations at these intermediate scales that are overridden by the low-frequency high-power flows when
NASA Astrophysics Data System (ADS)
Etehadtavakol, Mahnaz; Ng, E. Y. K.; Chandran, Vinod; Rabbani, Hossien
2013-11-01
Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.
NASA Astrophysics Data System (ADS)
Daglis, I. A.; Balasis, G.; Georgiou, M.; Papadimitriou, C.; Zesta, E.; Anastasiadis, A.
2012-01-01
Magnetospheric ULF waves influence radiation belt dynamics and are therefore of particular relevance for space weather nowcasting and forecasting efforts. We have used novel algorithms based on wavelet spectral methods to analyze multipoint observations of ULF wave activity by the Cluster and THEMIS missions and by ground-based magnetometers. Wavelet analysis is becoming a common tool for analyzing localized variations of power within a time series. By decomposing a time series into time-frequency space, we are able to determine both the dominant modes of variability and how these modes vary in time. The advantage of analyzing a signal with wavelets as the analyzing kernel is that it enables us to study features of the signal locally with a detail matched to their scale. Owing to its unique time-frequency localization, wavelet analysis is especially useful for signals that are non-stationary, have short-lived transient components, have features at different scales, or have singularities. The results are rather promising for the development of automatic identification tools, which will allow the detection and classification of various categories of ULF waves from multipoint magnetospheric observations according to well-defined criteria.
NASA Technical Reports Server (NTRS)
Kempel, Leo C.
1992-01-01
Wavelets are an exciting new topic in applied mathematics and signal processing. This paper will provide a brief review of wavelets which are also known as families of functions with an emphasis on interpretation rather than rigor. We will derive an indirect use of wavelets for the solution of integral equations based techniques adapted from image processing. Examples for resistive strips will be given illustrating the effect of these techniques as well as their promise in reducing dramatically the requirement in order to solve an integral equation for large bodies. We also will present a direct implementation of wavelets to solve an integral equation. Both methods suggest future research topics and may hold promise for a variety of uses in computational electromagnetics.
An Improved Wavelet Packet-Chaos Model for Life Prediction of Space Relays Based on Volterra Series
Li, Lingling; Han, Ye; Chen, Wenyuan; Lv, Congmin; Sun, Dongwang
2016-01-01
In this paper, an improved algorithm of wavelet packet-chaos model for life prediction of space relays based on volterra series is proposed. In the proposed method, the high and low frequency time sequence components of performance parameters are obtained by employing the improved wavelet packet transform to decompose the performance parameters of the relay into multiple scales. Then the optimization algorithm of parameters in volterra series is improved, and is used to construct a chaotic forecasting model for the high and low frequency time sequence components gained by the wavelet packet transform. At last, the chaotic forecasting results of the high and low frequency components are combined by taking the wavelet packet reconstruction approach, so as to predict the lifetime of the studied space relay. The algorithm can predict the life curve of the relay accurately and reflect the characteristics of the relay performance with sufficient accuracy. The proposed method is validated via a case study of a space relay. PMID:27355578
NASA Astrophysics Data System (ADS)
Singh, Hukum
2016-06-01
An asymmetric scheme has been proposed for optical double images encryption in the gyrator wavelet transform (GWT) domain. Grayscale and binary images are encrypted separately using double random phase encoding (DRPE) in the GWT domain. Phase masks based on devil's vortex Fresnel Lens (DVFLs) and random phase masks (RPMs) are jointly used in spatial as well as in the Fourier plane. The images to be encrypted are first gyrator transformed and then single-level discrete wavelet transformed (DWT) to decompose LL , HL , LH and HH matrices of approximation, horizontal, vertical and diagonal coefficients. The resulting coefficients from the DWT are multiplied by other RPMs and the results are applied to inverse discrete wavelet transform (IDWT) for obtaining the encrypted images. The images are recovered from their corresponding encrypted images by using the correct parameters of the GWT, DVFL and its digital implementation has been performed using MATLAB 7.6.0 (R2008a). The mother wavelet family, DVFL and gyrator transform orders associated with the GWT are extra keys that cause difficulty to an attacker. Thus, the scheme is more secure as compared to conventional techniques. The efficacy of the proposed scheme is verified by computing mean-squared-error (MSE) between recovered and the original images. The sensitivity of the proposed scheme is verified with encryption parameters and noise attacks.
Identification of diesel front sound source based on continuous wavelet transform.
Hao, Zhi-yong; Han, Jun
2004-09-01
Acoustic signals from diesel engines contain useful information but also include considerable noise components. To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement. PMID:15323001
Rincón, Francisco; Recas, Joaquin; Khaled, Nadia; Atienza, David
2011-11-01
This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG) wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems. PMID:21827976
Performance evaluation of wavelet-based face verification on a PDA recorded database
NASA Astrophysics Data System (ADS)
Sellahewa, Harin; Jassim, Sabah A.
2006-05-01
The rise of international terrorism and the rapid increase in fraud and identity theft has added urgency to the task of developing biometric-based person identification as a reliable alternative to conventional authentication methods. Human Identification based on face images is a tough challenge in comparison to identification based on fingerprints or Iris recognition. Yet, due to its unobtrusive nature, face recognition is the preferred method of identification for security related applications. The success of such systems will depend on the support of massive infrastructures. Current mobile communication devices (3G smart phones) and PDA's are equipped with a camera which can capture both still and streaming video clips and a touch sensitive display panel. Beside convenience, such devices provide an adequate secure infrastructure for sensitive & financial transactions, by protecting against fraud and repudiation while ensuring accountability. Biometric authentication systems for mobile devices would have obvious advantages in conflict scenarios when communication from beyond enemy lines is essential to save soldier and civilian life. In areas of conflict or disaster the luxury of fixed infrastructure is not available or destroyed. In this paper, we present a wavelet-based face verification scheme that have been specifically designed and implemented on a currently available PDA. We shall report on its performance on the benchmark audio-visual BANCA database and on a newly developed PDA recorded audio-visual database that take include indoor and outdoor recordings.
Fetal QRS detection and heart rate estimation: a wavelet-based approach.
Almeida, Rute; Gonçalves, Hernâni; Bernardes, João; Rocha, Ana Paula
2014-08-01
Fetal heart rate monitoring is used for pregnancy surveillance in obstetric units all over the world but in spite of recent advances in analysis methods, there are still inherent technical limitations that bound its contribution to the improvement of perinatal indicators. In this work, a previously published wavelet transform based QRS detector, validated over standard electrocardiogram (ECG) databases, is adapted to fetal QRS detection over abdominal fetal ECG. Maternal ECG waves were first located using the original detector and afterwards a version with parameters adapted for fetal physiology was applied to detect fetal QRS, excluding signal singularities associated with maternal heartbeats. Single lead (SL) based marks were combined in a single annotator with post processing rules (SLR) from which fetal RR and fetal heart rate (FHR) measures can be computed. Data from PhysioNet with reference fetal QRS locations was considered for validation, with SLR outperforming SL including ICA based detections. The error in estimated FHR using SLR was lower than 20 bpm for more than 80% of the processed files. The median error in 1 min based FHR estimation was 0.13 bpm, with a correlation between reference and estimated FHR of 0.48, which increased to 0.73 when considering only records for which estimated FHR > 110 bpm. This allows us to conclude that the proposed methodology is able to provide a clinically useful estimation of the FHR. PMID:25070210
Non-stationary dynamics in the bouncing ball: A wavelet perspective
Behera, Abhinna K. Panigrahi, Prasanta K.; Sekar Iyengar, A. N.
2014-12-01
The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.
Non-stationary dynamics in the bouncing ball: a wavelet perspective.
Behera, Abhinna K; Iyengar, A N Sekar; Panigrahi, Prasanta K
2014-12-01
The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding to neutral turbulence, viscous dissipation regions, and different time varying periodic modulations. PMID:25554027
NASA Astrophysics Data System (ADS)
Chai, Bing-Bing; Vass, Jozsef; Zhuang, Xinhua
1997-04-01
Recent success in wavelet coding is mainly attributed to the recognition of importance of data organization. There has been several very competitive wavelet codecs developed, namely, Shapiro's Embedded Zerotree Wavelets (EZW), Servetto et. al.'s Morphological Representation of Wavelet Data (MRWD), and Said and Pearlman's Set Partitioning in Hierarchical Trees (SPIHT). In this paper, we propose a new image compression algorithm called Significant-Linked Connected Component Analysis (SLCCA) of wavelet coefficients. SLCCA exploits both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. A so-called significant link between connected components is designed to reduce the positional overhead of MRWD. In addition, the significant coefficients' magnitude are encoded in bit plane order to match the probability model of the adaptive arithmetic coder. Experiments show that SLCCA outperforms both EZW and MRWD, and is tied with SPIHT. Furthermore, it is observed that SLCCA generally has the best performance on images with large portion of texture. When applied to fingerprint image compression, it outperforms FBI's wavelet scalar quantization by about 1 dB.
Selective error detection for error-resilient wavelet-based image coding.
Karam, Lina J; Lam, Tuyet-Trang
2007-12-01
This paper introduces the concept of a similarity check function for error-resilient multimedia data transmission. The proposed similarity check function provides information about the effects of corrupted data on the quality of the reconstructed image. The degree of data corruption is measured by the similarity check function at the receiver, without explicit knowledge of the original source data. The design of a perceptual similarity check function is presented for wavelet-based coders such as the JPEG2000 standard, and used with a proposed "progressive similarity-based ARQ" (ProS-ARQ) scheme to significantly decrease the retransmission rate of corrupted data while maintaining very good visual quality of images transmitted over noisy channels. Simulation results with JPEG2000-coded images transmitted over the Binary Symmetric Channel, show that the proposed ProS-ARQ scheme significantly reduces the number of retransmissions as compared to conventional ARQ-based schemes. The presented results also show that, for the same number of retransmitted data packets, the proposed ProS-ARQ scheme can achieve significantly higher PSNR and better visual quality as compared to the selective-repeat ARQ scheme. PMID:18092593
Choi, Hyun Ho; Lee, Ju Hwan; Kim, Sung Min; Park, Sung Yun
2015-01-01
Here, the speckle noise in ultrasonic images is removed using an image fusion-based denoising method. To optimize the denoising performance, each discrete wavelet transform (DWT) and filtering technique was analyzed and compared. In addition, the performances were compared in order to derive the optimal input conditions. To evaluate the speckle noise removal performance, an image fusion algorithm was applied to the ultrasound images, and comparatively analyzed with the original image without the algorithm. As a result, applying DWT and filtering techniques caused information loss and noise characteristics, and did not represent the most significant noise reduction performance. Conversely, an image fusion method applying SRAD-original conditions preserved the key information in the original image, and the speckle noise was removed. Based on such characteristics, the input conditions of SRAD-original had the best denoising performance with the ultrasound images. From this study, the best denoising technique proposed based on the results was confirmed to have a high potential for clinical application. PMID:26405924
CR image filter methods research based on wavelet-domain hidden markov models
NASA Astrophysics Data System (ADS)
Wang, Jun-li; Wang, Yun-peng; Li, Da-yi; Li, Shi-wu; Kui, Hai-lin
2006-01-01
In the procedure of computed radiography imaging, we should firstly get across the characters of kinds of noises and the relationship between the image signals and noises. Based on the specialties of computed radiography (CR) images and medical image processing, we have study the filtering methods for computed radiography images noises. On the base of analyzing computed radiography imaging system in detail, the author think that the major two noises are Gaussian white noise and Poisson noise. Then, the different relationship of between two kinds of noises and signal were studied completely. By considering both the characteristics of computed radiography images and the statistical features of wavelet transformed images, a multiscale image filtering algorithm, which based on two-state hidden markov model (HMM) and mixture Gaussian statistical model, has been used to decrease the Gaussian white noise in computed images. By using EM (Expectation Maximization) algorithm to estimate noise coefficients in each scale and obtain power spectrum matrix, then this carried through the syncretized two Filter that are IIR(infinite impulse response) Wiener Filter and HMM, according to scale size ,and achieve the experiments as well as the comparison with other denoising methods were presented at last.
NASA Astrophysics Data System (ADS)
Nanda, Trushnamayee; Sahoo, Bhabagrahi; Beria, Harsh; Chatterjee, Chandranath
2016-08-01
Although flood forecasting and warning system is a very important non-structural measure in flood-prone river basins, poor raingauge network as well as unavailability of rainfall data in real-time could hinder its accuracy at different lead times. Conversely, since the real-time satellite-based rainfall products are now becoming available for the data-scarce regions, their integration with the data-driven models could be effectively used for real-time flood forecasting. To address these issues in operational streamflow forecasting, a new data-driven model, namely, the wavelet-based non-linear autoregressive with exogenous inputs (WNARX) is proposed and evaluated in comparison with four other data-driven models, viz., the linear autoregressive moving average with exogenous inputs (ARMAX), static artificial neural network (ANN), wavelet-based ANN (WANN), and dynamic nonlinear autoregressive with exogenous inputs (NARX) models. First, the quality of input rainfall products of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), viz., TRMM and TRMM-real-time (RT) rainfall products is assessed through statistical evaluation. The results reveal that the satellite rainfall products moderately correlate with the observed rainfall, with the gauge-adjusted TRMM product outperforming the real-time TRMM-RT product. The TRMM rainfall product better captures the ground observations up to 95 percentile range (30.11 mm/day), although the hit rate decreases for high rainfall intensity. The effect of antecedent rainfall (AR) and climate forecast system reanalysis (CFSR) temperature product on the catchment response is tested in all the developed models. The results reveal that, during real-time flow simulation, the satellite-based rainfall products generally perform worse than the gauge-based rainfall. Moreover, as compared to the existing models, the flow forecasting by the WNARX model is way better than the other four models studied herein with the
Uddin, Muhammad Shahin; Tahtali, Murat; Lambert, Andrew J; Pickering, Mark R; Marchese, Margaret; Stuart, Iain
2016-05-20
Compared with other medical-imaging modalities, ultrasound (US) imaging is a valuable way to examine the body's internal organs, and two-dimensional (2D) imaging is currently the most common technique used in clinical diagnoses. Conventional 2D US imaging systems are highly flexible cost-effective imaging tools that permit operators to observe and record images of a large variety of thin anatomical sections in real time. Recently, 3D US imaging has also been gaining popularity due to its considerable advantages over 2D US imaging. It reduces dependency on the operator and provides better qualitative and quantitative information for an effective diagnosis. Furthermore, it provides a 3D view, which allows the observation of volume information. The major shortcoming of any type of US imaging is the presence of speckle noise. Hence, speckle reduction is vital in providing a better clinical diagnosis. The key objective of any speckle-reduction algorithm is to attain a speckle-free image while preserving the important anatomical features. In this paper we introduce a nonlinear multi-scale complex wavelet-diffusion based algorithm for speckle reduction and sharp-edge preservation of 2D and 3D US images. In the proposed method we use a Rayleigh and Maxwell-mixture model for 2D and 3D US images, respectively, where a genetic algorithm is used in combination with an expectation maximization method to estimate mixture parameters. Experimental results using both 2D and 3D synthetic, physical phantom, and clinical data demonstrate that our proposed algorithm significantly reduces speckle noise while preserving sharp edges without discernible distortions. The proposed approach performs better than the state-of-the-art approaches in both qualitative and quantitative measures. PMID:27411128
NASA Astrophysics Data System (ADS)
Zamani, Ahmad; Kolahi Azar, Amir; Safavi, Ali
2014-06-01
This paper presents a wavelet-based multifractal approach to characterize the statistical properties of temporal distribution of the 1982-2012 seismic activity in Mammoth Mountain volcano. The fractal analysis of time-occurrence series of seismicity has been carried out in relation to seismic swarm in association with magmatic intrusion happening beneath the volcano on 4 May 1989. We used the wavelet transform modulus maxima based multifractal formalism to get the multifractal characteristics of seismicity before, during, and after the unrest. The results revealed that the earthquake sequences across the study area show time-scaling features. It is clearly perceived that the multifractal characteristics are not constant in different periods and there are differences among the seismicity sequences. The attributes of singularity spectrum have been utilized to determine the complexity of seismicity for each period. Findings show that the temporal distribution of earthquakes for swarm period was simpler with respect to pre- and post-swarm periods.
Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging
Huettner, Andrew M.; Mickevicius, Nikolai J.; Ersoz, Ali; Koch, Kevin M.; Muftuler, L. Tugan; Nencka, Andrew S.
2015-01-01
A new method for designing radiofrequency (RF) pulses with numerical optimization in the wavelet domain is presented. Numerical optimization may yield solutions that might otherwise have not been discovered with analytic techniques alone. Further, processing in the wavelet domain reduces the number of unknowns through compression properties inherent in wavelet transforms, providing a more tractable optimization problem. This algorithm is demonstrated with simultaneous multi-slice (SMS) spin echo refocusing pulses because reduced peak RF power is necessary for SMS diffusion imaging with high acceleration factors. An iterative, nonlinear, constrained numerical minimization algorithm was developed to generate an optimized RF pulse waveform. Wavelet domain coefficients were modulated while iteratively running a Bloch equation simulator to generate the intermediate slice profile of the net magnetization. The algorithm minimizes the L2-norm of the slice profile with additional terms to penalize rejection band ripple and maximize the net transverse magnetization across each slice. Simulations and human brain imaging were used to demonstrate a new RF pulse design that yields an optimized slice profile and reduced peak energy deposition when applied to a multiband single-shot echo planar diffusion acquisition. This method may be used to optimize factors such as magnitude and phase spectral profiles and peak RF pulse power for multiband simultaneous multi-slice (SMS) acquisitions. Wavelet-based RF pulse optimization provides a useful design method to achieve a pulse waveform with beneficial amplitude reduction while preserving appropriate magnetization response for magnetic resonance imaging. PMID:26517262
Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging.
Huettner, Andrew M; Mickevicius, Nikolai J; Ersoz, Ali; Koch, Kevin M; Muftuler, L Tugan; Nencka, Andrew S
2015-01-01
A new method for designing radiofrequency (RF) pulses with numerical optimization in the wavelet domain is presented. Numerical optimization may yield solutions that might otherwise have not been discovered with analytic techniques alone. Further, processing in the wavelet domain reduces the number of unknowns through compression properties inherent in wavelet transforms, providing a more tractable optimization problem. This algorithm is demonstrated with simultaneous multi-slice (SMS) spin echo refocusing pulses because reduced peak RF power is necessary for SMS diffusion imaging with high acceleration factors. An iterative, nonlinear, constrained numerical minimization algorithm was developed to generate an optimized RF pulse waveform. Wavelet domain coefficients were modulated while iteratively running a Bloch equation simulator to generate the intermediate slice profile of the net magnetization. The algorithm minimizes the L2-norm of the slice profile with additional terms to penalize rejection band ripple and maximize the net transverse magnetization across each slice. Simulations and human brain imaging were used to demonstrate a new RF pulse design that yields an optimized slice profile and reduced peak energy deposition when applied to a multiband single-shot echo planar diffusion acquisition. This method may be used to optimize factors such as magnitude and phase spectral profiles and peak RF pulse power for multiband simultaneous multi-slice (SMS) acquisitions. Wavelet-based RF pulse optimization provides a useful design method to achieve a pulse waveform with beneficial amplitude reduction while preserving appropriate magnetization response for magnetic resonance imaging. PMID:26517262
NASA Astrophysics Data System (ADS)
Cheng, Jun; Zhang, Jun; Tian, Jinwen
2015-12-01
Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.
Wang, Diya; Zong, Yujin; Yang, Xuan; Hu, Hong; Wan, Jinjin; Zhang, Lei; Bouakaz, Ayache; Wan, Mingxi
2016-07-01
The aim of the study described here was to develop an ultrasound contrast plane wave imaging (PWI) method based on pulse-inversion bubble wavelet transform imaging (PIWI) to improve the contrast-to-tissue ratio of contrast images. A pair of inverted "bubble wavelets" with plane waves was constructed according to the modified Herring equation. The original echoes were replaced by the maximum wavelet correlation coefficients obtained from bubble wavelet correlation analysis. The echoes were then summed to distinguish microbubbles from tissues. In in vivo experiments on rabbit kidney, PIWI improved the contrast-to-tissue ratio of contrast images up to 4.5 ± 1.5 dB, compared with that obtained in B-mode (p < 0.05), through use of a pair of inverted plane waves. The disruption rate and infusion time of microbubbles in PIWI-based PWI were then quantified using two perfusion parameters, area under the curve and half transmit time estimated from time-intensity curves, respectively. After time-intensity curves were denoised by detrended fluctuation analysis, the average area under the curve and half transit time of PIWI-based PWI were 55.94% (p < 0.05) and 20.51% (p < 0.05) higher than those of conventional focused imaging, respectively. Because of its high contrast-to-tissue ratio and low disruption of microbubbles, PIWI-based PWI has a long infusion time and is therefore beneficial for transient monitoring and perfusion assessment of microbubbles circulating in vessels. PMID:27067280
Hejč, Jakub; Vítek, Martin; Ronzhina, Marina; Nováková, Marie; Kolářová, Jana
2015-09-01
We present a novel wavelet-based ECG delineation method with robust classification of P wave and T wave. The work is aimed on an adaptation of the method to long-term experimental electrograms (EGs) measured on isolated rabbit heart and to evaluate the effect of global ischemia in experimental EGs on delineation performance. The algorithm was tested on a set of 263 rabbit EGs with established reference points and on human signals using standard Common Standards for Quantitative Electrocardiography Standard Database (CSEDB). On CSEDB, standard deviation (SD) of measured errors satisfies given criterions in each point and the results are comparable to other published works. In rabbit signals, our QRS detector reached sensitivity of 99.87% and positive predictivity of 99.89% despite an overlay of spectral components of QRS complex, P wave and power line noise. The algorithm shows great performance in suppressing J-point elevation and reached low overall error in both, QRS onset (SD = 2.8 ms) and QRS offset (SD = 4.3 ms) delineation. T wave offset is detected with acceptable error (SD = 12.9 ms) and sensitivity nearly 99%. Variance of the errors during global ischemia remains relatively stable, however more failures in detection of T wave and P wave occur. Due to differences in spectral and timing characteristics parameters of rabbit based algorithm have to be highly adaptable and set more precisely than in human ECG signals to reach acceptable performance. PMID:26577367
Fahmy, Gamal; Black, John; Panchanathan, Sethuraman
2006-06-01
Today's multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are: 1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented. PMID:16764265
Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation
Maji, Pradipta; Roy, Shaswati
2015-01-01
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of brain magnetic resonance (MR) images. For many human experts, manual segmentation is a difficult and time consuming task, which makes an automated brain MR image segmentation method desirable. In this regard, this paper presents a new segmentation method for brain MR images, integrating judiciously the merits of rough-fuzzy computing and multiresolution image analysis technique. The proposed method assumes that the major brain tissues, namely, gray matter, white matter, and cerebrospinal fluid from the MR images are considered to have different textural properties. The dyadic wavelet analysis is used to extract the scale-space feature vector for each pixel, while the rough-fuzzy clustering is used to address the uncertainty problem of brain MR image segmentation. An unsupervised feature selection method is introduced, based on maximum relevance-maximum significance criterion, to select relevant and significant textural features for segmentation problem, while the mathematical morphology based skull stripping preprocessing step is proposed to remove the non-cerebral tissues like skull. The performance of the proposed method, along with a comparison with related approaches, is demonstrated on a set of synthetic and real brain MR images using standard validity indices. PMID:25848961
A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression
Nguyen, Nha; Vo, An; Choi, Inchan
2015-01-01
Abstract Studying epigenetic landscapes is important to understand the condition for gene regulation. Clustering is a useful approach to study epigenetic landscapes by grouping genes based on their epigenetic conditions. However, classical clustering approaches that often use a representative value of the signals in a fixed-sized window do not fully use the information written in the epigenetic landscapes. Clustering approaches to maximize the information of the epigenetic signals are necessary for better understanding gene regulatory environments. For effective clustering of multidimensional epigenetic signals, we developed a method called Dewer, which uses the entropy of stationary wavelet of epigenetic signals inside enriched regions for gene clustering. Interestingly, the gene expression levels were highly correlated with the entropy levels of epigenetic signals. Dewer separates genes better than a window-based approach in the assessment using gene expression and achieved a correlation coefficient above 0.9 without using any training procedure. Our results show that the changes of the epigenetic signals are useful to study gene regulation. PMID:25383910
A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis.
Yang, Lina; Tang, Yuan Yan; Lu, Yang; Luo, Huiwu
2015-01-01
One of the key tasks related to proteins is the similarity comparison of protein sequences in the area of bioinformatics and molecular biology, which helps the prediction and classification of protein structure and function. It is a significant and open issue to find similar proteins from a large scale of protein database efficiently. This paper presents a new distance based protein similarity analysis using a new encoding method of protein sequence which is based on fractal dimension. The protein sequences are first represented into the 1-dimensional feature vectors by their biochemical quantities. A series of Hybrid method involving discrete Wavelet transform, Fractal dimension calculation (HWF) with sliding window are then applied to form the feature vector. At last, through the similarity calculation, we can obtain the distance matrix, by which, the phylogenic tree can be constructed. We apply this approach by analyzing the ND5 (NADH dehydrogenase subunit 5) protein cluster data set. The experimental results show that the proposed model is more accurate than the existing ones such as Su's model, Zhang's model, Yao's model and MEGA software, and it is consistent with some known biological facts. PMID:26357222
Li, Fang; Wang, Ji-hua; Lu, An-xiang; Han, Ping
2015-04-01
The concentration of Cr, Cu, Zn, As and Pb in soil was tested by portable X-ray fluorescence spectrometer. Each sample was tested for 3 times, then after using wavelet threshold noise filtering method for denoising and smoothing the spectra, a standard curve for each heavy metal was established according to the standard values of heavy metals in soil and the corresponding counts which was the average of the 3 processed spectra. The signal to noise ratio (SNR), mean square error (MSE) and information entropy (H) were taken to assess the effects of denoising when using wavelet threshold noise filtering method for determining the best wavelet basis and wavelet decomposition level. Some samples with different concentrations and H3 B03 (blank) were chosen to retest this instrument to verify its stability. The results show that: the best denoising result was obtained with the coif3 wavelet basis at the decomposition level of 3 when using the wavelet transform method. The determination coefficient (R2) range of the instrument is 0.990-0.996, indicating that a high degree of linearity was found between the contents of heavy metals in soil and each X-ray fluorescence spectral characteristic peak intensity with the instrument measurement within the range (0-1,500 mg · kg(-1)). After retesting and calculating, the results indicate that all the detection limits of the instrument are below the soil standards at national level. The accuracy of the model has been effectively improved, and the instrument also shows good precision with the practical application of wavelet transform to the establishment and improvement of X-ray fluorescence spectrometer detection model. Thus the instrument can be applied in on-site rapid screening of heavy metal in contaminated soil. PMID:26197612
Data compression by wavelet transforms
NASA Technical Reports Server (NTRS)
Shahshahani, M.
1992-01-01
A wavelet transform algorithm is applied to image compression. It is observed that the algorithm does not suffer from the blockiness characteristic of the DCT-based algorithms at compression ratios exceeding 25:1, but the edges do not appear as sharp as they do with the latter method. Some suggestions for the improved performance of the wavelet transform method are presented.
Wavelet compression of medical imagery.
Reiter, E
1996-01-01
Wavelet compression is a transform-based compression technique recently shown to provide diagnostic-quality images at compression ratios as great as 30:1. Based on a recently developed field of applied mathematics, wavelet compression has found success in compression applications from digital fingerprints to seismic data. The underlying strength of the method is attributable in large part to the efficient representation of image data by the wavelet transform. This efficient or sparse representation forms the basis for high-quality image compression by providing subsequent steps of the compression scheme with data likely to result in long runs of zero. These long runs of zero in turn compress very efficiently, allowing wavelet compression to deliver substantially better performance than existing Fourier-based methods. Although the lack of standardization has historically been an impediment to widespread adoption of wavelet compression, this situation may begin to change as the operational benefits of the technology become better known. PMID:10165355
NASA Astrophysics Data System (ADS)
Zhang, Yan; Lian, Jijian; Liu, Fang
2016-02-01
Modal parameter identification is a core issue in the health monitoring and damage detection of hydraulic structures. The parameters are mainly obtained from the measured vibrational response under ambient excitation. However, the response signal is mixed with noise and interference signals, which will cover the structure vibration information; therefore, the parameter cannot be identified. This paper proposes an improved filtering method based on an ensemble empirical mode decomposition (EEMD) and wavelet threshold method. A 'noise index' is presented to estimate the noise degree of the components decomposed by the EEMD, and this index is related to the wavelet threshold calculation. In addition, the improved filtering method combined with an eigensystem realization algorithm (ERA) and a singular entropy (SE) is applied to an operational modal identification of a roof overflow powerhouse with a bulb tubular unit.
NASA Astrophysics Data System (ADS)
Bakhouche, A.; Doghmane, N.
2008-06-01
In this paper, a new adaptive watermarking algorithm is proposed for still image based on the wavelet transform. The two major applications for watermarking are protecting copyrights and authenticating photographs. Our robust watermarking [3] [22] is used for copyright protection owners. The main reason for protecting copyrights is to prevent image piracy when the provider distributes the image on the Internet. Embed watermark in low frequency band is most resistant to JPEG compression, blurring, adding Gaussian noise, rescaling, rotation, cropping and sharpening but embedding in high frequency is most resistant to histogram equalization, intensity adjustment and gamma correction. In this paper, we extend the idea to embed the same watermark in two bands (LL and HH bands or LH and HL bands) at the second level of Discrete Wavelet Transform (DWT) decomposition. Our generalization includes all the four bands (LL, HL, LH, and HH) by modifying coefficients of the all four bands in order to compromise between acceptable imperceptibility level and attacks' resistance.
Group-normalized wavelet packet signal processing
NASA Astrophysics Data System (ADS)
Shi, Zhuoer; Bao, Zheng
1997-04-01
Since the traditional wavelet and wavelet packet coefficients do not exactly represent the strength of signal components at the very time(space)-frequency tilling, group- normalized wavelet packet transform (GNWPT), is presented for nonlinear signal filtering and extraction from the clutter or noise, together with the space(time)-frequency masking technique. The extended F-entropy improves the performance of GNWPT. For perception-based image, soft-logic masking is emphasized to remove the aliasing with edge preserved. Lawton's method for complex valued wavelets construction is extended to generate the complex valued compactly supported wavelet packets for radar signal extraction. This kind of wavelet packets are symmetry and unitary orthogonal. Well-defined wavelet packets are chosen by the analysis remarks on their time-frequency characteristics. For real valued signal processing, such as images and ECG signal, the compactly supported spline or bi- orthogonal wavelet packets are preferred for perfect de- noising and filtering qualities.
NASA Astrophysics Data System (ADS)
Gómez Valverde, Juan J.; Ortuño, Juan E.; Guerra, Pedro; Hermann, Boris; Zabihian, Behrooz; Rubio-Guivernau, José L.; Santos, Andrés.; Drexler, Wolfgang; Ledesma-Carbayo, Maria J.
2015-07-01
Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we propose a new speckle reduction process and compare it with various denoising filters with high edge-preserving potential, using several sets of dermatological OCT B-scans. To validate the performance we used a custom-designed spectral domain OCT and two different data set groups. The first group consisted in five datasets of a single B-scan captured N times (with N<20), the second were five 3D volumes of 25 Bscans. As quality metrics we used signal to noise (SNR), contrast to noise (CNR) and equivalent number of looks (ENL) ratios. Our results show that a process based on a combination of a 2D enhanced sigma digital filter and a wavelet compounding method achieves the best results in terms of the improvement of the quality metrics. In the first group of individual B-scans we achieved improvements in SNR, CNR and ENL of 16.87 dB, 2.19 and 328 respectively; for the 3D volume datasets the improvements were 15.65 dB, 3.44 and 1148. Our results suggest that the proposed enhancement process may significantly reduce speckle, increasing SNR, CNR and ENL and reducing the number of extra acquisitions of the same frame.
Discrete wavelet transform-based spatial-temporal approach for quantized video watermarking
NASA Astrophysics Data System (ADS)
Faragallah, Osama S.
2011-07-01
We propose a new public digital watermarking technique for video copyright protection working in the discrete wavelet transform (DWT) domain. The proposed scheme is a combination of spread-spectrum and quantization-based watermarking. The proposed scheme is characterized by two achievements: (i) a spread-spectrum technique is used to spread the power spectrum of the watermark data and (ii) an error correction code is applied and embeds the watermark with spatial and temporal redundancy. The goal of these two achievements is to increase robustness against attacks, protect the watermark against bit errors, and achieve a very good perceptual quality. The effectiveness of the proposed scheme is verified through a series of experiments in which a number of video and standard image-processing attacks are conducted. The proposed scheme achieves a very good perceptual quality with mean peak signal-to-noise-ratio values of the watermarked videos of >40 dB and high resistance to a large spectrum of attacks.
NASA Astrophysics Data System (ADS)
Zauner, G.; Mayr, G.; Hendorfer, G.
2009-02-01
Active infrared thermography is a method for non-destructive testing (NDT) of materials and components. In pulsed thermography (PT), a brief and high intensity flash is used to heat the sample. The decay of the sample surface temperature is detected and recorded by an infrared camera. Any subsurface anomaly (e.g. inclusion, delamination, etc.) gives rise to a local temperature increase (thermal contrast) on the sample surface. Conventionally, in Pulsed Phase Thermography (PPT) the analysis of PT time series is done by means of Discrete Fourier Transform producing phase images which can suppress unwanted physical effects (due to surface emissivity variations or non-uniform heating). The drawback of the Fourier-based approach is the loss of temporal information, making quantitative inversion procedures tricky (e.g. defect depth measurements). In this paper the complex Morlet-Wavelet transform is used to preserve the time information of the signal and thus provides information about the depth of a subsurface defect. Additionally, we propose to use the according phase contrast value to derive supplementary information about the thermal reflection properties at the defect interface. This provides additional information (e.g. about the thermal mismatch factor between the specimen and the defect) making interpretation of PPT results easier and perhaps unequivocal.
Vahabi, Zahra; Amirfattahi, Rasoul; Shayegh, Farzaneh; Ghassemi, Fahimeh
2015-09-01
Considerable efforts have been made in order to predict seizures. Among these methods, the ones that quantify synchronization between brain areas, are the most important methods. However, to date, a practically acceptable result has not been reported. In this paper, we use a synchronization measurement method that is derived according to the ability of bi-spectrum in determining the nonlinear properties of a system. In this method, first, temporal variation of the bi-spectrum of different channels of electro cardiography (ECoG) signals are obtained via an extended wavelet-based time-frequency analysis method; then, to compare different channels, the bi-phase correlation measure is introduced. Since, in this way, the temporal variation of the amount of nonlinear coupling between brain regions, which have not been considered yet, are taken into account, results are more reliable than the conventional phase-synchronization measures. It is shown that, for 21 patients of FSPEEG database, bi-phase correlation can discriminate the pre-ictal and ictal states, with very low false positive rates (FPRs) (average: 0.078/h) and high sensitivity (100%). However, the proposed seizure predictor still cannot significantly overcome the random predictor for all patients. PMID:26126613
A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform
Yu, Bin; Zhang, Yan
2013-01-01
The increasing protein sequences from the genome project require theoretical methods to predict transmembrane helical segments (TMHs). So far, several prediction methods have been reported, but there are some deficiencies in prediction accuracy and adaptability in these methods. In this paper, a method based on discrete wavelet transform (DWT) has been developed to predict the number and location of TMHs in membrane proteins. PDB coded as 1KQG is chosen as an example to describe the prediction process by this method. 80 proteins with known 3D structure from Mptopo database are chosen at random as data sets (including 325 TMHs) and 80 sequences are divided into 13 groups according to their function and type. TMHs prediction is carried out for each group of membrane protein sequences and obtain satisfactory result. To verify the feasibility of this method, 80 membrane protein sequences are treated as test sets, 308 TMHs can be predicted and the prediction accuracy is 96.3%. Compared with the main prediction results of seven popular prediction methods, the obtained results indicate that the proposed method in this paper has higher prediction accuracy. PMID:23289014
Mousavi, Seyed Mortaza; Adamoğlu, Ahmet; Demiralp, Tamer; Shayesteh, Mahrokh G.
2014-01-01
Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. We analyzed EEG changes for phase coupling between delta and alpha subbands using a new algorithm for depth of general anesthesia measurement based on complex wavelet transform (CWT) in patients anesthetized by Propofol. Entropy and histogram of modulated signals were calculated by taking bispectral index (BIS) values as reference. Entropies corresponding to different BIS intervals using Mann-Whitney U test showed that they had different continuous distributions. The results demonstrated that there is a phase coupling between 3 and 4 Hz in delta and 8-9 Hz in alpha subbands and these changes are shown better at the channel T7 of EEG. Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented. PMID:25276220
Ye, Linlin; Yang, Dan; Wang, Xu
2014-06-01
A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG denoising and meanwhile keep the characteristics of original ECG signal. PMID:25219236
NASA Astrophysics Data System (ADS)
Zhang, Xingwu; Gao, Robert X.; Yan, Ruqiang; Chen, Xuefeng; Sun, Chuang; Yang, Zhibo
2016-08-01
Crack is one of the crucial causes of structural failure. A methodology for quantitative crack identification is proposed in this paper based on multivariable wavelet finite element method and particle swarm optimization. First, the structure with crack is modeled by multivariable wavelet finite element method (MWFEM) so that the vibration parameters of the first three natural frequencies in arbitrary crack conditions can be obtained, which is named as the forward problem. Second, the structure with crack is tested to obtain the vibration parameters of first three natural frequencies by modal testing and advanced vibration signal processing method. Then, the analyzed and measured first three natural frequencies are combined together to obtain the location and size of the crack by using particle swarm optimization. Compared with traditional wavelet finite element method, MWFEM method can achieve more accurate vibration analysis results because it interpolates all the solving variables at one time, which makes the MWFEM-based method to improve the accuracy in quantitative crack identification. In the end, the validity and superiority of the proposed method are verified by experiments of both cantilever beam and simply supported beam.
Paul, Sabyasachi; Suman, V; Sarkar, P K; Ranade, A K; Pulhani, V; Dafauti, S; Datta, D
2013-08-01
A wavelet transform based denoising methodology has been applied to detect the presence of any discernable trend in (137)Cs and (90)Sr 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. PMID:23524202
NASA Astrophysics Data System (ADS)
Zhang, Xin; Zhang, Haijiang
2015-10-01
It has been a challenge to image velocity changes in real time by seismic travel time tomography. If more seismic events are included in the tomographic system, the inverted velocity models do not have necessary time resolution to resolve velocity changes. But if fewer events are used for real-time tomography, the system is less stable and the inverted model may contain some artifacts, and thus, resolved velocity changes may not be real. To mitigate these issues, we propose a wavelet-based time-dependent double-difference (DD) tomography method. The new method combines the multiscale property of wavelet representation and the fast converging property of the simultaneous algebraic reconstruction technique to solve the velocity models at multiple scales for sequential time segments. We first test the new method using synthetic data constructed using real event and station distribution for Mount Etna volcano in Italy. Then we show its effectiveness to determine velocity changes for the 2001 and 2002 eruptions of Mount Etna volcano. Compared to standard DD tomography that uses seismic events from a longer time period, wavelet-based time-dependent tomography better resolves velocity changes that may be caused by fracture closure and opening as well as fluid migration before and after volcano eruptions.
Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy
NASA Astrophysics Data System (ADS)
Han, Long; Li, Cheng Wei; Guo, Song Lin; Su, Xun Wen
2015-10-01
In order to accomplish the feature extraction from a mixed fault signal of bearings, this paper proposes a feature extraction method based on the improved Fast-ICA algorithm and the wavelet packet energy spectrum. The conventional fast-ICA algorithm can only separate the mixed signals, while the convergence speed is relatively slow and the convergence effect is not sufficient. The method of the third-order Newton iteration is adopted in this paper to improve the Fast-ICA algorithm. Moreover, the improved Fast-ICA algorithm is confirmed to have a faster convergence speed and higher precision than the conventional Fast-ICA algorithm. The improved Fast-ICA algorithm is applied to separate the acoustic emission signal in which two kinds of fault components are comprised. The wavelet packet energy spectrum is used to extract the feature information in the separated samples. In addition, the fault diagnosis is performed based on the SVM algorithm. It is confirmed that the slight damage and fracture of a bearing can accurately be recognized. The results show that the improved FAST-ICA and wavelet packet energy method in feature extraction is sufficiently effective.
Spectral Laplace-Beltrami wavelets with applications in medical images.
Tan, Mingzhen; Qiu, Anqi
2015-05-01
The spectral graph wavelet transform (SGWT) has recently been developed to compute wavelet transforms of functions defined on non-Euclidean spaces such as graphs. By capitalizing on the established framework of the SGWT, we adopt a fast and efficient computation of a discretized Laplace-Beltrami (LB) operator that allows its extension from arbitrary graphs to differentiable and closed 2-D manifolds (smooth surfaces embedded in the 3-D Euclidean space). This particular class of manifolds are widely used in bioimaging to characterize the morphology of cells, tissues, and organs. They are often discretized into triangular meshes, providing additional geometric information apart from simple nodes and weighted connections in graphs. In comparison with the SGWT, the wavelet bases constructed with the LB operator are spatially localized with a more uniform "spread" with respect to underlying curvature of the surface. In our experiments, we first use synthetic data to show that traditional applications of wavelets in smoothing and edge detectio can be done using the wavelet bases constructed with the LB operator. Second, we show that multi-resolutional capabilities of the proposed framework are applicable in the classification of Alzheimer's patients with normal subjects using hippocampal shapes. Wavelet transforms of the hippocampal shape deformations at finer resolutions registered higher sensitivity (96%) and specificity (90%) than the classification results obtained from the direct usage of hippocampal shape deformations. In addition, the Laplace-Beltrami method requires consistently a smaller number of principal components (to retain a fixed variance) at higher resolution as compared to the binary and weighted graph Laplacians, demonstrating the potential of the wavelet bases in adapting to the geometry of the underlying manifold. PMID:25343758
Smart-phone based electrocardiogram wavelet decomposition and neural network classification
NASA Astrophysics Data System (ADS)
Jannah, N.; Hadjiloucas, S.; Hwang, F.; Galvão, R. K. H.
2013-06-01
This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.
Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions.
Cowlagi, Raghvendra V; Tsiotras, Panagiotis
2012-10-01
We present a path- and motion-planning scheme that is "multiresolution" both in the sense of representing the environment with high accuracy only locally and in the sense of addressing the vehicle kinematic and dynamic constraints only locally. The proposed scheme uses rectangular multiresolution cell decompositions, efficiently generated using the wavelet transform. The wavelet transform is widely used in signal and image processing, with emerging applications in autonomous sensing and perception systems. The proposed motion planner enables the simultaneous use of the wavelet transform in both the perception and in the motion-planning layers of vehicle autonomy, thus potentially reducing online computations. We rigorously prove the completeness of the proposed path-planning scheme, and we provide numerical simulation results to illustrate its efficacy. PMID:22581136
Best parameters selection for wavelet packet-based compression of magnetic resonance images.
Abu-Rezq, A N; Tolba, A S; Khuwaja, G A; Foda, S G
1999-10-01
Transmission of compressed medical images is becoming a vital tool in telemedicine. Thus new methods are needed for efficient image compression. This study discovers the best design parameters for a data compression scheme applied to digital magnetic resonance (MR) images. The proposed technique aims at reducing the transmission cost while preserving the diagnostic information. By selecting the wavelet packet's filters, decomposition level, and subbands that are better adapted to the frequency characteristics of the image, one may achieve better image representation in the sense of lower entropy or minimal distortion. Experimental results show that the selection of the best parameters has a dramatic effect on the data compression rate of MR images. In all cases, decomposition at three or four levels with the Coiflet 5 wavelet (Coif 5) results in better compression performance than the other wavelets. Image resolution is found to have a remarkable effect on the compression rate. PMID:10529302
Avci, Derya; Dogantekin, Akif
2016-01-01
Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%. PMID:27274882