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Sample records for based multi wavelet

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

    PubMed

    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

  2. An Investigation of Wavelet Bases for Grid-Based Multi-Scale Simulations Final Report

    SciTech Connect

    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.

  3. 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

  4. 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.

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

    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

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

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2007-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2008-01-01

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

  9. Wavelet-based detection of bush encroachment in a savanna using multi-temporal aerial photographs and satellite imagery

    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.

  10. Wavelet-Based Grid Generation

    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.

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

    PubMed

    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.

  12. 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.

  13. [Study on application of multi-spectral image texture to discriminating rice categories based on wavelet packet and support vector machine].

    PubMed

    Chen, Xiao-Jing; Wu, Di; He, Yong; Liu, Shou

    2009-01-01

    Based on multi-spectral digital image texture feature, a new rapid and nondestructive method for discriminating rice categories was put forward. The new method combined the advantages of wavelet packet and support vector machine (SVM). In the present study, the images which are 1 036 pixels in vertical direction by 1,384 pixels in horizontal direction with 24-bit depth were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multi-spectral digital imager. The three wavebands of image (red, green and NIR) can be composed into one image which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. NIR waveband images were decomposed to 16 subbands using three wavelet packet multi-resolution. Because the main feature of texture information is concentrated on the middle frequency, the 8 subbands of middle frequency were selected to calculate entropy, and the entropy of three wavebands of original image was calculated at the same time. Eighty images (twenty for each category) were used for calibration set and eighty images (twenty for each category) were used as the prediction set. Then the rice categories were classified by SVM. The classification rate of rice categories was only 93.75% using the entropy of original image, but reached 100% by wavelet packet decomposition. The overall results show that the technique combining wavelet packet and support vector machine can be efficiently utilized for texture recognition of multi-spectra, and is an effective and simple technique for discriminating the rice categories. This study also provides a foundation for rice grading and other rice industry processing such as quality diction and milling degree.

  14. 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.

  15. 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.

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

    PubMed

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

    2006-09-01

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

  17. Wavelet-based Evapotranspiration Forecasts

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  18. 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.

  19. Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images

    PubMed Central

    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

  20. 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.

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

    PubMed Central

    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

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

    PubMed

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

    2012-01-01

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

  3. Wavelet methods in multi-conjugate adaptive optics

    NASA Astrophysics Data System (ADS)

    Helin, T.; Yudytskiy, M.

    2013-08-01

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

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

    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

  5. Wavelet-based multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Liu, Dian-Ting; Zhou, Xiao-Dan; Wang, Cheng-Wen

    2008-09-01

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

  6. 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.

  7. Multi-level basis selection of wavelet packet decomposition tree for heart sound classification.

    PubMed

    Safara, Fatemeh; Doraisamy, Shyamala; Azman, Azreen; Jantan, Azrul; Abdullah Ramaiah, Asri Ranga

    2013-10-01

    Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.

  8. 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.

  9. Wavelet-based acoustic recognition of aircraft

    SciTech Connect

    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.

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

    NASA Astrophysics Data System (ADS)

    Xu, Fan; Wang, Yuanqing

    2015-11-01

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

  11. 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.

  12. 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.

  13. Efficient VLSI architecture for multi-dimensional discrete wavelet transform

    NASA Astrophysics Data System (ADS)

    Xiong, Cheng-Yi; Tian, Jin-Wen; Liu, Jian

    2005-10-01

    Efficient VLSI architectures for multi-dimensional (m-D) discrete wavelet transform (DWT), e.g. m=2, 3, are presented, in which the lifting scheme of DWT is used to reduce efficiently hardware complexity. The parallelism of 2m subbands transforms in lifting-based m-D DWT is explored, which increases efficiently the throughput rate of separable m-D DWT. The proposed architecture is composed of m2m-1 1-D DWT modules working in parallel and pipelined, which is designed to process 2m input samples per clock cycle, and generate 2m subbands coefficients synchronously. The total time of computing one level of decomposition for a 2-D image (3-D image sequence) of size N2 (MN2) is approximately N2/4 (MN2/8) intra- clock cycles (ccs). An efficient line-based architecture framework for both 2D+t and t+2D 3-D DWT is first proposed. Compared with the similar works reported in previous literature, the proposed architecture has good performance in terms of production of computation time and hardware cost. The proposed architecture is simple, regular, scalable and well suited for VLSI implementation.

  14. 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.

  15. EEG analysis using wavelet-based information tools.

    PubMed

    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.

  16. Improved total variation algorithms for wavelet-based denoising

    NASA Astrophysics Data System (ADS)

    Easley, Glenn R.; Colonna, Flavia

    2007-04-01

    Many improvements of wavelet-based restoration techniques suggest the use of the total variation (TV) algorithm. The concept of combining wavelet and total variation methods seems effective but the reasons for the success of this combination have been so far poorly understood. We propose a variation of the total variation method designed to avoid artifacts such as oil painting effects and is more suited than the standard TV techniques to be implemented with wavelet-based estimates. We then illustrate the effectiveness of this new TV-based method using some of the latest wavelet transforms such as contourlets and shearlets.

  17. 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.

  18. 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.

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

    PubMed

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

    2003-06-01

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

  20. 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.

  1. 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.

  2. Facial Feature Extraction Based on Wavelet Transform

    NASA Astrophysics Data System (ADS)

    Hung, Nguyen Viet

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

  3. Wavelet based image visibility enhancement of IR images

    NASA Astrophysics Data System (ADS)

    Jiang, Qin; Owechko, Yuri; Blanton, Brendan

    2016-05-01

    Enhancing the visibility of infrared images obtained in a degraded visibility environment is very important for many applications such as surveillance, visual navigation in bad weather, and helicopter landing in brownout conditions. In this paper, we present an IR image visibility enhancement system based on adaptively modifying the wavelet coefficients of the images. In our proposed system, input images are first filtered by a histogram-based dynamic range filter in order to remove sensor noise and convert the input images into 8-bit dynamic range for efficient processing and display. By utilizing a wavelet transformation, we modify the image intensity distribution and enhance image edges simultaneously. In the wavelet domain, low frequency wavelet coefficients contain original image intensity distribution while high frequency wavelet coefficients contain edge information for the original images. To modify the image intensity distribution, an adaptive histogram equalization technique is applied to the low frequency wavelet coefficients while to enhance image edges, an adaptive edge enhancement technique is applied to the high frequency wavelet coefficients. An inverse wavelet transformation is applied to the modified wavelet coefficients to obtain intensity images with enhanced visibility. Finally, a Gaussian filter is used to remove blocking artifacts introduced by the adaptive techniques. Since wavelet transformation uses down-sampling to obtain low frequency wavelet coefficients, histogram equalization of low-frequency coefficients is computationally more efficient than histogram equalization of the original images. We tested the proposed system with degraded IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of degraded IR images.

  4. Wavelet-based approach to character skeleton.

    PubMed

    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.

  5. Carriage Error Identification Based on Cross-Correlation Analysis and Wavelet Transformation

    PubMed Central

    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

  6. 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

  7. Wavelet based feature extraction and visualization in hyperspectral tissue characterization

    PubMed Central

    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

  8. 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.

  9. 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.

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

    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.

  11. Wavelet-based regularity analysis reveals Recurrent Spatiotemporal Behavior in Resting-state fMRI

    PubMed Central

    Smith, Robert X.; Jann, Kay; Ances, Beau; Wang, Danny J.J.

    2015-01-01

    One of the major findings from multi-modal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain’s spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform (SWT) to preserve signal structure, followed by construction of “lagged” subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an “objective” estimate of noise level at each scale. We found that the brain’s default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared to standard multiscale entropy analysis. Wavelet based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals. PMID:26096080

  12. A wavelet-based baseline drift correction method for grounded electrical source airborne transient electromagnetic signals

    NASA Astrophysics Data System (ADS)

    Wang, Yuan 1Ji, Yanju 2Li, Suyi 13Lin, Jun 12Zhou, Fengdao 1Yang, Guihong

    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

  13. [Application of kalman filtering based on wavelet transform in ICP-AES].

    PubMed

    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

  14. Wavelet-based compression of multichannel climate data

    NASA Astrophysics Data System (ADS)

    Sharifahmadian, Ershad; Choi, Yoonsuk; Latifi, Shahram; Dascalu, Sergiu; Harris, Frederick C.

    2014-05-01

    To simultaneously compress multichannel climate data, the Wavelet Subbands Arranging Technique (WSAT) is studied. The proposed technique is based on the wavelet transform, and has been designed to improve the transmission of voluminous climate data. The WSAT method significantly reduces the number of transmitted or stored bits in a bit stream, and preserves required quality. In the proposed technique, the arranged wavelet subbands of input channels provide more efficient compression for multichannel climate data due to building appropriate parent-offspring relations among wavelet coefficients. To test and evaluate the proposed technique, data from the Nevada climate change database is utilized. Based on results, the proposed technique can be an appropriate choice for the compression of multichannel climate data with significantly high compression ratio at low error.

  15. [ECoG classification based on wavelet variance].

    PubMed

    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

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

    PubMed

    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

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

    PubMed

    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.

  18. 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

  19. Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

    PubMed Central

    Khullar, Siddharth; Michael, Andrew; Correa, Nicolle; Adali, Tulay; Baum, Stefi A.; Calhoun, Vince D.

    2010-01-01

    We present a novel integrated wavelet-domain based framework (w-ICA) for 3-D de-noising functional magnetic resonance imaging (fMRI) data followed by source separation analysis using independent component analysis (ICA) in the wavelet domain. We propose the idea of a 3-D wavelet-based multi-directional de-noising scheme where each volume in a 4-D fMRI data set is sub-sampled using the axial, sagittal and coronal geometries to obtain three different slice-by-slice representations of the same data. The filtered intensity value of an arbitrary voxel is computed as an expected value of the de-noised wavelet coefficients corresponding to the three viewing geometries for each sub-band. This results in a robust set of de-noised wavelet coefficients for each voxel. Given the decorrelated nature of these de-noised wavelet coefficients; it is possible to obtain more accurate source estimates using ICA in the wavelet domain. The contributions of this work can be realized as two modules. First, the analysis module where we combine a new 3-D wavelet denoising approach with better signal separation properties of ICA in the wavelet domain, to yield an activation component that corresponds closely to the true underlying signal and is maximally independent with respect to other components. Second, we propose and describe two novel shape metrics for post-ICA comparisons between activation regions obtained through different frameworks. We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA). The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic (ROC) curves. It was observed that the proposed framework was able to preserve the actual activation shape in a consistent manner even for very high noise levels in addition to significant reduction in false

  20. Predicting apple tree leaf nitrogen content based on hyperspectral applying wavelet and wavelet packet analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Yao; Zheng, Lihua; Li, Minzan; Deng, Xiaolei; Sun, Hong

    2012-11-01

    The visible and NIR spectral reflectance were measured for apple leaves by using a spectrophotometer in fruit-bearing, fruit-falling and fruit-maturing period respectively, and the nitrogen content of each sample was measured in the lab. The analysis of correlation between nitrogen content of apple tree leaves and their hyperspectral data was conducted. Then the low frequency signal and high frequency noise reduction signal were extracted by using wavelet packet decomposition algorithm. At the same time, the original spectral reflectance was denoised taking advantage of the wavelet filtering technology. And then the principal components spectra were collected after PCA (Principal Component Analysis). It was known that the model built based on noise reduction principal components spectra reached higher accuracy than the other three ones in fruit-bearing period and physiological fruit-maturing period. Their calibration R2 reached 0.9529 and 0.9501, and validation R2 reached 0.7285 and 0.7303 respectively. While in the fruit-falling period the model based on low frequency principal components spectra reached the highest accuracy, and its calibration R2 reached 0.9921 and validation R2 reached 0.6234. The results showed that it was an effective way to improve ability of predicting apple tree nitrogen content based on hyperspectral analysis by using wavelet packet algorithm.

  1. A Wavelet-Based Methodology for Grinding Wheel Condition Monitoring

    SciTech Connect

    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.

  2. Wavelet transform based on the optimal wavelet pairs for tunable diode laser absorption spectroscopy signal processing.

    PubMed

    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.

  3. Wavelet-based analysis of circadian behavioral rhythms.

    PubMed

    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

  4. 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.

  5. 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.

  6. Simulation-based design using wavelets

    NASA Astrophysics Data System (ADS)

    Williams, John R.; Amaratunga, Kevin S.

    1994-03-01

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

  7. Raman spectral data denoising based on wavelet analysis

    NASA Astrophysics Data System (ADS)

    Chen, Chen; Peng, Fei; Cheng, Qinghua; Xu, Dahai

    2008-12-01

    Abstract As one kind of molecule scattering spectroscopy, Raman spectroscopy (RS) is characterized by the frequency excursion that can show the information of molecule. RS has a broad application in biological, chemical, environmental and industrial fields. But signals in Raman spectral analysis often have noise, which greatly influences the achievement of accurate analytical results. The de-noising of RS signals is an important part of spectral analysis. Wavelet transform has been established with the Fourier transform as a data-processing method in analytical fields. The main fields of application are related to de-noising, compression, variable reduction, and signal suppression. In de-noising of Raman Spectroscopy, wavelet is chosen to construct de-noising function because of its excellent properties. In this paper, bior wavelet is adopted to remove the noise in the Raman spectra. It eliminates noise obviously and the result is satisfying. This method can provide some bases for practical de-noising in Raman spectra.

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. 3D Wavelet-Based Filter and Method

    DOEpatents

    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.

  13. Enhancing Hyperspectral Data Throughput Utilizing Wavelet-Based Fingerprints

    SciTech Connect

    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.

  14. 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.

  15. 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.

  16. Fast wavelet based sparse approximate inverse preconditioner

    SciTech Connect

    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.

  17. 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.

  18. A Wavelet-Based Approach to Fall Detection

    PubMed Central

    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

  19. A wavelet-based approach to fall detection.

    PubMed

    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

  20. 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.

  1. 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.

  2. Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet

    PubMed Central

    Nguyen, Nha; Huang, Heng; Oraintara, Soontorn; Vo, An

    2010-01-01

    Motivation: Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise. Results: We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results. Availability: http://ranger.uta.edu/∼heng/MS/GDWavelet.html or http://www.naaan.org/nhanguyen/archive.htm Contact: heng

  3. Analysis of a wavelet-based robust hash algorithm

    NASA Astrophysics Data System (ADS)

    Meixner, Albert; Uhl, Andreas

    2004-06-01

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

  4. An optical image segmentor using neural based wavelet filtering techniques

    NASA Astrophysics Data System (ADS)

    Veronin, Christopher P.; Rogers, Steven K.; Kabrisky, Matthew; Priddy, Kevin L.; Ayer, Kevin W.

    1991-10-01

    This paper presents a neural based optical image segmentation scheme for locating potential targets in cluttered FLIR images. The advantage of such a scheme is speed, i.e., the speed of light. Such a design is critical to achieve real-time segmentation and classification for machine vision applications. The segmentation scheme used was based on texture discrimination and employed biologically based orientation specific filters (wavelet filters) as its main component. These filters are well understood impulse response functions of mammalian vision systems from input to striate cortex. By using the proper choice of aperture pair separation, dilation, and orientation, targets in FLIR imagery were optically segmented. Wavelet filtering is illustrated for glass template slides, as well as segmentation for static and real-time FLIR imagery displayed on a liquid crystal television.

  5. Optical image segmentation using neural-based wavelet filtering techniques

    NASA Astrophysics Data System (ADS)

    Veronin, Christopher P.; Priddy, Kevin L.; Rogers, Steven K.; Ayer, Kevin W.; Kabrisky, Matthew; Welsh, Byron M.

    1992-02-01

    This paper presents a neural based optical image segmentation scheme for locating potential targets in cluttered FLIR images. The advantage of such a scheme is speed, i.e., the speed of light. Such a design is critical to achieve real-time segmentation and classification for machine vision applications. The segmentation scheme used was based on texture discrimination and employed biologically based orientation specific filters (wavelet filters) as its main component. These filters are well understood impulse response functions of mammalian vision systems from input to striate cortex. By using the proper choice of aperture pair separation, dilation, and orientation, targets in FLIR imagery were optically segmented. Wavelet filtering is illustrated for glass template slides, as well as segmentation for static and real-time FLIR imagery displayed on a liquid crystal television.

  6. 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.

  7. Multi-Resolution and Wavelet Representations for Identifying Signatures of Disease

    PubMed Central

    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

  8. 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.

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

    PubMed

    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.

  10. 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 .

  11. 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.

  12. 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.

  13. 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).

  14. Pulsar Signal Denoising Method Based on Laplace Distribution in No-subsampling Wavelet Packet Domain

    NASA Astrophysics Data System (ADS)

    Wenbo, Wang; Yanchao, Zhao; Xiangli, Wang

    2016-11-01

    In order to improve the denoising effect of the pulsar signal, a new denoising method is proposed in the no-subsampling wavelet packet domain based on the local Laplace prior model. First, we count the true noise-free pulsar signal’s wavelet packet coefficient distribution characteristics and construct the true signal wavelet packet coefficients’ Laplace probability density function model. Then, we estimate the denosied wavelet packet coefficients by using the noisy pulsar wavelet coefficients based on maximum a posteriori criteria. Finally, we obtain the denoisied pulsar signal through no-subsampling wavelet packet reconstruction of the estimated coefficients. The experimental results show that the proposed method performs better when calculating the pulsar time of arrival than the translation-invariant wavelet denoising method.

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

    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.

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

    PubMed

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

    2013-01-01

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

  17. 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.

  18. 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.

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

    PubMed

    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

  20. Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

    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.

  1. 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

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

    PubMed

    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.

  3. An Evolved Wavelet Library Based on Genetic Algorithm

    PubMed Central

    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

  4. Interpretations of gravity and magnetic anomalies in the Songliao Basin with Wavelet Multi-scale Decomposition

    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.

  5. 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.

  6. Wavelet based hyperspectral image restoration using spatial and spectral penalties

    NASA Astrophysics Data System (ADS)

    Rasti, Behnood; Sveinsson, Johannes R.; Ulfarsson, Magnus O.; Benediktsson, Jon A.

    2013-10-01

    In this paper a penalized least squares cost function with a new spatial-spectral penalty is proposed for hyper- spectral image restoration. The new penalty is a combination of a Group LASSO (GLASSO) and First Order Roughness Penalty (FORP) in the wavelet domain. The restoration criterion is solved using the Alternative Direction Method of Multipliers (ADMM). The results are compared with other restoration methods where the proposed method outperforms them for the simulated noisy data set based on Signal to Noise Ratio (SNR) and visually outperforms them on a real degraded data set.

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

    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.

  8. 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.

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

    SciTech Connect

    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.

  10. Sensitivity Analysis of Wavelet-based Approach to Multiresolution-Characterization and Scaling of Two-Dimensional Heterogeneous Fields

    NASA Astrophysics Data System (ADS)

    Hyun, Y.; Ahn, Y.

    2012-12-01

    A wavelet-based scaling approach has recently been used to characterize and/or upscale hydro-geologic variables with given Hurst coefficient, characteristic length scale, and orientation. A wavelet-based approach requires specifying a mother wavelet for wavelet analysis. We perform the sensitivity analysis of wavelet transforms to several types of mother wavelets in characterizing and scaling two-dimensional random fractal fields which are theoretically generated for various Hurst coefficient, characteristic lengths, and orientations. We use haar, Daubechies, Symlets, and Coiflets wavelets and compare the results. The numerical studies are carried out using Matlab wavelet toolbox. Results show that the Daubechies wavelet is most suitable for scaling random fractal fields with among various wavelets. In characterization of heterogeneous fields on a multiresolution, characteristic lengths inferred from simulated fields vary with mother wavelets. This study suggests that one should be careful in choosing a mother wavelet function for scaling studies by means of wavelet-based analyses for reliable results and no reliable results are expected for characterizing fractal fields on a multiresolution with various mother wavelets.

  11. 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.

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

    PubMed Central

    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

  13. 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.

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

    PubMed

    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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. A wavelet-based method for multispectral face recognition

    NASA Astrophysics Data System (ADS)

    Zheng, Yufeng; Zhang, Chaoyang; Zhou, Zhaoxian

    2012-06-01

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

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

    PubMed

    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

  1. Wavelet-based characterization of gait signal for neurological abnormalities.

    PubMed

    Baratin, E; Sugavaneswaran, L; Umapathy, K; Ioana, C; Krishnan, S

    2015-02-01

    Studies conducted by the World Health Organization (WHO) indicate that over one billion suffer from neurological disorders worldwide, and lack of efficient diagnosis procedures affects their therapeutic interventions. Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders. PMID:25661004

  2. 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.

  3. An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms

    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.

  4. Adaptive directional wavelet transform based on directional prefiltering.

    PubMed

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

    2010-04-01

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

  5. 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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

    Campisi, Patrizio; Longari, Andrea; Neri, Alessandro

    1999-10-01

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

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

    PubMed

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

    2013-02-01

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

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

    NASA Astrophysics Data System (ADS)

    Hester, D.; González, A.

    2012-04-01

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

  10. Exploring the Multi-Scale Statistical Analysis of Ionospheric Scintillation via Wavelets and Empirical Mode Decomposition

    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.

  11. Estimating cognitive workload using wavelet entropy-based features during an arithmetic task.

    PubMed

    Zarjam, Pega; Epps, Julien; Chen, Fang; Lovell, Nigel H

    2013-12-01

    Electroencephalography (EEG) has shown promise as an indicator of cognitive workload; however, precise workload estimation is an ongoing research challenge. In this investigation, seven levels of workload were induced using an arithmetic task, and the entropy of wavelet coefficients extracted from EEG signals is shown to distinguish all seven levels. For a subject-independent multi-channel classification scheme, the entropy features achieved high accuracy, up to 98% for channels from the frontal lobes, in the delta frequency band. This suggests that a smaller number of EEG channels in only one frequency band can be deployed for an effective EEG-based workload classification system. Together with analysis based on phase locking between channels, these results consistently suggest increased synchronization of neural responses for higher load levels. PMID:24290935

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

    PubMed

    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

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

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-06-01

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

  15. A comparison of spectral decorrelation techniques and performance evaluation metrics for a wavelet-based, multispectral data compression algorithm

    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.

  16. 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.

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

    SciTech Connect

    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.

  18. Energy-based wavelet de-noising of hydrologic time series.

    PubMed

    Sang, Yan-Fang; Liu, Changming; Wang, Zhonggen; Wen, Jun; Shang, Lunyu

    2014-01-01

    De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed.

  19. A criterion for signal-based selection of wavelets for denoising intrafascicular nerve recordings.

    PubMed

    Kamavuako, Ernest Nlandu; Jensen, Winnie; Yoshida, Ken; Kurstjens, Mathijs; Farina, Dario

    2010-02-15

    In this paper we propose a novel method for denoising intrafascicular nerve signals with the aim of improving action potential (AP) detection. The method is based on the stationary wavelet transform and thresholding of the wavelet coefficients. Since the choice of the mother wavelet substantially impact the performance, a criterion is proposed for selecting the optimal wavelet. The criterion for selection was based on the root mean square of the average of the output signal triggered by the detected APs. The mother wavelet was parameterized through the scaling filter, which allowed optimization through the proposed criterion. The method was tested on simulated signals and on experimental neural recordings. Experimental signals were recorded from the tibial branch of the sciatic nerve of three anaesthetized New Zealand white rabbits during controlled muscle stretches. The simulation results showed that the proposed method had an equivalent effect on AP detection performance (percentage of correct detection at 6 dB signal-to-noise ratio, mean+/-SD, 95.3+/-5.2%) to the a-posteriori choice of the best wavelet (96.1+/-3.6). Moreover, the AP detection after the proposed denoising method resulted in a correlation of 0.94+/-0.02 between the estimated spike rate and the muscle length. Therefore, the study proposes an effective method for selecting the optimal mother wavelet for denoising neural signals with the aim of improving AP detection.

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

    NASA Astrophysics Data System (ADS)

    Bhowmik, Deepayan; Abhayaratne, Charith

    2009-02-01

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

  1. Atmospheric turbulence mitigation using complex wavelet-based fusion.

    PubMed

    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.

  2. 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.

  3. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion.

    PubMed

    Wang, Gang; Wang, Zhizhong; Chen, Weiting; Zhuang, Jun

    2006-10-01

    In this paper we present an optimal wavelet packet (OWP) method based on Davies-Bouldin criterion for the classification of surface electromyographic signals. To reduce the feature dimensionality of the outputs of the OWP decomposition, the principle components analysis was employed. Then we chose a neural network classifier to discriminate four types of prosthesis movements. The proposed method achieved a mean classification accuracy of 93.75%, which outperformed the method using the energy of wavelet packet coefficients (with mean classification accuracy 86.25%) and the fuzzy wavelet packet method (87.5%).

  4. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  6. Comparison of two wavelet-based tools for data mining of urban water networks time series.

    PubMed

    Villez, K; Pelletier, G; Rosén, C; Anctil, F; Duchesne, C; Vanrolleghem, P A

    2007-01-01

    In this paper, two approaches to data mining of time series have been tested and compared. Both methods are based on the wavelet decomposition of data series and allow the localization of important characteristics of a time series in both the time and frequency domain. The first method is a common method based on the analysis of wavelet power spectra. The second approach is new to the applied field of urban water networks and provides a qualitative description of the data series based on the cubic spline wavelet decomposition of the data. It is shown that wavelet power spectra indicate important and basic characteristics of the data but fail to provide detailed information of the underlying phenomena. In contrast, the second method allows the extraction of more and more detailed information that is important in a context of process monitoring and diagnosis.

  7. [Detection of reducing sugar content of potato granules based on wavelet compression by near infrared spectroscopy].

    PubMed

    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

  8. An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform

    NASA Astrophysics Data System (ADS)

    Kim, Jonghoon; Cho, B. H.

    2014-08-01

    This paper introduces an innovative approach to analyze electrochemical characteristics and state-of-health (SOH) diagnosis of a Li-ion cell based on the discrete wavelet transform (DWT). In this approach, the DWT has been applied as a powerful tool in the analysis of the discharging/charging voltage signal (DCVS) with non-stationary and transient phenomena for a Li-ion cell. Specifically, DWT-based multi-resolution analysis (MRA) is used for extracting information on the electrochemical characteristics in both time and frequency domain simultaneously. Through using the MRA with implementation of the wavelet decomposition, the information on the electrochemical characteristics of a Li-ion cell can be extracted from the DCVS over a wide frequency range. Wavelet decomposition based on the selection of the order 3 Daubechies wavelet (dB3) and scale 5 as the best wavelet function and the optimal decomposition scale is implemented. In particular, this present approach develops these investigations one step further by showing low and high frequency components (approximation component An and detail component Dn, respectively) extracted from variable Li-ion cells with different electrochemical characteristics caused by aging effect. Experimental results show the clearness of the DWT-based approach for the reliable diagnosis of the SOH for a Li-ion cell.

  9. 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.

  10. 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.

  11. 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.

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

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    PubMed

    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.

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

    PubMed

    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

  16. 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.

  17. 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)

  18. 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.

  19. 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.

  20. Content-based image classification with circular harmonic wavelets

    NASA Astrophysics Data System (ADS)

    Jacovitti, Giovanni; Neri, Alessandro

    1998-07-01

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

  1. 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.

  2. 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.

  3. Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning

    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.

  4. 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.

  5. Wavelet-based motion artifact removal for electrodermal activity.

    PubMed

    Chen, Weixuan; Jaques, Natasha; Taylor, Sara; Sano, Akane; Fedor, Szymon; Picard, Rosalind W

    2015-01-01

    Electrodermal activity (EDA) recording is a powerful, widely used tool for monitoring psychological or physiological arousal. However, analysis of EDA is hampered by its sensitivity to motion artifacts. We propose a method for removing motion artifacts from EDA, measured as skin conductance (SC), using a stationary wavelet transform (SWT). We modeled the wavelet coefficients as a Gaussian mixture distribution corresponding to the underlying skin conductance level (SCL) and skin conductance responses (SCRs). The goodness-of-fit of the model was validated on ambulatory SC data. We evaluated the proposed method in comparison with three previous approaches. Our method achieved a greater reduction of artifacts while retaining motion-artifact-free data.

  6. Robust rate-control for wavelet-based image coding via conditional probability models.

    PubMed

    Gaubatz, Matthew D; Hemami, Sheila S

    2007-03-01

    Real-time rate-control for wavelet image coding requires characterization of the rate required to code quantized wavelet data. An ideal robust solution can be used with any wavelet coder and any quantization scheme. A large number of wavelet quantization schemes (perceptual and otherwise) are based on scalar dead-zone quantization of wavelet coefficients. A key to performing rate-control is, thus, fast, accurate characterization of the relationship between rate and quantization step size, the R-Q curve. A solution is presented using two invocations of the coder that estimates the slope of each R-Q curve via probability modeling. The method is robust to choices of probability models, quantization schemes and wavelet coders. Because of extreme robustness to probability modeling, a fast approximation to spatially adaptive probability modeling can be used in the solution, as well. With respect to achieving a target rate, the proposed approach and associated fast approximation yield average percentage errors around 0.5% and 1.0% on images in the test set. By comparison, 2-coding-pass rho-domain modeling yields errors around 2.0%, and post-compression rate-distortion optimization yields average errors of around 1.0% at rates below 0.5 bits-per-pixel (bpp) that decrease down to about 0.5% at 1.0 bpp; both methods exhibit more competitive performance on the larger images. The proposed method and fast approximation approach are also similar in speed to the other state-of-the-art methods. In addition to possessing speed and accuracy, the proposed method does not require any training and can maintain precise control over wavelet step sizes, which adds flexibility to a wavelet-based image-coding system.

  7. 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.

  8. Comparisom of Wavelet-Based and Hht-Based Feature Extraction Methods for Hyperspectral Image Classification

    NASA Astrophysics Data System (ADS)

    Huang, X.-M.; Hsu, P.-H.

    2012-07-01

    Hyperspectral images, which contain rich and fine spectral information, can be used to identify surface objects and improve land use/cover classification accuracy. Due to the property of high dimensionality of hyperspectral data, traditional statistics-based classifiers cannot be directly used on such images with limited training samples. This problem is referred as "curse of dimensionality". The commonly used method to solve this problem is dimensionality reduction, and feature extraction is used to reduce the dimensionality of hyperspectral images more frequently. There are two types of feature extraction methods. The first type is based on statistical property of data. The other type is based on time-frequency analysis. In this study, the time-frequency analysis methods are used to extract the features for hyperspectral image classification. Firstly, it has been proven that wavelet-based feature extraction provide an effective tool for spectral feature extraction. On the other hand, Hilbert-Huang transform (HHT), a relative new time-frequency analysis tool, has been widely used in nonlinear and nonstationary data analysis. In this study, wavelet transform and HHT are implemented on the hyperspectral data for physical spectral analysis. Therefore, we can get a small number of salient features, reduce the dimensionality of hyperspectral images and keep the accuracy of classification results. An AVIRIS data set is used to test the performance of the proposed HHT-based feature extraction methods; then, the results are compared with wavelet-based feature extraction. According to the experiment results, HHT-based feature extraction methods are effective tools and the results are similar with wavelet-based feature extraction methods.

  9. Wavelet-based regularity analysis reveals recurrent spatiotemporal behavior in resting-state fMRI.

    PubMed

    Smith, Robert X; Jann, Kay; Ances, Beau; Wang, Danny J J

    2015-09-01

    One of the major findings from multimodal neuroimaging studies in the past decade is that the human brain is anatomically and functionally organized into large-scale networks. In resting state fMRI (rs-fMRI), spatial patterns emerge when temporal correlations between various brain regions are tallied, evidencing networks of ongoing intercortical cooperation. However, the dynamic structure governing the brain's spontaneous activity is far less understood due to the short and noisy nature of the rs-fMRI signal. Here, we develop a wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform to measure recurrent temporal pattern stability within the rs-fMRI signal across multiple temporal scales. The method consists of performing a stationary wavelet transform to preserve signal structure, followed by construction of "lagged" subsequences to adjust for correlated features, and finally the calculation of sample entropy across wavelet scales based on an "objective" estimate of noise level at each scale. We found that the brain's default mode network (DMN) areas manifest a higher level of irregularity in rs-fMRI time series than rest of the brain. In 25 aged subjects with mild cognitive impairment and 25 matched healthy controls, wavelet-based regularity analysis showed improved sensitivity in detecting changes in the regularity of rs-fMRI signals between the two groups within the DMN and executive control networks, compared with standard multiscale entropy analysis. Wavelet-based regularity analysis based on noise estimation capabilities of the wavelet transform is a promising technique to characterize the dynamic structure of rs-fMRI as well as other biological signals.

  10. Wavelet-Based Processing for Fiber Optic Sensing Systems

    NASA Technical Reports Server (NTRS)

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    DelMarco, Stephen

    2016-05-01

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

  12. Wavelet based R-peak detection for heart rate variability studies.

    PubMed

    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

  13. sEMG wavelet-based indices predicts muscle power loss during dynamic contractions.

    PubMed

    González-Izal, M; Rodríguez-Carreño, I; Malanda, A; Mallor-Giménez, F; Navarro-Amézqueta, I; Gorostiaga, E M; Izquierdo, M

    2010-12-01

    The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F(med)), Dimitrov spectral index of muscle fatigue (FI(nsm5)), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI(nsm5) and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.

  14. Wavelet-based identification of DNA focal genomic aberrations from single nucleotide polymorphism arrays

    PubMed Central

    2011-01-01

    Background Copy number aberrations (CNAs) are an important molecular signature in cancer initiation, development, and progression. However, these aberrations span a wide range of chromosomes, making it hard to distinguish cancer related genes from other genes that are not closely related to cancer but are located in broadly aberrant regions. With the current availability of high-resolution data sets such as single nucleotide polymorphism (SNP) microarrays, it has become an important issue to develop a computational method to detect driving genes related to cancer development located in the focal regions of CNAs. Results In this study, we introduce a novel method referred to as the wavelet-based identification of focal genomic aberrations (WIFA). The use of the wavelet analysis, because it is a multi-resolution approach, makes it possible to effectively identify focal genomic aberrations in broadly aberrant regions. The proposed method integrates multiple cancer samples so that it enables the detection of the consistent aberrations across multiple samples. We then apply this method to glioblastoma multiforme and lung cancer data sets from the SNP microarray platform. Through this process, we confirm the ability to detect previously known cancer related genes from both cancer types with high accuracy. Also, the application of this approach to a lung cancer data set identifies focal amplification regions that contain known oncogenes, though these regions are not reported using a recent CNAs detecting algorithm GISTIC: SMAD7 (chr18q21.1) and FGF10 (chr5p12). Conclusions Our results suggest that WIFA can be used to reveal cancer related genes in various cancer data sets. PMID:21569311

  15. Wavelet-based time series bootstrap model for multidecadal streamflow simulation using climate indicators

    NASA Astrophysics Data System (ADS)

    Erkyihun, Solomon Tassew; Rajagopalan, Balaji; Zagona, Edith; Lall, Upmanu; Nowak, Kenneth

    2016-05-01

    A model to generate stochastic streamflow projections conditioned on quasi-oscillatory climate indices such as Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) is presented. Recognizing that each climate index has underlying band-limited components that contribute most of the energy of the signals, we first pursue a wavelet decomposition of the signals to identify and reconstruct these features from annually resolved historical data and proxy based paleoreconstructions of each climate index covering the period from 1650 to 2012. A K-Nearest Neighbor block bootstrap approach is then developed to simulate the total signal of each of these climate index series while preserving its time-frequency structure and marginal distributions. Finally, given the simulated climate signal time series, a K-Nearest Neighbor bootstrap is used to simulate annual streamflow series conditional on the joint state space defined by the simulated climate index for each year. We demonstrate this method by applying it to simulation of streamflow at Lees Ferry gauge on the Colorado River using indices of two large scale climate forcings: Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO), which are known to modulate the Colorado River Basin (CRB) hydrology at multidecadal time scales. Skill in stochastic simulation of multidecadal projections of flow using this approach is demonstrated.

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

    PubMed

    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.

  17. Wavelet-based adaptive denoising and baseline correction for MALDI TOF MS.

    PubMed

    Shin, Hyunjin; Sampat, Mehul P; Koomen, John M; Markey, Mia K

    2010-06-01

    Proteomic profiling by MALDI TOF mass spectrometry (MS) is an effective method for identifying biomarkers from human serum/plasma, but the process is complicated by the presence of noise in the spectra. In MALDI TOF MS, the major noise source is chemical noise, which is defined as the interference from matrix material and its clusters. Because chemical noise is nonstationary and nonwhite, wavelet-based denoising is more effective than conventional noise reduction schemes based on Fourier analysis. However, current wavelet-based denoising methods for mass spectrometry do not fully consider the characteristics of chemical noise. In this article, we propose new wavelet-based high-frequency noise reduction and baseline correction methods that were designed based on the discrete stationary wavelet transform. The high-frequency noise reduction algorithm adaptively estimates the time-varying threshold for each frequency subband from multiple realizations of chemical noise and removes noise from mass spectra of samples using the estimated thresholds. The baseline correction algorithm computes the monotonically decreasing baseline in the highest approximation of the wavelet domain. The experimental results demonstrate that our algorithms effectively remove artifacts in mass spectra that are due to chemical noise while preserving informative features as compared to commonly used denoising methods.

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

    PubMed Central

    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

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

    PubMed

    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

  20. 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.

  1. Robust wavelet-based video watermarking scheme for copyright protection using the human visual system

    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.

  2. 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.

  3. 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.

  4. 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.

  5. Wavelet-based pavement image compression and noise reduction

    NASA Astrophysics Data System (ADS)

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

    2005-08-01

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

  6. 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.

  7. Multiresolution Wavelet Based Adaptive Numerical Dissipation Control for Shock-Turbulence Computations

    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

  8. Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals

    NASA Astrophysics Data System (ADS)

    Elbarghathi, F.; Wang, T.; Zhen, D.; Gu, F.; Ball, A.

    2012-05-01

    Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

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

    PubMed

    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

  10. 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.

  11. 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.

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

    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.

  13. An investigation of time efficiency in wavelet-based Markov parameter extraction methods

    SciTech Connect

    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.

  14. Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

    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.

  15. A study on discrete wavelet-based noise removal from EEG signals.

    PubMed

    Asaduzzaman, K; Reaz, M B I; Mohd-Yasin, F; Sim, K S; Hussain, M S

    2010-01-01

    Electroencephalogram (EEG) serves as an extremely valuable tool for clinicians and researchers to study the activity of the brain in a non-invasive manner. It has long been used for the diagnosis of various central nervous system disorders like seizures, epilepsy, and brain damage and for categorizing sleep stages in patients. The artifacts caused by various factors such as Electrooculogram (EOG), eye blink, and Electromyogram (EMG) in EEG signal increases the difficulty in analyzing them. Discrete wavelet transform has been applied in this research for removing noise from the EEG signal. The effectiveness of the noise removal is quantitatively measured using Root Mean Square (RMS) Difference. This paper reports on the effectiveness of wavelet transform applied to the EEG signal as a means of removing noise to retrieve important information related to both healthy and epileptic patients. Wavelet-based noise removal on the EEG signal of both healthy and epileptic subjects was performed using four discrete wavelet functions. With the appropriate choice of the wavelet function (WF), it is possible to remove noise effectively to analyze EEG significantly. Result of this study shows that WF Daubechies 8 (db8) provides the best noise removal from the raw EEG signal of healthy patients, while WF orthogonal Meyer does the same for epileptic patients. This algorithm is intended for FPGA implementation of portable biomedical equipments to detect different brain state in different circumstances.

  16. Application of Conjunctive Nonlinear Model Based on Wavelet Transforms and Artificial Neural Networks to Drought Forecasting

    NASA Astrophysics Data System (ADS)

    Abrishamchi, A.; Mehdikhani, H.; Tajrishy, M.; Marino, M. A.; Abrishamchi, A.

    2007-12-01

    Drought forecasting plays an important role in mitigation of economic, environmental and social impacts of drought. Traditional statistical time series methods have a limited ability to capture non-stationarities and nonlinearities in data. Artificial Neural Network (ANN) because of highly flexible function estimator that has self- learning and self-adaptive feature has shown great ability in forecasting nonlinear and nonstationary time series in hydrology. Recently wavelet transforms have become a common tool for analyzing local variation in time series. Wavelet transforms provide a useful decomposition of a signal, or time series; therefore, hybrid models have been proposed for forecasting a time series based on a wavelet transform preprocessing. Wavelet-transformed data aids in improving the ability of forecasting models by diagnosing signal's main frequency component and abstract local information of the original time series on various resolution levels. This paper presents a conjunctive nonlinear model using Wavelet Transforms and Artificial Neural Network. Application of the model in Zayandeh-Rood River basin (Iran) shows that the conjunctive model significantly improves the ability of artificial neural networks for 1, 3, 6 and 9 months ahead forecasting of EDI (effective drought indices) time series. Improved forecasts allow water resources decision makers to develop drought preparedness plans far in advance.

  17. 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.

  18. 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.

  19. A stationary wavelet transform based approach to registration of planning CT and setup cone beam-CT images in radiotherapy.

    PubMed

    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).

  20. 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.

  1. Wavelet transform modulus maxima based fractal correlation analysis

    NASA Astrophysics Data System (ADS)

    Lin, D. C.; Sharif, A.

    2007-12-01

    The wavelet transform modulus maxima (WTMM) used in the singularity analysis of one fractal function is extended to study the fractal correlation of two multifractal functions. The technique is developed in the framework of joint partition function analysis (JPFA) proposed by Meneveau et al. [C. Meneveau, K.R. Sreenivasan, Phys. Rev. A 41, 894 (1990)] and is shown to be equally effective. In addition, we show that another leading approach developed for the same purpose, namely, relative multifractal analysis, can be considered as a special case of JPFA at a particular parameter setting.

  2. 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.

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

    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.

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

    PubMed Central

    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

  5. Analysis of photonic Doppler velocimetry data based on the continuous wavelet transform

    SciTech Connect

    Liu Shouxian; Wang Detian; Li Tao; Chen Guanghua; Li Zeren; Peng Qixian

    2011-02-15

    The short time Fourier transform (STFT) cannot resolve rapid velocity changes in most photonic Doppler velocimetry (PDV) data. A practical analysis method based on the continuous wavelet transform (CWT) was presented to overcome this difficulty. The adaptability of the wavelet family predicates that the continuous wavelet transform uses an adaptive time window to estimate the instantaneous frequency of signals. The local frequencies of signal are accurately determined by finding the ridge in the spectrogram of the CWT and then are converted to target velocity according to the Doppler effects. A performance comparison between the CWT and STFT is demonstrated by a plate-impact experiment data. The results illustrate that the new method is automatic and adequate for analysis of PDV data.

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

    PubMed

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

    2007-01-01

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

  7. Wavelet-based Poisson solver for use in particle-in-cell simulations.

    PubMed

    Terzić, Balsa; Pogorelov, Ilya V

    2005-06-01

    We report on a successful implementation of a wavelet-based Poisson solver for use in three-dimensional particle-in-cell simulations. Our method 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 additional compression of relevant data sets. We present and discuss preliminary results relating to the application of the new solver to test problems in accelerator physics and astrophysics. PMID:15980304

  8. [Segmentation of medical images based on dyadic wavelet transform and active contour model].

    PubMed

    Li, Hong; Wang, Huinan; Chang, Linfeng; Shao, Xiaoli

    2008-12-01

    The interference of noise and the weak edge characteristic of symptom information on medical images prevent the traditional methods of segmentation from having good effects. In this paper is proposed a boundary detection method of focus which is based on dyadic wavelet transform and active contour model. In this method, the true edge points are detected by dyadic wavelet transform and linked by improved fast active contour model algorithm. The result of experiment on MRI of brain shows that the method can remove the influence of noise effective and detect the contour of brain tumor actually. PMID:19166191

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

    PubMed Central

    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

  10. Bayesian wavelet-based image denoising using the Gauss-Hermite expansion.

    PubMed

    Rahman, S M Mahbubur; Ahmad, M Omair; Swamy, M N S

    2008-10-01

    The probability density functions (PDFs) of the wavelet coefficients play a key role in many wavelet-based image processing algorithms, such as denoising. The conventional PDFs usually have a limited number of parameters that are calculated from the first few moments only. Consequently, such PDFs cannot be made to fit very well with the empirical PDF of the wavelet coefficients of an image. As a result, the shrinkage function utilizing any of these density functions provides a substandard denoising performance. In order for the probabilistic model of the image wavelet coefficients to be able to incorporate an appropriate number of parameters that are dependent on the higher order moments, a PDF using a series expansion in terms of the Hermite polynomials that are orthogonal with respect to the standard Gaussian weight function, is introduced. A modification in the series function is introduced so that only a finite number of terms can be used to model the image wavelet coefficients, ensuring at the same time the resulting PDF to be non-negative. It is shown that the proposed PDF matches the empirical one better than some of the standard ones, such as the generalized Gaussian or Bessel K-form PDF. A Bayesian image denoising technique is then proposed, wherein the new PDF is exploited to statistically model the subband as well as the local neighboring image wavelet coefficients. Experimental results on several test images demonstrate that the proposed denoising method, both in the subband-adaptive and locally adaptive conditions, provides a performance better than that of most of the methods that use PDFs with limited number of parameters.

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

    PubMed

    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

  12. 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.

  13. Wavelet Estimation of Plant Spatial Patterns in Multi-temporal Aerial Photography

    NASA Astrophysics Data System (ADS)

    Strand, E. K.; Vierling, L. A.; Smith, A. M.; Bunting, S. C.; Hann, D. B.; Gessler, P. E.

    2005-12-01

    Spatial wavelet analysis represents a powerful set of image processing techniques that has considerable potential to quantify ecologically relevant patterns at multiple scales. We utilized 1-m panchromatic aerial photography from 1939 and 1998 to automatically detect the location and crown diameters of western juniper (Juniperus occidentalis) plants as they encroach upon a sagebrush (Artemisia spp.) steppe landscape via 2-dimensional wavelet analysis. We further demonstrate how wavelet analysis can be used to estimate plant cover, above-ground carbon stock and net C sequestration rate in above-ground woody tissue. The juniper crown diameters derived from wavelet analysis produced a strong correlation with crown diameters measured via comparable hand-digitizing in a geographic information system (r=0.96, n=69) with a 5% commission and an 8% omission error. When compared to field data we found that plants with crown diameters three times larger than the image pixel size were accurately depicted by the wavelet technique (r=0.86, n=60) with a 19% omission and 0% commission error. Similarly, the GIS hand-digitizing yielded a 15% omission error and 0 % commission error (r =0.92, n=65). Plants with a crown diameter smaller than 3 m were not detected by the wavelet method nor were they possible to record in a GIS, however these small plants comprised only 4% of the woody carbon across the sampled landscape. We further compared the wavelet estimated plant cover to field data for 20 plots with juniper cover ranging from 1.2 - 61.8 %. Cover estimates using wavelets are accurate up to approximately 20% juniper cover (r=0.81, n=13), but plant cover is underestimated for plots with a canopy closure > 20% due to crown clumping. Considering all of the 20 plots, the wavelet method estimated 10.7% total plant cover compared to the field estimate of 13.9%. We attribute approximately 50% of the error in the cover estimate to the inability to detect plants < 3 m in size, and 50 % of

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

    PubMed

    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.

  15. 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.

  16. Wavelet-based learning vector quantization for automatic target recognition

    NASA Astrophysics Data System (ADS)

    Chan, Lipchen A.; Nasrabadi, Nasser M.; Mirelli, Vincent

    1996-06-01

    An automatic target recognition classifier is constructed that uses a set of dedicated vector quantizers (VQs). The background pixels in each input image are properly clipped out by a set of aspect windows. The extracted target area for each aspect window is then enlarged to a fixed size, after which a wavelet decomposition splits the enlarged extraction into several subbands. A dedicated VQ codebook is generated for each subband of a particular target class at a specific range of aspects. Thus, each codebook consists of a set of feature templates that are iteratively adapted to represent a particular subband of a given target class at a specific range of aspects. These templates are then further trained by a modified learning vector quantization (LVQ) algorithm that enhances their discriminatory characteristics. A recognition rate of 69.0 percent is achieved on a highly cluttered test set.

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

    PubMed

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

    2014-10-01

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

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

    NASA Astrophysics Data System (ADS)

    Régulo, C.; Almenara, J. M.; Alonso, R.; Deeg, H.; Roca Cortés, T.

    2007-06-01

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

  19. 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.

  20. An Adaptive Wavelet-Based Denoising Algorithm for Enhancing Speech in Non-stationary Noise Environment

    NASA Astrophysics Data System (ADS)

    Wang, Kun-Ching

    Traditional wavelet-based speech enhancement algorithms are ineffective in the presence of highly non-stationary noise because of the difficulties in the accurate estimation of the local noise spectrum. In this paper, a simple method of noise estimation employing the use of a voice activity detector is proposed. We can improve the output of a wavelet-based speech enhancement algorithm in the presence of random noise bursts according to the results of VAD decision. The noisy speech is first preprocessed using bark-scale wavelet packet decomposition (BSWPD) to convert a noisy signal into wavelet coefficients (WCs). It is found that the VAD using bark-scale spectral entropy, called as BS-Entropy, parameter is superior to other energy-based approach especially in variable noise-level. The wavelet coefficient threshold (WCT) of each subband is then temporally adjusted according to the result of VAD approach. In a speech-dominated frame, the speech is categorized into either a voiced frame or an unvoiced frame. A voiced frame possesses a strong tone-like spectrum in lower subbands, so that the WCs of lower-band must be reserved. On the contrary, the WCT tends to increase in lower-band if the speech is categorized as unvoiced. In a noise-dominated frame, the background noise can be almost completely removed by increasing the WCT. The objective and subjective experimental results are then used to evaluate the proposed system. The experiments show that this algorithm is valid on various noise conditions, especially for color noise and non-stationary noise conditions.

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

    PubMed Central

    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

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

    PubMed

    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

  3. 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

  4. 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.

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

    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.

  6. Application of time-resolved glucose concentration photoacoustic signals based on an improved wavelet denoising

    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%.

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

    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.

  8. Brain tumor detection using scalp eeg with modified Wavelet-ICA and multi layer feed forward neural network.

    PubMed

    Selvam, V Salai; Shenbagadevi, S

    2011-01-01

    Use of scalp EEG for the diagnosis of various cerebral disorders is progressively increasing. Though the advanced neuroimaging techniques such as MRI and CT-SCAN still stay as principal confirmative methods for detecting and localizing brain tumors, the development of automated systems for the detection of brain tumors using the scalp EEG has started attracting the researchers all over the world notably since 2000. This is because of two important facts: (i) cheapness and easiness of methods of recording and analyzing the scalp EEG and (ii) lower risk and possible early detection. This paper presents a method of detecting the brain tumor using the first, second and third order statistics of the scalp EEG with a Modified Wavelet-Independent Component Analysis (MwICA) technique and a multi-layer feed-forward neural network. PMID:22255732

  9. 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.

  10. 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'.

  11. 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."

  12. Wavelet-based automatic determination of the P- and S-wave arrivals

    NASA Astrophysics Data System (ADS)

    Bogiatzis, P.; Ishii, M.

    2013-12-01

    The detection of P- and S-wave arrivals is important for a variety of seismological applications including earthquake detection and characterization, and seismic tomography problems such as imaging of hydrocarbon reservoirs. For many years, dedicated human-analysts manually selected the arrival times of P and S waves. However, with the rapid expansion of seismic instrumentation, automatic techniques that can process a large number of seismic traces are becoming essential in tomographic applications, and for earthquake early-warning systems. In this work, we present a pair of algorithms for efficient picking of P and S onset times. The algorithms are based on the continuous wavelet transform of the seismic waveform that allows examination of a signal in both time and frequency domains. Unlike Fourier transform, the basis functions are localized in time and frequency, therefore, wavelet decomposition is suitable for analysis of non-stationary signals. For detecting the P-wave arrival, the wavelet coefficients are calculated using the vertical component of the seismogram, and the onset time of the wave is identified. In the case of the S-wave arrival, we take advantage of the polarization of the shear waves, and cross-examine the wavelet coefficients from the two horizontal components. In addition to the onset times, the automatic picking program provides estimates of uncertainty, which are important for subsequent applications. The algorithms are tested with synthetic data that are generated to include sudden changes in amplitude, frequency, and phase. The performance of the wavelet approach is further evaluated using real data by comparing the automatic picks with manual picks. Our results suggest that the proposed algorithms provide robust measurements that are comparable to manual picks for both P- and S-wave arrivals.

  13. On the analysis of wavelet-based approaches for print mottle artifacts

    NASA Astrophysics Data System (ADS)

    Eid, Ahmed H.; Cooper, Brian E.

    2014-01-01

    Print mottle is one of several attributes described in ISO/IEC DTS 24790, a draft technical specification for the measurement of image quality for monochrome printed output. It defines mottle as aperiodic fluctuations of lightness less than about 0.4 cycles per millimeter, a definition inherited from the latest official standard on printed image quality, ISO/IEC 13660. In a previous publication, we introduced a modification to the ISO/IEC 13660 mottle measurement algorithm that includes a band-pass, wavelet-based, filtering step to limit the contribution of high-frequency fluctuations including those introduced by print grain artifacts. This modification has improved the algorithm's correlation with the subjective evaluation of experts who rated the severity of printed mottle artifacts. Seeking to improve upon the mottle algorithm in ISO/IEC 13660, the ISO 24790 committee evaluated several mottle metrics. This led to the selection of the above wavelet-based approach as the top candidate algorithm for inclusion in a future ISO/IEC standard. Recent experimental results from the ISO committee showed higher correlation between the wavelet-based approach and the subjective evaluation conducted by the ISO committee members based upon 25 samples covering a variety of printed mottle artifacts. In addition, we introduce an alternative approach for measuring mottle defects based on spatial frequency analysis of wavelet- filtered images. Our goal is to establish a link between the spatial-based mottle (ISO/IEC DTS 24790) approach and its equivalent frequency-based one in light of Parseval's theorem. Our experimental results showed a high correlation between the spatial and frequency based approaches.

  14. Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet-NN hybrid model

    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.

  15. Multi-step-ahead predictor design for effective long-term forecast of hydrological signals using a novel wavelet neural network hybrid model

    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.

  16. Study of geomagnetic disturbances and ring current variability during storm and quiet times using wavelet analysis and ground-based magnetic data from multiple stations

    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

  17. Wavelet-based surrogate time series for multiscale simulation of heterogeneous catalysis

    DOE PAGESBeta

    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

  18. Corrosion in Reinforced Concrete Panels: Wireless Monitoring and Wavelet-Based Analysis

    PubMed Central

    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

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

    PubMed

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

    2016-09-01

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

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

    PubMed

    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.

  1. 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.

  2. Nonlinear structure analysis of carbon and energy markets with MFDCCA based on maximum overlap wavelet transform

    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.

  3. Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI.

    PubMed

    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.

  4. Ultrasonic test of resistance spot welds based on wavelet package analysis.

    PubMed

    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.

  5. Implantable neural spike detection using lifting-based stationary wavelet transform.

    PubMed

    Yang, Yuning; Mason, Andrew J

    2011-01-01

    Spike detection from high data rate neural recordings is desired to ease the bandwidth bottleneck of bio-telemetry. An appropriate spike detection method should be able to detect spikes under low signal-to-noise ratio (SNR) while meeting the power and area constraints of implantation. This paper introduces a spike detection system utilizing lifting-based stationary wavelet transform (SWT) that decomposes neural signals into 2 levels using 'symmlet2' wavelet basis. This approach enables accurate spike detection down to an SNR of only 2. The lifting-based SWT architecture permits a hardware implementation consuming only 6.6 μW power and 0.07 mm(2) area for 32 channels with 3.2 MHz master clock.

  6. Total variation versus wavelet-based methods for image denoising in fluorescence lifetime imaging microscopy

    PubMed Central

    Chang, Ching-Wei; Mycek, Mary-Ann

    2014-01-01

    We report the first application of wavelet-based denoising (noise removal) methods to time-domain box-car fluorescence lifetime imaging microscopy (FLIM) images and compare the results to novel total variation (TV) denoising methods. Methods were tested first on artificial images and then applied to low-light live-cell images. Relative to undenoised images, TV methods could improve lifetime precision up to 10-fold in artificial images, while preserving the overall accuracy of lifetime and amplitude values of a single-exponential decay model and improving local lifetime fitting in live-cell images. Wavelet-based methods were at least 4-fold faster than TV methods, but could introduce significant inaccuracies in recovered lifetime values. The denoising methods discussed can potentially enhance a variety of FLIM applications, including live-cell, in vivo animal, or endoscopic imaging studies, especially under challenging imaging conditions such as low-light or fast video-rate imaging. PMID:22415891

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

    SciTech Connect

    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.

  8. 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.

  9. Recognition of short-term changes in physiological signals with the wavelet-based multifractal formalism

    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.

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

    PubMed

    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.

  11. Wavelet-based ECG data compression system with linear quality control scheme.

    PubMed

    Ku, Cheng-Tung; Hung, King-Chu; Wu, Tsung-Ching; Wang, Huan-Sheng

    2010-06-01

    Maintaining reconstructed signals at a desired level of quality is crucial for lossy ECG data compression. Wavelet-based approaches using a recursive decomposition process are unsuitable for real-time ECG signal recoding and commonly obtain a nonlinear compression performance with distortion sensitive to quantization error. The sensitive response is caused without compromising the influences of word-length-growth (WLG) effect and unfavorable for the reconstruction quality control of ECG data compression. In this paper, the 1-D reversible round-off nonrecursive discrete periodic wavelet transform is applied to overcome the WLG magnification effect in terms of the mechanisms of error propagation resistance and significant normalization of octave coefficients. The two mechanisms enable the design of a multivariable quantization scheme that can obtain a compression performance with the approximate characteristics of linear distortion. The quantization scheme can be controlled with a single control variable. Based on the linear compression performance, a linear quantization scale prediction model is presented for guaranteeing reconstruction quality. 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 reconstruction quality control than other wavelet-based methods.

  12. HIRDLS observations of global gravity wave absolute momentum fluxes: A wavelet based approach

    NASA Astrophysics Data System (ADS)

    John, Sherine Rachel; Kishore Kumar, Karanam

    2016-02-01

    Using wavelet technique for detection of height varying vertical and horizontal wavelengths of gravity waves, the absolute values of gravity wave momentum fluxes are estimated from High Resolution Dynamics Limb Sounder (HIRDLS) temperature measurements. Two years of temperature measurements (2005 December-2007 November) from HIRDLS onboard EOS-Aura satellite over the globe are used for this purpose. The least square fitting method is employed to extract the 0-6 zonal wavenumber planetary wave amplitudes, which are removed from the instantaneous temperature profiles to extract gravity wave fields. The vertical and horizontal wavelengths of the prominent waves are computed using wavelet and cross correlation techniques respectively. The absolute momentum fluxes are then estimated using prominent gravity wave perturbations and their vertical and horizontal wavelengths. The momentum fluxes obtained from HIRDLS are compared with the fluxes obtained from ground based Rayleigh LIDAR observations over a low latitude station, Gadanki (13.5°N, 79.2°E) and are found to be in good agreement. After validation, the absolute gravity wave momentum fluxes over the entire globe are estimated. It is found that the winter hemisphere has the maximum momentum flux magnitudes over the high latitudes with a secondary maximum over the summer hemispheric low-latitudes. The significance of the present study lies in introducing the wavelet technique for estimating the height varying vertical and horizontal wavelengths of gravity waves and validating space based momentum flux estimations using ground based lidar observations.

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

    PubMed

    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

  14. 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.

  15. Analysis of damped tissue vibrations in time-frequency space: a wavelet-based approach.

    PubMed

    Enders, Hendrik; von Tscharner, Vinzenz; Nigg, Benno M

    2012-11-15

    There is evidence that vibrations of soft tissue compartments are not appropriately described by a single sinusoidal oscillation for certain types of locomotion such as running or sprinting. This paper discusses a new method to quantify damping of superimposed oscillations using a wavelet-based time-frequency approach. This wavelet-based method was applied to experimental data in order to analyze the decay of the overall power of vibration signals over time. Eight healthy subjects performed sprinting trials on a 30 m runway on a hard surface and a soft surface. Soft tissue vibrations were quantified from the tissue overlaying the muscle belly of the medial gastrocnemius muscle. The new methodology determines damping coefficients with an average error of 2.2% based on a wavelet scaling factor of 0.7. This was sufficient to detect differences in soft tissue compartment damping between the hard and soft surface. On average, the hard surface elicited a 7.02 s(-1) lower damping coefficient than the soft surface (p<0.05). A power spectral analysis of the muscular vibrations occurring during sprinting confirmed that vibrations during dynamic movements cannot be represented by a single sinusoidal function. Compared to the traditional sinusoidal approach, this newly developed method can quantify vibration damping for systems with multiple vibration modes that interfere with one another. This new time-frequency analysis may be more appropriate when an acceleration trace does not follow a sinusoidal function, as is the case with multiple forms of human locomotion. PMID:22995145

  16. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

    PubMed Central

    2012-01-01

    Background Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading. Methods Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system. Results The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85. Conclusions Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance. PMID:23253202

  17. An Efficient P300-based BCI Using Wavelet Features and IBPSO-based Channel Selection

    PubMed Central

    Perseh, Bahram; Sharafat, Ahmad R.

    2012-01-01

    We present a novel and efficient scheme that selects a minimal set of effective features and channels for detecting the P300 component of the event-related potential in the brain–computer interface (BCI) paradigm. For obtaining a minimal set of effective features, we take the truncated coefficients of discrete Daubechies 4 wavelet, and for selecting the effective electroencephalogram channels, we utilize an improved binary particle swarm optimization algorithm together with the Bhattacharyya criterion. We tested our proposed scheme on dataset IIb of BCI competition 2005 and achieved 97.5% and 74.5% accuracy in 15 and 5 trials, respectively, using a simple classification algorithm based on Bayesian linear discriminant analysis. We also tested our proposed scheme on Hoffmann's dataset for eight subjects, and achieved similar results. PMID:23717804

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

    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.

  19. Wavelet-based Poisson Solver for use in Particle-In-CellSimulations

    SciTech Connect

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-04-01

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

  1. 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.

  2. 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.

  3. Parameter identification of fractional order linear system based on Haar wavelet operational matrix.

    PubMed

    Li, Yuanlu; Meng, Xiao; Zheng, Bochao; Ding, Yaqing

    2015-11-01

    Fractional order systems can be more adequate for the description of dynamical systems than integer order models, however, how to obtain fractional order models are still actively exploring. In this paper, an identification method for fractional order linear system was proposed. This is a method based on input-output data in time domain. The input and output signals are represented by Haar wavelet, and then fractional order systems described by fractional order differential equations are transformed into fractional order integral equations. Taking use of the Haar wavelet operational matrix of the fractional order integration, the fractional order linear system can easily be converted into a system of algebraic equation. Finally, the parameters of the fractional order system are determined by minimizing the errors between the output of the real system and that of the identified system. Numerical simulations, involving integral and fractional order systems, confirm the efficiency of the above methodology.

  4. Audio watermarking robust to geometrical distortions based on dyadic wavelet transform

    NASA Astrophysics Data System (ADS)

    Wang, Yong; Wu, Shaoquan; Huang, Jiwu

    2007-02-01

    Geometrical transforms such as time-scale modification (TSM), random removal(RR), random duplication(RD), and cropping, are of common operations on audio signals while presents many challenges to robust audio watermarking. The existing algorithms aiming at solving the geometrical distortions have various drawbacks e.g. high false alarm probability, heavy computation load, small data hiding capacity, and low robustness performance. In this paper an audio watermarking algorithm based on dyadic wavelet transform robust to geometrical distortions is proposed. Watermark synchronization is achieved using the geometrical invariant properties of dyadic wavelet transform. A well-designed coding scheme is proposed for lowering the bit error rate of the watermark. The experimental results show that the watermark is robust to geometrical transforms and other common operations. Compared with other existing algorithms the proposed algorithm has several advantages of high robustness, large data hiding capacity and low computation load.

  5. 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.

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

    PubMed

    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.

  7. Active health system based on wavelet transform analysis of diffracted Lamb waves

    NASA Astrophysics Data System (ADS)

    Lemistre, Michel B.; Osmont, Daniel L.; Balageas, Daniel L.

    2000-08-01

    In composite materials, delaminations are discontinuities producing mode conversion processes generating various out-going modes. The Discrete Wavelet Transform allows isolating various propagation modes and extracting them in order to measure the time delay between the arrivals of the main burst and a specific out-going mode, for various propagation paths. This process permits, with a good accuracy, to localize a damage and to estimate its extension. An active health monitoring system composed of integrated disc-shaped, 100 (mu) m-thick and 5 mm-dia PZT transducers working sequentially as actuators and receives is presented. The diagnostic is based on multiresolution process by wavelet transform applied on recorded Lamb wave signals obtained before and after damage. The robustness and portability of this technique is demonstrated by the fact that, after validation in our laboratory it was successfully applied to data coming from an experiment conducted in an other Laboratory using its own Health Monitoring system.

  8. CHARACTERIZING COMPLEXITY IN SOLAR MAGNETOGRAM DATA USING A WAVELET-BASED SEGMENTATION METHOD

    SciTech Connect

    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.

  9. Distributed edge detection algorithm based on wavelet transform for wireless video sensor network

    NASA Astrophysics Data System (ADS)

    Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng

    2010-12-01

    Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.

  10. Distributed edge detection algorithm based on wavelet transform for wireless video sensor network

    NASA Astrophysics Data System (ADS)

    Li, Qiulin; Hao, Qun; Song, Yong; Wang, Dongsheng

    2011-05-01

    Edge detection algorithms are critical to image processing and computer vision. Traditional edge detection algorithms are not suitable for wireless video sensor network (WVSN) in which the nodes are with in limited calculation capability and resources. In this paper, a distributed edge detection algorithm based on wavelet transform designed for WVSN is proposed. Wavelet transform decompose the image into several parts, then the parts are assigned to different nodes through wireless network separately. Each node performs sub-image edge detecting algorithm correspondingly, all the results are sent to sink node, Fusing and Synthesis which include image binary and edge connect are executed in it. And finally output the edge image. Lifting scheme and parallel distributed algorithm are adopted to improve the efficiency, simultaneously, decrease the computational complexity. Experimental results show that this method could achieve higher efficiency and better result.

  11. 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.

  12. A study of interceptor attitude control based on adaptive wavelet neural networks

    NASA Astrophysics Data System (ADS)

    Li, Da; Wang, Qing-chao

    2005-12-01

    This paper engages to study the 3-DOF attitude control problem of the kinetic interceptor. When the kinetic interceptor enters into terminal guidance it has to maneuver with large angles. The characteristic of interceptor attitude system is nonlinearity, strong-coupling and MIMO. A kind of inverse control approach based on adaptive wavelet neural networks was proposed in this paper. Instead of using one complex neural network as the controller, the nonlinear dynamics of the interceptor can be approximated by three independent subsystems applying exact feedback-linearization firstly, and then controllers for each subsystem are designed using adaptive wavelet neural networks respectively. This method avoids computing a large amount of the weights and bias in one massive neural network and the control parameters can be adaptive changed online. Simulation results betray that the proposed controller performs remarkably well.

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

    SciTech Connect

    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.

  14. 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.

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

    PubMed Central

    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

  16. A Wavelet-based Fast Discrimination of Transformer Magnetizing Inrush Current

    NASA Astrophysics Data System (ADS)

    Kitayama, Masashi

    Recently customers who need electricity of higher quality have been installing co-generation facilities. They can avoid voltage sags and other distribution system related disturbances by supplying electricity to important load from their generators. For another example, FRIENDS, highly reliable distribution system using semiconductor switches or storage devices based on power electronics technology, is proposed. These examples illustrates that the request for high reliability in distribution system is increasing. In order to realize these systems, fast relaying algorithms are indispensable. The author proposes a new method of detecting magnetizing inrush current using discrete wavelet transform (DWT). DWT provides the function of detecting discontinuity of current waveform. Inrush current occurs when transformer core becomes saturated. The proposed method detects spikes of DWT components derived from the discontinuity of the current waveform at both the beginning and the end of inrush current. Wavelet thresholding, one of the wavelet-based statistical modeling, was applied to detect the DWT component spikes. The proposed method is verified using experimental data using single-phase transformer and the proposed method is proved to be effective.

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

    PubMed

    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

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

    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.

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

    NASA Astrophysics Data System (ADS)

    Yang, Wei; Yang, Yuanhong; Yang, Mingwei

    2014-06-01

    We propose a fast digital envelope detector (DED) based on the generalized harmonic wavelet transform to improve the performance of coherent heterodyne Brillouin optical time domain reflectometry. The proposed DED can obtain undistorted envelopes due to the zero phase-shift ideal bandpass filter (BPF) characteristics of the generalized harmonic wavelet (GHW). Its envelope average ability benefits from the passband designing flexibility of the GHW, and its demodulation speed can be accelerated by using a fast algorithm that only analyses signals of interest within the passband of the GHW with reduced computational complexity. The feasibility and advantage of the proposed DED are verified by simulations and experiments. With an optimized bandwidth, Brillouin frequency shift accuracy improvements of 19.4% and 11.14%, as well as envelope demodulation speed increases of 39.1% and 24.9%, are experimentally attained by the proposed DED over Hilbert transform (HT) and Morlet wavelet transform (MWT) based DEDs, respectively. Spatial resolution by the proposed DED is undegraded, which is identical to the undegraded value by HT-DED with an allpass filter characteristic and better than the degraded value by MWT-DED with a Gaussian BPF characteristic.

  20. Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness

    PubMed Central

    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

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

    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

  2. Wavelet based characterization of ex vivo vertebral trabecular bone structure with 3T MRI compared to microCT

    SciTech Connect

    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.

  3. An Improved Wavelet Packet-Chaos Model for Life Prediction of Space Relays Based on Volterra Series.

    PubMed

    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.

  4. 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

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

    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

  6. 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.

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

    PubMed

    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

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

    PubMed

    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.

  9. 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.

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

    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.

  11. 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.

  12. Performance analysis of wavelet transforms and morphological operator-based classification of epilepsy risk levels

    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.

  13. Ultrafast active cavitation imaging with enhanced cavitation to tissue ratio based on wavelet transform and pulse inversion.

    PubMed

    Liu, Runna; Hu, Hong; Xu, Shanshan; Huo, Rui; Wang, Supin; Wan, Mingxi

    2015-06-01

    The quality of ultrafast active cavitation imaging (UACI) using plane wave transmission is hindered by low transmission pressure, which is necessary to prevent bubble destruction. In this study, a UACI method that combined wavelet transform with pulse inversion (PI) was proposed to enhance the contrast between the cavitation bubbles and surrounding tissues. The main challenge in using wavelet transform is the selection of the optimum mother wavelet. A mother wavelet named "cavitation bubble wavelet" and constructed according to Rayleigh-Plesset-Noltingk-Neppiras-Poritsky model was expected to obtain a high correlation between the bubbles and beamformed echoes. The method was validated by in vitro experiments. Results showed that the image quality was associated with the initial radius of bubble and the scale. The signal-to-noise ratio (SNR) of the best optimum cavitation bubble wavelet transform (CBWT) mode image was improved by 3.2 dB compared with that of the B-mode image in free-field experiments. The cavitation-to-tissue ratio of the best optimum PI-based CBWT mode image was improved by 2.3 dB compared with that of the PI-based B-mode image in tissue experiments. Furthermore, the SNR versus initial radius curve had the potential to estimate the size distribution of cavitation bubbles.

  14. Sparse imaging of cortical electrical current densities via wavelet transforms

    NASA Astrophysics Data System (ADS)

    Liao, Ke; Zhu, Min; Ding, Lei; Valette, Sébastien; Zhang, Wenbo; Dickens, Deanna

    2012-11-01

    While the cerebral cortex in the human brain is of functional importance, functions defined on this structure are difficult to analyze spatially due to its highly convoluted irregular geometry. This study developed a novel L1-norm regularization method using a newly proposed multi-resolution face-based wavelet method to estimate cortical electrical activities in electroencephalography (EEG) and magnetoencephalography (MEG) inverse problems. The proposed wavelets were developed based on multi-resolution models built from irregular cortical surface meshes, which were realized in this study too. The multi-resolution wavelet analysis was used to seek sparse representation of cortical current densities in transformed domains, which was expected due to the compressibility of wavelets, and evaluated using Monte Carlo simulations. The EEG/MEG inverse problems were solved with the use of the novel L1-norm regularization method exploring the sparseness in the wavelet domain. The inverse solutions obtained from the new method using MEG data were evaluated by Monte Carlo simulations too. The present results indicated that cortical current densities could be efficiently compressed using the proposed face-based wavelet method, which exhibited better performance than the vertex-based wavelet method. In both simulations and auditory experimental data analysis, the proposed L1-norm regularization method showed better source detection accuracy and less estimation errors than other two classic methods, i.e. weighted minimum norm (wMNE) and cortical low-resolution electromagnetic tomography (cLORETA). This study suggests that the L1-norm regularization method with the use of face-based wavelets is a promising tool for studying functional activations of the human brain.

  15. 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

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

    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.

  17. 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.

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

    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.

  19. 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…

  20. Investigation of urban faults in Shenzhen using wavelet multi-scale analysis and modeling of gravity observations

    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).

  1. Evaluation of blood access dysfunction based on a wavelet transform analysis of shunt murmurs.

    PubMed

    Sato, Toshio; Tsuji, Kiichi; Kawashima, Norimichi; Agishi, Tetsuzo; Toma, Hiroshi

    2006-01-01

    We investigated shunt murmurs based on wavelet transform analysis as a new method for assessing vascular access function. In the present study, in patients with venous stenosis near an arteriovenous fistula (A-V fistula), a sensor was placed at different positions around the stenosis and shunt murmur signals obtained using a measurement system were subjected to time-frequency analysis based on wavelet transforms. The shunt murmurs obtained from the stenotic region closely represented some features of murmurs that are often referred to as "high-pitch" murmurs in the clinical setting. In contrast, shunt murmurs obtained about 5 cm downstream of the stenotic region closely represented some features of murmurs that are often referred to as "low-pitch" murmurs in the clinical setting. Furthermore, with the aim of extending the lifespan of arteriovenous grafts (A-V grafts) by detecting and treating stenotic lesions before the A-V graft becomes occluded, we evaluated the possibility of utilizing the present shunt murmur analysis for monitoring stenosis in such A-V grafts. When shunt murmurs from patients with A-V grafts were analyzed, the results suggested that the blood flow through the venous anastomosis of the graft was the most turbulent. This present method whereby blood flow in an A-V fistula is assessed based on the frequency distribution on a time-frequency plane by wavelet transform analysis is advantageous because findings are not markedly affected by sensor attachment. Furthermore, because the sensor is attached using an adhesive collar, measurements can be taken over a short period of time before each dialysis session. PMID:16807812

  2. Energy Detection Based on Undecimated Discrete Wavelet Transform and Its Application in Magnetic Anomaly Detection

    PubMed Central

    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

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

    PubMed

    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.

  4. Application of chaotic prediction model based on wavelet transform on water quality prediction

    NASA Astrophysics Data System (ADS)

    Zhang, L.; Zou, Z. H.; Zhao, Y. F.

    2016-08-01

    Dissolved oxygen (DO) is closely related to water self-purification capacity. In order to better forecast its concentration, the chaotic prediction model, based on the wavelet transform, is proposed and applied to a certain monitoring section of the Mentougou area of the Haihe River Basin. The result is compared with the simple application of the chaotic prediction model. The study indicates that the new model aligns better with the real data and has a higher accuracy. Therefore, it will provide significant decision support for water protection and water environment treatment.

  5. 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.

  6. Automatic quantitative analysis of ultrasound tongue contours via wavelet-based functional mixed models.

    PubMed

    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.

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

    SciTech Connect

    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.

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

    PubMed

    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

  9. 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

  10. [Adaptive de-noising of ECG signal based on stationary wavelet transform].

    PubMed

    Dong, Hong-sheng; Zhang, Ai-hua; Hao, Xiao-hong

    2009-03-01

    According to the limitations of wavelet threshold in de-noising method, we approached a combining algorithm of the stationary wavelet transform with adaptive filter. The stationary wavelet transformation can suppress Gibbs phenomena in traditional DWT effectively, and adaptive filter is introduced at the high scale wavelet coefficient of the stationary wavelet transformation. It would remove baseline wander and keep the shape of low frequency and low amplitude P wave, T wave and ST segment wave of ECG signal well. That is important for analyzing ECG signal of other feature information.

  11. Three-dimensional Wavelet-based Adaptive Mesh Refinement for Global Atmospheric Chemical Transport Modeling

    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

  12. Study on fluorometric discrimination of phytoplankton based on time-series vectors of wavelet transform

    NASA Astrophysics Data System (ADS)

    Zhang, Fang; Su, Rongguo; He, Jianfeng; Cai, Minghong; Luo, Wei; Wang, Xiulin

    2010-02-01

    The feasibility of using time domain of wavelet transform as characteristics to establish a fluorometric discrimination method of phytoplankton was discussed. Twelve phytoplankton species belonging to nine genera of five divisions were studied. Five steps were introduced: firstly, the feasibility of utilizing 3D fluorescence spectra (3D-FS) to discriminate phytoplankton was discussed; the relative standard deviation (RSD) and included angle cosine (IAC) were used as the test criterion. 3D-FS had such potentials, for most RSD were <5% and most IAC were >0.990. Secondly, the 3D-FS were decomposed by db7 wavelet and time-series vectors (TSVs) were generated. Thirdly, the optimal characteristic spectra (OCS) were selected from the TSV by Bayesian linear discriminant analysis (BLDA). The ability of OCS to classify phytoplankton was tested, and the correct classification ratios (CCRs) at different levels were obtained. Most CCRs were 90-100% at the species level. They were >98% at the genus level, and >99% at the division level. Fourthly, the growth and light stability of the OCS were tested. Both stabilities were high with lower RSD (<3%) and higher IAC (>0.999) compared with 3D-FS. Fifthly, a "database of reference spectra" consisting of 46 reference spectra was established by hierarchical cluster analysis (HCA). Based on this, the discrimination method of phytoplankton species was established by nonnegative least squares (NNLSs). Most reference spectra were representative to phytoplankton species; and had moderate anti-noise ability: With noise ≤10%, the correct discrimination ratios (CDRs) were >98% at the genus level and >99% at the division level. 20% noise was a larger interference which made CDRs down to 85% at the genus level and to 99% at the division level. A fluorometric discrimination method of phytoplankton could be established based on TSV of wavelet transform.

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

    PubMed Central

    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

  14. A quantitative acoustic emission study on fracture processes in ceramics based on wavelet packet decomposition

    SciTech Connect

    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.

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

    PubMed

    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

  16. Neurochip based on light-addressable potentiometric sensor with wavelet transform de-noising*

    PubMed Central

    Liu, Qing-jun; Ye, Wei-wei; Yu, Hui; Hu, Ning; Du, Li-ping; Wang, Ping

    2010-01-01

    Neurochip based on light-addressable potentiometric sensor (LAPS), whose sensing elements are excitable cells, can monitor electrophysiological properties of cultured neuron networks with cellular signals well analyzed. Here we report a kind of neurochip with rat pheochromocytoma (PC12) cells hybrid with LAPS and a method of de-noising signals based on wavelet transform. Cells were cultured on LAPS for several days to form networks, and we then used LAPS system to detect the extracellular potentials with signals de-noised according to decomposition in the time-frequency space. The signal was decomposed into various scales, and coefficients were processed based on the properties of each layer. At last, signal was reconstructed based on the new coefficients. The results show that after de-noising, baseline drift is removed and signal-to-noise ratio is increased. It suggests that the neurochip of PC12 cells coupled to LAPS is stable and suitable for long-term and non-invasive measurement of cell electrophysiological properties with wavelet transform, taking advantage of its time-frequency localization analysis to reduce noise. PMID:20443210

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

    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.

  18. Wavelet-transform-based active imaging of cavitation bubbles in tissues induced by high intensity focused ultrasound.

    PubMed

    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.

  19. Wavelet-transform-based active imaging of cavitation bubbles in tissues induced by high intensity focused ultrasound.

    PubMed

    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

  20. 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.

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

    SciTech Connect

    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.

  2. Sparse overcomplete Gabor wavelet representation based on local competitions.

    PubMed

    Fischer, Sylvain; Cristóbal, Gabriel; Redondo, Rafael

    2006-02-01

    Gabor representations present a number of interesting properties despite the fact that the basis functions are nonorthogonal and provide an overcomplete representation or a nonexact reconstruction. Overcompleteness involves an expansion of the number of coefficients in the transform domain and induces a redundancy that can be further reduced through computational costly iterative algorithms like Matching Pursuit. Here, a biologically plausible algorithm based on competitions between neighboring coefficients is employed for adaptively representing any source image by a selected subset of Gabor functions. This scheme involves a sharper edge localization and a significant reduction of the information redundancy, while, at the same time, the reconstruction quality is preserved. The method is characterized by its biological plausibility and promising results, but it still requires a more in depth theoretical analysis for completing its validation.

  3. 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.

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

    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.

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

    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.

  6. [A novel method to determine the redshifts of active galaxies based on wavelet transform].

    PubMed

    Tu, Liang-Ping; Luo, A-Li; Jiang, Bin; Wei, Peng; Zhao, Yong-Heng; Liu, Rong

    2012-10-01

    Automatically determining redshifts of galaxies is very important for astronomical research on large samples, such as large-scale structure of cosmological significance. Galaxies are generally divided into normal galaxies and active galaxies, and the spectra of active galaxies mostly have more obvious emission lines. In the present paper, the authors present a novel method to determine spectral redshifts of active galaxies rapidly based on wavelet transformation mainly, and it does not need to extract line information accurately. This method includes the following steps: Firstly, we denoised a spectrum to be processed; Secondly, the low-frequency spectrum was extracted based on wavelet transform, and then we could get the residual spectrum through the denoised spectrum subtracting the low-frequency spectrum; Thirdly, the authors calculated the standard deviation of the residual spectrum and determined a threshold value T, then retained the wavelength set whose corresponding flux was greater than T; Fourthly, according to the wavelength form of all the standard lines, we calculated all the candidate redshifts; Finally, utilizing the density estimation method based on Parzen window, we determined the redshift point with maximum density, and the average value of its neighborhood would be the final redshift of this spectrum. The experiments on simulated data and real data from SDSS-DR7 show that this method is robust and its correct rate is encouraging. And it can be expected to be applied in the project of LAMOST.

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

    PubMed

    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

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

    PubMed Central

    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

  9. [A wavelet-transform-based method for the automatic detection of late-type stars].

    PubMed

    Liu, Zhong-tian; Zhao, Rrui-zhen; Zhao, Yong-heng; Wu, Fu-chao

    2005-07-01

    The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

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

    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.

  11. A wavelet based method for automatic detection of slow eye movements: a pilot study.

    PubMed

    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

  12. On the Use of Adaptive Wavelet-based Methods for Ocean Modeling and Data Assimilation Problems

    NASA Astrophysics Data System (ADS)

    Vasilyev, Oleg V.; Yousuff Hussaini, M.; Souopgui, Innocent

    2014-05-01

    Latest advancements in parallel wavelet-based numerical methodologies for the solution of partial differential equations, combined with the unique properties of wavelet analysis to unambiguously identify and isolate localized dynamically dominant flow structures, make it feasible to start developing integrated approaches for ocean modeling and data assimilation problems that take advantage of temporally and spatially varying meshes. In this talk the Parallel Adaptive Wavelet Collocation Method with spatially and temporarily varying thresholding is presented and the feasibility/potential advantages of its use for ocean modeling are discussed. The second half of the talk focuses on the recently developed Simultaneous Space-time Adaptive approach that addresses one of the main challenges of variational data assimilation, namely the requirement to have a forward solution available when solving the adjoint problem. The issue is addressed by concurrently solving forward and adjoint problems in the entire space-time domain on a near optimal adaptive computational mesh that automatically adapts to spatio-temporal structures of the solution. The compressed space-time form of the solution eliminates the need to save or recompute forward solution for every time slice, as it is typically done in traditional time marching variational data assimilation approaches. The simultaneous spacio-temporal discretization of both the forward and the adjoint problems makes it possible to solve both of them concurrently on the same space-time adaptive computational mesh reducing the amount of saved data to the strict minimum for a given a priori controlled accuracy of the solution. The simultaneous space-time adaptive approach of variational data assimilation is demonstrated for the advection diffusion problem in 1D-t and 2D-t dimensions.

  13. Statistically significant contrasts between EMG waveforms revealed using wavelet-based functional ANOVA.

    PubMed

    McKay, J Lucas; Welch, Torrence D J; Vidakovic, Brani; Ting, Lena H

    2013-01-01

    We developed wavelet-based functional ANOVA (wfANOVA) as a novel approach for comparing neurophysiological signals that are functions of time. Temporal resolution is often sacrificed by analyzing such data in large time bins, increasing statistical power by reducing the number of comparisons. We performed ANOVA in the wavelet domain because differences between curves tend to be represented by a few temporally localized wavelets, which we transformed back to the time domain for visualization. We compared wfANOVA and ANOVA performed in the time domain (tANOVA) on both experimental electromyographic (EMG) signals from responses to perturbation during standing balance across changes in peak perturbation acceleration (3 levels) and velocity (4 levels) and on simulated data with known contrasts. In experimental EMG data, wfANOVA revealed the continuous shape and magnitude of significant differences over time without a priori selection of time bins. However, tANOVA revealed only the largest differences at discontinuous time points, resulting in features with later onsets and shorter durations than those identified using wfANOVA (P < 0.02). Furthermore, wfANOVA required significantly fewer (~1/4;×; P < 0.015) significant F tests than tANOVA, resulting in post hoc tests with increased power. In simulated EMG data, wfANOVA identified known contrast curves with a high level of precision (r(2) = 0.94 ± 0.08) and performed better than tANOVA across noise levels (P < <0.01). Therefore, wfANOVA may be useful for revealing differences in the shape and magnitude of neurophysiological signals (e.g., EMG, firing rates) across multiple conditions with both high temporal resolution and high statistical power. PMID:23100136

  14. Non parametric denoising methods based on wavelets: Application to electron microscopy images in low exposure time

    SciTech Connect

    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

  15. Gaseous emboli detection based on a dual-wavelet transform analysis.

    PubMed

    Ng, H S; Nygaard, H; Hasenkam, J M; Johansen, P

    2007-08-01

    Emboli monitoring is nowadays based on the assessment of microembolic signals by Doppler ultrasound. However, the present systems have problems in detecting multiple emboli. A more dedicated algorithm for post-processing of the recorded Doppler signals was proposed. Based on the hypothesis that single and multiple gaseous emboli can be quantified by combining discrete and continuous wavelet transformation, the aim of this study was to detect gaseous emboli and to validate our method visually. A flow rig was used where gaseous emboli were generated. Doppler signals and visual validation data of gaseous emboli were acquired simultaneously. Microembolic signals were extracted and analysed using wavelet transformation. Results were validated against a visual reference. At various degrees of bubble generation, the system had 100 per cent detection during a low frequency of bubble generation but an estimation error of 7.4 per cent during a high frequency of bubble generation. The estimation error varied between -7.4 and +3 per cent. The system had a higher rate of success in detecting large gaseous emboli in small numbers than small gaseous emboli in large numbers. Single and double emboli were successfully detected and separated, whereas gaseous emboli clouds could be detected but not quantified. Being able to separate simultaneous gaseous emboli may offer new means of increasing detectability for embolism monitoring.

  16. Wavelet-based image fusion for securing multiple images through asymmetric keys

    NASA Astrophysics Data System (ADS)

    Mehra, Isha; Nishchal, Naveen K.

    2015-01-01

    Image fusion is one of the popular methods which provides better quality fused image for interpreting an image data. Discrete wavelet transform based fusion technique is one such method, in which low and high frequency components are merged together to improve the image content. In this paper, we propose this fusion technique for generating asymmetric keys for securing multiple images. An input image to be encrypted is digitally encoded into two phase-only masks employing the principle of optical interference. This process has been repeated for three different input images; however, it can be extended to n images. Now, one of the phase-only masks corresponding to each input image is preserved as a phase key while another set of phase masks are fused together. This fused image is called the encrypted image. Unlike optical asymmetric encryption technique based on amplitude- and phase-truncation approach, here, four asymmetric keys are generated corresponding to each image. Asymmetric keys corresponding to each image, fractional orders, phase-only masks, level of decomposition and type of wavelet, enlarge the key space and hence offer enhanced security. The proposed method is demonstrated through the simulation results.

  17. Exploring Weak and Overlapped Returns of a LIDAR Waveform with a Wavelet-Based Echo Detector

    NASA Astrophysics Data System (ADS)

    Wang, C. K.

    2012-08-01

    Full waveform data recording the reflected laser signal from ground objects have been provided by some commercial airborne LIDAR systems in the last few years. Waveform data enable users to explore more information and characteristics of the earth surface than conventional LIDAR point cloud. An important application is to extract extra point clouds from waveform data in addition to the point cloud generated by the online process of echo detection. Some difficult-to-detect points, which may be important to topographic mapping, can be rediscovered from waveform data. The motivation of this study is to explore weak and overlapped returns of a waveform. This paper presents a wavelet-based echo detection algorithm, which is compared with the zero-crossing detection method for evaluation. Some simulated waveforms deteriorated with different noises are made to test the limitations of the detector. The experimental results show that the wavelet-based detector outperformed the zero-crossing detector in both difficult-to-detect cases. The detector is also applied to a real waveform dataset. In addition to the total number of echoes provided by the instrument, the detector found 18% more of echoes. The proposed detector is significant in finding weak and overlapped returns from waveforms.

  18. Wavelet-Based Artifact Identification and Separation Technique for EEG Signals during Galvanic Vestibular Stimulation

    PubMed Central

    Adib, Mani; Cretu, Edmond

    2013-01-01

    We present a new method for removing artifacts in electroencephalography (EEG) records during Galvanic Vestibular Stimulation (GVS). The main challenge in exploiting GVS is to understand how the stimulus acts as an input to brain. We used EEG to monitor the brain and elicit the GVS reflexes. However, GVS current distribution throughout the scalp generates an artifact on EEG signals. We need to eliminate this artifact to be able to analyze the EEG signals during GVS. We propose a novel method to estimate the contribution of the GVS current in the EEG signals at each electrode by combining time-series regression methods with wavelet decomposition methods. We use wavelet transform to project the recorded EEG signal into various frequency bands and then estimate the GVS current distribution in each frequency band. The proposed method was optimized using simulated signals, and its performance was compared to well-accepted artifact removal methods such as ICA-based methods and adaptive filters. The results show that the proposed method has better performance in removing GVS artifacts, compared to the others. Using the proposed method, a higher signal to artifact ratio of −1.625 dB was achieved, which outperformed other methods such as ICA-based methods, regression methods, and adaptive filters. PMID:23956786

  19. Accelerating patch-based directional wavelets with multicore parallel computing in compressed sensing MRI.

    PubMed

    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

  20. Measurement of load impedance in power cables using wavelet-transform-based time-frequency domain reflectometry

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

    In this paper, wavelet-transform-based time-frequency domain reflectometry (WTFDR) is proposed for load impedance measurement. In order to measure the load impedance, the energy of the measured signal in the time-frequency domain, the phase difference between the reference signal and the reflected signal, the characteristic impedance, and the attenuation factor of the measured cable must all be known. Since the complex wavelet transform is composed of real and imaginary parts, the phase difference is easily obtained using the ratio of the real coefficient to the imaginary coefficient. In addition, the wavelet energy denotes the sum of the square of the modulus of the wavelet transform and describes the energy of the measured signal in the time and frequency domains. To accurately determine the characteristic impedance and attenuation factors, the power cable should be estimated as a coaxial cable. Using WTFDR with the complex mother wavelet and the estimated power cable, the load impedance can be obtained simply and accurately. Finally, real experiments for the evaluation of various load impedances are carried out to confirm the effectiveness and accuracy of the proposed method compared to the conventional time-frequency domain reflectometry.

  1. Segmentation of complementary DNA microarray images by wavelet-based Markov random field model.

    PubMed

    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).

  2. Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

    PubMed

    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

  3. Detection of damage in multiwire cables based on wavelet entropy evolution

    NASA Astrophysics Data System (ADS)

    Ibáñez, Flor; Baltazar, Arturo; Mijarez, Rito

    2015-08-01

    Multiwire cables are widely used in important engineering structures. Since they are exposed to several dynamic and static loads as well as detrimental environmental conditions, their structural health can be compromised. Due to the critical role played by multiwire cables, it is necessary to develop a non-destructive health monitoring method to maintain their structure and proper performance. Ultrasonic inspection using guided waves is a promising non-destructive damage monitoring technique for rods, single and multiwire cables. However, the propagated guided waves are composed of an infinite number of dispersive vibrational modes making their analysis difficult. In this work, an entropy-based method to identify small changes in non-stationary signals is proposed. An experimental system to capture and post-process acoustic signals is implemented. The discrete wavelet transform is computed in order to obtain the reconstructed wavelet coefficients of the signals and to analyze the energy at different scales. The use of the concept of entropy evolution of non-stationary signals to detect damage in multiwire cables is evaluated. The results show that there is a correlation between the entropy value and level of damage of the cable including breaking of single wires and change in the mechanical contact conditions among the wires. It is found that the studied method has low sensitivity to signal noise and can reduce the computational complexity encountered in a typical time-frequency analysis.

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

    PubMed Central

    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

  5. 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.

  6. A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

    PubMed

    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.

  7. Wavelet-Based ECG Steganography for Protecting Patient Confidential Information in Point-of-Care Systems.

    PubMed

    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.

  8. Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study

    PubMed Central

    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

  9. A clustering-based fuzzy wavelet neural network model for short-term load forecasting.

    PubMed

    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

  10. Wavelet-based AR-SVM for health monitoring of smart structures

    NASA Astrophysics Data System (ADS)

    Kim, Yeesock; Chong, Jo Woon; Chon, Ki H.; Kim, JungMi

    2013-01-01

    This paper proposes a novel structural health monitoring framework for damage detection of smart structures. The framework is developed through the integration of the discrete wavelet transform, an autoregressive (AR) model, damage-sensitive features, and a support vector machine (SVM). The steps of the method are the following: (1) the wavelet-based AR (WAR) model estimates vibration signals obtained from both the undamaged and damaged smart structures under a variety of random signals; (2) a new damage-sensitive feature is formulated in terms of the AR parameters estimated from the structural velocity responses; and then (3) the SVM is applied to each group of damaged and undamaged data sets in order to optimally separate them into either damaged or healthy groups. To demonstrate the effectiveness of the proposed structural health monitoring framework, a three-story smart building equipped with a magnetorheological (MR) damper under artificial earthquake signals is studied. It is shown from the simulation that the proposed health monitoring scheme is effective in detecting damage of the smart structures in an efficient way.

  11. Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

    PubMed

    Sappa, Angel D; Carvajal, Juan A; Aguilera, Cristhian A; Oliveira, Miguel; Romero, Dennis; Vintimilla, Boris X

    2016-06-10

    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).

  12. Damage detection on sudden stiffness reduction based on discrete wavelet transform.

    PubMed

    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.

  13. Non-invasive baroreflex sensitivity assessment using wavelet transfer function-based time-frequency analysis.

    PubMed

    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

  14. Damage detection on sudden stiffness reduction based on discrete wavelet transform.

    PubMed

    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

  15. 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.

  16. A Wiener-Wavelet-Based filter for de-noising satellite soil moisture retrievals

    NASA Astrophysics Data System (ADS)

    Massari, Christian; Brocca, Luca; Ciabatta, Luca; Moramarco, Tommaso; Su, Chun-Hsu; Ryu, Dongryeol; Wagner, Wolfgang

    2014-05-01

    The reduction of noise in microwave satellite soil moisture (SM) retrievals is of paramount importance for practical applications especially for those associated with the study of climate changes, droughts, floods and other related hydrological processes. So far, Fourier based methods have been used for de-noising satellite SM retrievals by filtering either the observed emissivity time series (Du, 2012) or the retrieved SM observations (Su et al. 2013). This contribution introduces an alternative approach based on a Wiener-Wavelet-Based filtering (WWB) technique, which uses the Entropy-Based Wavelet de-noising method developed by Sang et al. (2009) to design both a causal and a non-causal version of the filter. WWB is used as a post-retrieval processing tool to enhance the quality of observations derived from the i) Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), ii) the Advanced SCATterometer (ASCAT), and iii) the Soil Moisture and Ocean Salinity (SMOS) satellite. The method is tested on three pilot sites located in Spain (Remedhus Network), in Greece (Hydrological Observatory of Athens) and in Australia (Oznet network), respectively. Different quantitative criteria are used to judge the goodness of the de-noising technique. Results show that WWB i) is able to improve both the correlation and the root mean squared differences between satellite retrievals and in situ soil moisture observations, and ii) effectively separates random noise from deterministic components of the retrieved signals. Moreover, the use of WWB de-noised data in place of raw observations within a hydrological application confirms the usefulness of the proposed filtering technique. Du, J. (2012), A method to improve satellite soil moisture retrievals based on Fourier analysis, Geophys. Res. Lett., 39, L15404, doi:10.1029/ 2012GL052435 Su,C.-H.,D.Ryu, A. W. Western, and W. Wagner (2013), De-noising of passive and active microwave satellite soil moisture time

  17. [A P-wave detection method based on multi-feature].

    PubMed

    Song, Lixin; Guan, Lili; Wang, Qian; Wang, Yuhong

    2014-04-01

    Generally, P-wave is the wave of low-frequency and low-amplitude, and it could be affected by baseline drift, electromyography (EMG) interference and other noises easily. Not every heart beat contains the P-wave, and it is also a major problem to determine the P-wave exist or not in a heart beat. In order to solve the limitation of suiting the diverse morphological P-wave using wavelet-amplitude-transform algorithm and the limitation of selecting the pseudo-P-wave sample using the wavelet transform and neural network, we presented new P-wave detecting method based on wave-amplitude threshold and using the multi-feature as the input of neural networks. Firstly, we removed the noise of ECG through the wavelet transform, then determined the position of the candidate P-wave by calculating modulus maxima of the wavelet transform, and then determine the P-wave exist or not by wave-amplitude threshold method initially. Finally we determined whether the P-wave existed or not by the neural networks. The method is validated based on the QT database which is supplied with manual labels made by physicians. We compared the detection effect of ECG P-waves, which was obtained with the method developed in the study, with the algorithm of wavelet threshold value and the method based on "wavelet-amplitude-slope", and verified the feasibility of the proposed algorithm. The detected ECG signal, which is recorded in the hospital ECG division, was consistent with the doctor's labels. Furthermore, after detecting the 13 sets of ECG which were 15 min long, the detection rate for the correct P-wave is 99.911%.

  18. A wavelet-based metric for visual texture discrimination with applications in evolutionary ecology.

    PubMed

    Kiltie, R A; Fan, J; Laine, A F

    1995-03-01

    Much work on natural and sexual selection is concerned with the conspicuousness of visual patterns (textures) on animal and plant surfaces. Previous attempts by evolutionary biologists to quantify apparency of such textures have involved subjective estimates of conspicuousness or statistical analyses based on transect samples. We present a method based on wavelet analysis that avoids subjectivity and that uses more of the information in image textures than transects do. Like the human visual system for texture discrimination, and probably like that of other vertebrates, this method is based on localized analysis of orientation and frequency components of the patterns composing visual textures. As examples of the metric's utility, we present analyses of crypsis for tigers, zebras, and peppered moth morphs.

  19. New type of wavelet-based spectral analysis by which modes with different toroidal mode number are separated

    NASA Astrophysics Data System (ADS)

    Ohdachi, S.

    2016-11-01

    A new type of wavelet-based analysis for the magnetic fluctuations by which toroidal mode number can be resolved is proposed. By using a wavelet, having a different phase toroidally, a spectrogram with a specific toroidal mode number can be obtained. When this analysis is applied to the measurement of the fluctuations observed in the large helical device, MHD activities having similar frequency in the laboratory frame can be separated from the difference of the toroidal mode number. It is useful for the non-stationary MHD activity. This method is usable when the toroidal magnetic probes are not symmetrically distributed.

  20. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction

    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.

  1. Detection of multi-scale secondary flow structures using anisotropic 2D Ricker wavelets in a bent tube model for curved arteries

    NASA Astrophysics Data System (ADS)

    Plesniak, Daniel H.; Bulusu, Kartik V.; Plesniak, Michael W.

    2012-11-01

    Interpretation of complex flow patterns observed in this study of a model curved artery required characterization of multiple, low-circulation secondary flow structures that were observed during the late systolic deceleration and diastolic phases under physiological inflow conditions. Phase-locked, planar vorticity PIV data were acquired at various cross-sectional locations of the 180-degree bent tube model. High circulation, deformed Dean- and Lyne-type vortices were observed during early stages of deceleration, while several smaller scale, highly deformed, low-circulation vortical patterns appeared in the core and near-wall regions during late systolic deceleration and diastolic phases. Due to the multiplicity of vortical scales and shapes, anisotropic 2D Ricker wavelets were used for coherent structure detection in a continuous wavelet transform algorithm (PIVlet 1.2). Our bio-inspired study is geared towards understanding whether optimizing the shape of the wavelet kernel will enable better resolution of several low-circulation, multi-scale secondary flow morphologies and whether new insights into the dynamics of arterial secondary flow structures can accordingly be gained. Supported by the National Science Foundation, Grant No. CBET-0828903 and GW Center for Biomimetics and Bioinspired Engineering (COBRE).

  2. An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis

    PubMed Central

    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

  3. An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis.

    PubMed

    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

  4. An Improved Method of Parameter Identification and Damage Detection in Beam Structures under Flexural Vibration Using Wavelet Multi-Resolution Analysis.

    PubMed

    Ravanfar, Seyed Alireza; Razak, Hashim Abdul; Ismail, Zubaidah; Monajemi, Hooman

    2015-09-09

    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.

  5. Mobile Healthcare for Automatic Driving Sleep-Onset Detection Using Wavelet-Based EEG and Respiration Signals

    PubMed Central

    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

  6. Research on the technique of public watermarking system based on wavelet transform and neural network

    NASA Astrophysics Data System (ADS)

    Xu, Li; Tao, Gu

    2007-04-01

    A hybrid algorithm of using a wavelet transform and a neural network is presented which solves the problems confronted in public watermarking systems. First, to get the wavelet coefficients, db1 wavelet is used to decompose the selected image. Second, to ensure better quality of the watermarked image, some wavelet coefficients and their closely relevant wavelet coefficients are randomly selected from the wavelet coefficients decomposed by the low pass filter and used to establish the relational model by using a neural network. Third, the bit information of the watermark is also enlarged by increasing the amount of zeros or ones and then one bit of the results is embedded by adjusting the polarity between a chosen wavelet coefficient and the output value of the model. Finally, a new image with watermark information is reconstructed by using the modified wavelet coefficients and other unmodified wavelet coefficients. On the other hand, the process of retrieving the watermark is the inverse of the embedding process. The embedded watermark can also be retrieved by using the hybrid algorithm and the restore function without knowing the original image and watermark. Experimental results show that the proposed technique is very robust against some image processing operations and JPEG lossy compression. Meanwhile, the extracted watermark can be proved by the proposed method. Because of the neural network, the proposed method is also robust against attack of false authentication. Therefore, the hybrid algorithm can be used to protect the copyright of one important image.

  7. Gravity inversion using wavelet-based compression on parallel hybrid CPU/GPU systems: application to southwest Ghana

    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.

  8. Curvelet-based registration of multi-component seismic waves

    NASA Astrophysics Data System (ADS)

    Wang, Hairong; Cheng, Yuanfeng; Ma, Jianwei

    2014-05-01

    Registration of the travel time of PP waves and PS waves on the same coordinate is critical for joint interpretation in multi-component seismic exploration. In this paper, we propose a new curvelet-based registration method to improve the precision of registration, especially for the data with heavy random noises. By making registration in curvelet multiscale spaces from coarser to finer scale, the proposed method is not sensitive to initial values of velocity ratio of PP waves and PS waves. Applications of the new method to real seismic dataset from Shengli Oilfield, China show good registered results in terms of both qualitative and quantitative analysis, in comparison with a traditional registration method and a wavelet-based method.

  9. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM.

    PubMed

    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.

  10. Assessment of Power Quality based on Fuzzy Logic and Discrete Wavelet Transform for Nonstationary Disturbances

    NASA Astrophysics Data System (ADS)

    Sinha, Pampa; Nath, Sudipta

    2010-10-01

    The main aspects of power system delivery are reliability and quality. If all the customers of a power system get uninterrupted power through the year then the system is considered to be reliable. The term power quality may be referred to as maintaining near sinusoidal voltage at rated frequency at the consumers end. The power component definitions are defined according to the IEEE Standard 1459-2000 both for single phase and three phase unbalanced systems based on Fourier Transform (FFT). In the presence of nonstationary power quality (PQ) disturbances results in accurate values due to its sensitivity to the spectral leakage problem. To overcome these limitations the power quality components are calculated using Discrete Wavelet Transform (DWT). In order to handle the uncertainties associated with electric power systems operations fuzzy logic has been incorporated in this paper. A new power quality index has been introduced here which can assess the power quality under nonstationary disturbances.

  11. A wavelet transform based feature extraction and classification of cardiac disorder.

    PubMed

    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

  12. Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm.

    PubMed

    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.

  13. Patch-ordering-based wavelet frame and its use in inverse problems.

    PubMed

    Ram, Idan; Cohen, Israel; Elad, Michael

    2014-07-01

    In our previous work [1] we have introduced a redundant tree-based wavelet transform (RTBWT), originally designed to represent functions defined on high dimensional data clouds and graphs. We have further shown that RTBWT can be used as a highly effective image-adaptive redundant transform that operates on an image using orderings of its overlapped patches. The resulting transform is robust to corruptions in the image, and thus able to efficiently represent the unknown target image even when it is calculated from its corrupted version. In this paper, we utilize this redundant transform as a powerful sparsity-promoting regularizer in inverse problems in image processing. We show that the image representation obtained with this transform is a frame expansion, and derive the analysis and synthesis operators associated with it. We explore the use of this frame operators to image denoising and deblurring, and demonstrate in both these cases state-of-the-art results.

  14. 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.

  15. Prognostics of Lithium-Ion Batteries Based on Wavelet Denoising and DE-RVM

    PubMed Central

    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

  16. Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform.

    PubMed

    Lai, Zongying; Qu, Xiaobo; Liu, Yunsong; Guo, Di; Ye, Jing; Zhan, Zhifang; Chen, Zhong

    2016-01-01

    Compressed sensing magnetic resonance imaging has shown great capacity for accelerating magnetic resonance imaging if an image can be sparsely represented. How the image is sparsified seriously affects its reconstruction quality. In the present study, a graph-based redundant wavelet transform is introduced to sparsely represent magnetic resonance images in iterative image reconstructions. With this transform, image patches is viewed as vertices and their differences as edges, and the shortest path on the graph minimizes the total difference of all image patches. Using the l1 norm regularized formulation of the problem solved by an alternating-direction minimization with continuation algorithm, the experimental results demonstrate that the proposed method outperforms several state-of-the-art reconstruction methods in removing artifacts and achieves fewer reconstruction errors on the tested datasets.

  17. Optimum wavelet based masking for the contrast enhancement of medical images using enhanced cuckoo search algorithm.

    PubMed

    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

  18. 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.

  19. Assessment of flow regime alterations over a spectrum of temporal scales using wavelet-based approaches

    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

  20. Wavelets and electromagnetics

    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.

  1. Devil's vortex Fresnel lens phase masks on an asymmetric cryptosystem based on phase-truncation in gyrator wavelet transform domain

    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.

  2. An Improved Wavelet Packet-Chaos Model for Life Prediction of Space Relays Based on Volterra Series

    PubMed Central

    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

  3. Pattern discrimination of joint transform correlator based on wavelet subband filtering

    NASA Astrophysics Data System (ADS)

    Lin, Li-Chien; Cheng, Chau-Jern

    2004-04-01

    We propose and demonstrate a Gabor wavelet prefiltering prior to classical and binarized joint transform correlator implementation to enhance texture features of fingerprints. The frequency- and orientation-selective properties of the wavelet subband filter are utilized to extract important textural features for optimal correlation recognition. A selection criterion for wavelet subbands is derived, and it is shown that the maximum signal-to-noise ratio of the correlator is achieved by optimizing the threshold level. Simulation results show that the proposed method increases the discrimination power of the correlator, especially under noisy environments.

  4. Wavelet-based filter methods for the detection of small transiting planets: Application to Kepler and K2 light curves

    NASA Astrophysics Data System (ADS)

    Grziwa, Sascha; Korth, Judith; Paetzold, Martin; KEST

    2016-10-01

    The Rheinisches Institut für Umweltforschung (RIU-PF) has developed the software package EXOTRANS for the detection of transits of exoplanets in stellar light curves. This software package was in use during the CoRoT space mission (2006-2013). EXOTRANS was improved by different wavelet-based filter methods during the following years to separate stellar variation, orbital disturbances and instrumental effects from stellar light curves taken by space telescopes (Kepler, K2, TESS and PLATO). The VARLET filter separates faint transit signals from stellar variations without using a-priori information about the target star. VARLET considers variations by frequency, amplitude and shape simultaneously. VARLET is also able to extract most instrumental jumps and glitches. The PHALET filter separates periodic features independent of their shape and is used with the intention to separate diluting stellar binaries. It is also applied for the multi transit search. Stellar light curves of the K2 mission are constructed from the processing of target pixel files which corrects disturbances caused by the reduced pointing precision of the Kepler telescope after the failure of two gyroscopes. The combination of target pixel file processing with both filter techniques and the proven detection pipeline EXOTRANS lowers the detection limit, reduces false alarms and simplifies the detection of faint transits in light curves of the K2 mission. Using EXOTRANS many new candidates were detected in K2 light curves by using EXOTRANS which were successfully confirmed by ground-based follow-up observation of the KEST collaboration. New candidates and confirmed planets are presented.

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

    SciTech Connect

    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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  8. Image encryption algorithm based on wavelet packet decomposition and discrete linear canonical transform

    NASA Astrophysics Data System (ADS)

    Sharma, K. K.; Jain, Heena

    2013-01-01

    The security of digital data including images has attracted more attention recently, and many different image encryption methods have been proposed in the literature for this purpose. In this paper, a new image encryption method using wavelet packet decomposition and discrete linear canonical transform is proposed. The use of wavelet packet decomposition and DLCT increases the key size significantly making the encryption more robust. Simulation results of the proposed technique are also presented.

  9. Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging.

    PubMed

    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

  10. Wavelet Domain Radiofrequency Pulse Design Applied to Magnetic Resonance Imaging

    PubMed Central

    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

  11. A wavelet-MRA-based adaptive semi-Lagrangian method for the relativistic Vlasov Maxwell system

    NASA Astrophysics Data System (ADS)

    Besse, Nicolas; Latu, Guillaume; Ghizzo, Alain; Sonnendrücker, Eric; Bertrand, Pierre

    2008-08-01

    In this paper we present a new method for the numerical solution of the relativistic Vlasov-Maxwell system on a phase-space grid using an adaptive semi-Lagrangian method. The adaptivity is performed through a wavelet multiresolution analysis, which gives a powerful and natural refinement criterion based on the local measurement of the approximation error and regularity of the distribution function. Therefore, the multiscale expansion of the distribution function allows to get a sparse representation of the data and thus save memory space and CPU time. We apply this numerical scheme to reduced Vlasov-Maxwell systems arising in laser-plasma physics. Interaction of relativistically strong laser pulses with overdense plasma slabs is investigated. These Vlasov simulations revealed a rich variety of phenomena associated with the fast particle dynamics induced by electromagnetic waves as electron trapping, particle acceleration, and electron plasma wavebreaking. However, the wavelet based adaptive method that we developed here, does not yield significant improvements compared to Vlasov solvers on a uniform mesh due to the substantial overhead that the method introduces. Nonetheless they might be a first step towards more efficient adaptive solvers based on different ideas for the grid refinement or on a more efficient implementation. Here the Vlasov simulations are performed in a two-dimensional phase-space where the development of thin filaments, strongly amplified by relativistic effects requires an important increase of the total number of points of the phase-space grid as they get finer as time goes on. The adaptive method could be more useful in cases where these thin filaments that need to be resolved are a very small fraction of the hyper-volume, which arises in higher dimensions because of the surface-to-volume scaling and the essentially one-dimensional structure of the filaments. Moreover, the main way to improve the efficiency of the adaptive method is to

  12. Highly efficient codec based on significance-linked connected-component analysis of wavelet coefficients

    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.

  13. Selective error detection for error-resilient wavelet-based image coding.

    PubMed

    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.

  14. Speckle-reduction algorithm for ultrasound images in complex wavelet domain using genetic algorithm-based mixture model.

    PubMed

    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

  15. A wavelet-based non-linear autoregressive with exogenous inputs (WNARX) dynamic neural network model for real-time flood forecasting using satellite-based rainfall products

    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

  16. Wavelet-based multifractal analysis of earthquakes temporal distribution in Mammoth Mountain volcano, Mono County, Eastern California

    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.

  17. A wavelet-based single-view reconstruction approach for cone beam x-ray luminescence tomography imaging

    PubMed Central

    Liu, Xin; Wang, Hongkai; Xu, Mantao; Nie, Shengdong; Lu, Hongbing

    2014-01-01

    Single-view x-ray luminescence computed tomography (XLCT) imaging has short data collection time that allows non-invasively and fast resolving the three-dimensional (3-D) distribution of x-ray-excitable nanophosphors within small animal in vivo. However, the single-view reconstruction suffers from a severe ill-posed problem because only one angle data is used in the reconstruction. To alleviate the ill-posedness, in this paper, we propose a wavelet-based reconstruction approach, which is achieved by applying a wavelet transformation to the acquired singe-view measurements. To evaluate the performance of the proposed method, in vivo experiment was performed based on a cone beam XLCT imaging system. The experimental results demonstrate that the proposed method cannot only use the full set of measurements produced by CCD, but also accelerate image reconstruction while preserving the spatial resolution of the reconstruction. Hence, it is suitable for dynamic XLCT imaging study. PMID:25426315

  18. A Comprehensive Noise Robust Speech Parameterization Algorithm Using Wavelet Packet Decomposition-Based Denoising and Speech Feature Representation Techniques

    NASA Astrophysics Data System (ADS)

    Kotnik, Bojan; Kačič, Zdravko

    2007-12-01

    This paper concerns the problem of automatic speech recognition in noise-intense and adverse environments. The main goal of the proposed work is the definition, implementation, and evaluation of a novel noise robust speech signal parameterization algorithm. The proposed procedure is based on time-frequency speech signal representation using wavelet packet decomposition. A new modified soft thresholding algorithm based on time-frequency adaptive threshold determination was developed to efficiently reduce the level of additive noise in the input noisy speech signal. A two-stage Gaussian mixture model (GMM)-based classifier was developed to perform speech/nonspeech as well as voiced/unvoiced classification. The adaptive topology of the wavelet packet decomposition tree based on voiced/unvoiced detection was introduced to separately analyze voiced and unvoiced segments of the speech signal. The main feature vector consists of a combination of log-root compressed wavelet packet parameters, and autoregressive parameters. The final output feature vector is produced using a two-staged feature vector postprocessing procedure. In the experimental framework, the noisy speech databases Aurora 2 and Aurora 3 were applied together with corresponding standardized acoustical model training/testing procedures. The automatic speech recognition performance achieved using the proposed noise robust speech parameterization procedure was compared to the standardized mel-frequency cepstral coefficient (MFCC) feature extraction procedures ETSI ES 201 108 and ETSI ES 202 050.

  19. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs.

    PubMed

    Zhu, Biyun; Chen, Hui; Chen, Budong; Xu, Yan; Zhang, Kuan

    2014-02-01

    This study aims to explore the classification ability of decision trees (DTs) and support vector machines (SVMs) to discriminate between the digital chest radiographs (DRs) of pneumoconiosis patients and control subjects. Twenty-eight wavelet-based energy texture features were calculated at the lung fields on DRs of 85 healthy controls and 40 patients with stage I and stage II pneumoconiosis. DTs with algorithm C5.0 and SVMs with four different kernels were trained by samples with two combinations of the texture features to classify a DR as of a healthy subject or of a patient with pneumoconiosis. All of the models were developed with fivefold cross-validation, and the final performances of each model were compared by the area under receiver operating characteristic (ROC) curve. For both SVM (with a radial basis function kernel) and DT (with algorithm C5.0), areas under ROC curves (AUCs) were 0.94 ± 0.02 and 0.86 ± 0.04 (P = 0.02) when using the full feature set and 0.95 ± 0.02 and 0.88 ± 0.04 (P = 0.05) when using the selected feature set, respectively. When built on the selected texture features, the SVM with a polynomial kernel showed a higher diagnostic performance with an AUC value of 0.97 ± 0.02 than SVMs with a linear kernel, a radial basis function kernel and a sigmoid kernel with AUC values of 0.96 ± 0.02 (P = 0.37), 0.95 ± 0.02 (P = 0.24), and 0.90 ± 0.03 (P = 0.01), respectively. The SVM model with a polynomial kernel built on the selected feature set showed the highest diagnostic performance among all tested models when using either all the wavelet texture features or the selected ones. The model has a good potential in diagnosing pneumoconiosis based on digital chest radiographs.

  20. TME12/400: Application Oriented Wavelet-based Coding of Volumetric Medical Data

    PubMed Central

    Menegaz, G; Grewe, L; Lozano, A; Thiran, J-Ph

    1999-01-01

    Introduction While medical data are increasingly acquired in a multidimensional space, in clinical practice they are mainly still analyzed as images. We propose a wavelet-based coding technique exploiting the full dimensionality of the data distribution while allowing to recover a single image without any need to decode the whole volume. The proposed compression scheme is based on the Layered Zero Coding (LZC) method. Two modes are considered. In the progressive (PROG) mode, the volume is processed as a whole, while in the layer-per-layer (LPL) one each layer of each sub-band is encoded independently. The three-dimensional extension of the Embedded Zerotree Wavelet (EZW) coder is used as reference for coding efficiency. All working modalities provide a fully embedded bit-stream allowing a progressive by quality recovering of the encoded information. Methods The 3D DWT is performed mapping integers to integers thus allowing lossless compression. Two different coding systems have been considered: EZW and LZC. LZC models the expected statistical dependencies among coefficients by defining some conditional terms (contexts) which summarize the significance state of the samples belonging to a generalized neighborhood of the coefficient being encoded. Such terms are then used by a context adaptive arithmetic coder. The LPL mode has been designed in order to be able to independently decode any image of the dataset, and it is derived from the PROG mode by over-constraining the system. The sub-bands are quantized and encoded according to a sequence of uniform quantizers with decreasing step-size. This ensures progressiveness capabilities when decoding both the whole volume and a single image. Results Performances have been evaluated on two datasets: DSR and ANGIO, an opthalmologic angiographic sequence. For each mode the best context has been retained. Results show that the proposed system is competitive with EZW, and PROG mode is the more performant. The main factors

  1. A Wavelet-Based ECG Delineation Method: Adaptation to an Experimental Electrograms with Manifested Global Ischemia.

    PubMed

    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

  2. Texture characterization for joint compression and classification based on human perception in the wavelet domain.

    PubMed

    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

  3. A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis.

    PubMed

    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

  4. Wavelet-based noise-model driven denoising algorithm for differential phase contrast mammography.

    PubMed

    Arboleda, Carolina; Wang, Zhentian; Stampanoni, Marco

    2013-05-01

    Traditional mammography can be positively complemented by phase contrast and scattering x-ray imaging, because they can detect subtle differences in the electron density of a material and measure the local small-angle scattering power generated by the microscopic density fluctuations in the specimen, respectively. The grating-based x-ray interferometry technique can produce absorption, differential phase contrast (DPC) and scattering signals of the sample, in parallel, and works well with conventional X-ray sources; thus, it constitutes a promising method for more reliable breast cancer screening and diagnosis. Recently, our team proved that this novel technology can provide images superior to conventional mammography. This new technology was used to image whole native breast samples directly after mastectomy. The images acquired show high potential, but the noise level associated to the DPC and scattering signals is significant, so it is necessary to remove it in order to improve image quality and visualization. The noise models of the three signals have been investigated and the noise variance can be computed. In this work, a wavelet-based denoising algorithm using these noise models is proposed. It was evaluated with both simulated and experimental mammography data. The outcomes demonstrated that our method offers a good denoising quality, while simultaneously preserving the edges and important structural features. Therefore, it can help improve diagnosis and implement further post-processing techniques such as fusion of the three signals acquired.

  5. A Stationary Wavelet Entropy-Based Clustering Approach Accurately Predicts Gene Expression

    PubMed Central

    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

  6. A Fractal Dimension and Wavelet Transform Based Method for Protein Sequence Similarity Analysis.

    PubMed

    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.

  7. Rough-Fuzzy Clustering and Unsupervised Feature Selection for Wavelet Based MR Image Segmentation

    PubMed Central

    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

  8. Multiresolution Modeling of Polymer Solutions: Wavelet-Based Coarse-Graining and Reverse-Mapping

    NASA Astrophysics Data System (ADS)

    Ismail, Ahmed; Adorf, Carl Simon; Agarwal, Animesh; Iacovella, Christopher R.

    2014-03-01

    Unlike multiscale methods, which encompass multiple simulation techniques, multiresolution models uses one modeling technique at different length and time scales. We present a combined coarse-graining and reverse-mapping framework for modeling of semidilute polymer solutions, based on the wavelet-accelerated Monte Carlo (WAMC) method, which forms a hierarchy of resolutions to model polymers at length scales that cannot be reached via atomistic or even ``standard'' coarse-grained simulations. A universal scaling function is obtained so that potentials do not need to be recomputed as the scale of the system is changed. We show that coarse-grained polymer solutions can reproduce results obtained from the simulations of the more detailed atomistic system to a reasonable degree of accuracy. Reverse mapping proceeds similarly: using probability distributions obtained from coarse-graining the bond lengths, angles, torsions, and the non-bonded potentials, we can reconstruct a more detailed polymer consistent with both geometric constraints and energetic considerations. Using a ``convergence factor'' within a Monte Carlo-based energy optimization scheme, we can successfully reconstruct entire atomistic configurations from coarse-grained descriptions.

  9. 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.

  10. Wavelet-transform-based power management of hybrid vehicles with multiple on-board energy sources including fuel cell, battery and ultracapacitor

    NASA Astrophysics Data System (ADS)

    Zhang, Xi; Mi, Chris Chunting; Masrur, Abul; Daniszewski, David

    A wavelet-transform-based strategy is proposed for the power management of hybrid electric vehicles (HEV) with multiple on-board energy sources and energy storage systems including a battery, a fuel cell, and an ultra-capacitor. The proposed wavelet-transform algorithm is capable of identifying the high-frequency transient and real time power demand of the HEV, and allocating power components with different frequency contents to corresponding sources to achieve an optimal power management control algorithm. By using the wavelet decomposition algorithm, a proper combination can be achieved with a properly sized ultra-capacitor dealing with the chaotic high-frequency components of the total power demand, while the fuel cell and battery deal with the low and medium frequency power demand. Thus the system efficiency and life expectancy can be greatly extended. Simulation and experimental results validated the effectiveness of wavelet-transform-based power management algorithm.

  11. An improved filtering method based on EEMD and wavelet-threshold for modal parameter identification of hydraulic structure

    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.

  12. Spectral Laplace-Beltrami wavelets with applications in medical images.

    PubMed

    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.

  13. Online Epileptic Seizure Prediction Using Wavelet-Based Bi-Phase Correlation of Electrical Signals Tomography.

    PubMed

    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

  14. 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.

  15. Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis

    NASA Astrophysics Data System (ADS)

    Ivanov, Plamen Ch.; Rosenblum, Michael G.; Peng, C.-K.; Mietus, Joseph; Havlin, Shlomo; Stanley, H. Eugene; Goldberger, Ary L.

    1996-09-01

    BIOLOGICAL time-series analysis is used to identify hidden dynamical patterns which could yield important insights into underlying physiological mechanisms. Such analysis is complicated by the fact that biological signals are typically both highly irregular and non-stationary, that is, their statistical character changes slowly or intermittently as a result of variations in background influences1-3. Previous statistical analyses of heartbeat dynamics4-6 have identified long-range correlations and power-law scaling in the normal heartbeat, but not the phase interactions between the different frequency components of the signal. Here we introduce a new approach, based on the wavelet transform and an analytic signal approach, which can characterize non-stationary behaviour and elucidate such phase interactions. We find that, when suitably rescaled, the distributions of the variations in the beat-to-beat intervals for all healthy subjects are described by a single function stable over a wide range of timescales. However, a similar scaling function does not exist for a group with cardiopulmonary instability caused by sleep apnoea. We attribute the functional form of the scaling observed in the healthy subjects to underlying nonlinear dynamics, which seem to be essential to normal heart function. The approach introduced here should be useful in the analysis of other nonstationary biological signals.

  16. Speckle reduction process based on digital filtering and wavelet compounding in optical coherence tomography for dermatology

    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.

  17. A Wavelet Transform Based Method to Determine Depth of Anesthesia to Prevent Awareness during General Anesthesia

    PubMed Central

    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

  18. A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection.

    PubMed

    Islam, Md Kafiul; Rastegarnia, Amir; Yang, Zhi

    2016-09-01

    This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.

  19. A Simple Method for Predicting Transmembrane Proteins Based on Wavelet Transform

    PubMed Central

    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

  20. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    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

  1. [Research on ECG de-noising method based on ensemble empirical mode decomposition and wavelet transform using improved threshold function].

    PubMed

    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.

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

    PubMed

    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.

  3. Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization

    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.

  4. 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.

  5. Wavelet-based feature extraction applied to small-angle x-ray scattering patterns from breast tissue: a tool for differentiating between tissue types.

    PubMed

    Falzon, G; Pearson, S; Murison, R; Hall, C; Siu, K; Evans, A; Rogers, K; Lewis, R

    2006-05-21

    This paper reports on the application of wavelet decomposition to small-angle x-ray scattering (SAXS) patterns from human breast tissue produced by a synchrotron source. The pixel intensities of SAXS patterns of normal, benign and malignant tissue types were transformed into wavelet coefficients. Statistical analysis found significant differences between the wavelet coefficients describing the patterns produced by different tissue types. These differences were then correlated with position in the image and have been linked to the supra-molecular structural changes that occur in breast tissue in the presence of disease. Specifically, results indicate that there are significant differences between healthy and diseased tissues in the wavelet coefficients that describe the peaks produced by the axial d-spacing of collagen. These differences suggest that a useful classification tool could be based upon the spectral information within the axial peaks.

  6. Dual tree fractional quaternion wavelet transform for disparity estimation.

    PubMed

    Kumar, Sanoj; Kumar, Sanjeev; Sukavanam, Nagarajan; Raman, Balasubramanian

    2014-03-01

    This paper proposes a novel phase based approach for computing disparity as the optical flow from the given pair of consecutive images. A new dual tree fractional quaternion wavelet transform (FrQWT) is proposed by defining the 2D Fourier spectrum upto a single quadrant. In the proposed FrQWT, each quaternion wavelet consists of a real part (a real DWT wavelet) and three imaginary parts that are organized according to the quaternion algebra. First two FrQWT phases encode the shifts of image features in the absolute horizontal and vertical coordinate system, while the third phase has the texture information. The FrQWT allowed a multi-scale framework for calculating and adjusting local disparities and executing phase unwrapping from coarse to fine scales with linear computational efficiency. PMID:24388356

  7. An Expert Diagnosis System for Parkinson Disease Based on Genetic Algorithm-Wavelet Kernel-Extreme Learning Machine

    PubMed Central

    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

  8. A wavelet-based intermittency detection technique from PIV investigations in transitional boundary layers

    NASA Astrophysics Data System (ADS)

    Simoni, Daniele; Lengani, Davide; Guida, Roberto

    2016-09-01

    The transition process of the boundary layer growing over a flat plate with pressure gradient simulating the suction side of a low-pressure turbine blade and elevated free-stream turbulence intensity level has been analyzed by means of PIV and hot-wire measurements. A detailed view of the instantaneous flow field in the wall-normal plane highlights the physics characterizing the complex process leading to the formation of large-scale coherent structures during breaking down of the ordered motion of the flow, thus generating randomized oscillations (i.e., turbulent spots). This analysis gives the basis for the development of a new procedure aimed at determining the intermittency function describing (statistically) the transition process. To this end, a wavelet-based method has been employed for the identification of the large-scale structures created during the transition process. Successively, a probability density function of these events has been defined so that an intermittency function is deduced. This latter strictly corresponds to the intermittency function of the transitional flow computed trough a classic procedure based on hot-wire data. The agreement between the two procedures in the intermittency shape and spot production rate proves the capability of the method in providing the statistical representation of the transition process. The main advantages of the procedure here proposed concern with its applicability to PIV data; it does not require a threshold level to discriminate first- and/or second-order time-derivative of hot-wire time traces (that makes the method not influenced by the operator); and it provides a clear evidence of the connection between the flow physics and the statistical representation of transition based on theory of turbulent spot propagation.

  9. Experimental Studies on a Compact Storage Scheme for Wavelet-based Multiresolution Subregion Retrieval

    NASA Technical Reports Server (NTRS)

    Poulakidas, A.; Srinivasan, A.; Egecioglu, O.; Ibarra, O.; Yang, T.

    1996-01-01

    Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images effectively. In order to use them for image library systems, a compact storage scheme for quantized coefficient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.

  10. A generalized wavelet extrema representation

    SciTech Connect

    Lu, Jian; Lades, M.

    1995-10-01

    The wavelet extrema representation originated by Stephane Mallat is a unique framework for low-level and intermediate-level (feature) processing. In this paper, we present a new form of wavelet extrema representation generalizing Mallat`s original work. The generalized wavelet extrema representation is a feature-based multiscale representation. For a particular choice of wavelet, our scheme can be interpreted as representing a signal or image by its edges, and peaks and valleys at multiple scales. Such a representation is shown to be stable -- the original signal or image can be reconstructed with very good quality. It is further shown that a signal or image can be modeled as piecewise monotonic, with all turning points between monotonic segments given by the wavelet extrema. A new projection operator is introduced to enforce piecewise inonotonicity of a signal in its reconstruction. This leads to an enhancement to previously developed algorithms in preventing artifacts in reconstructed signal.

  11. Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

    PubMed Central

    Al-Qazzaz, Noor Kamal; Hamid Bin Mohd Ali, Sawal; Ahmad, Siti Anom; Islam, Mohd Shabiul; Escudero, Javier

    2015-01-01

    We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions. PMID:26593918

  12. Wear detection by means of wavelet-based acoustic emission analysis

    NASA Astrophysics Data System (ADS)

    Baccar, D.; Söffker, D.

    2015-08-01

    Wear detection and monitoring during operation are complex and difficult tasks especially for materials under sliding conditions. Due to the permanent contact and repetitive motion, the material surface remains during tests non-accessible for optical inspection so that attrition of the contact partners cannot be easily detected. This paper introduces the relevant scientific components of reliable and efficient condition monitoring system for online detection and automated classification of wear phenomena by means of acoustic emission (AE) and advanced signal processing approaches. The related experiments were performed using a tribological system consisting of two martensitic plates, sliding against each other. High sensitive piezoelectric transducer was used to provide the continuous measurement of AE signals. The recorded AE signals were analyzed mainly by time-frequency analysis. A feature extraction module using a novel combination of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were used for the first time. A detailed correlation analysis between complex signal characteristics and the surface damage resulting from contact fatigue was investigated. Three wear process stages were detected and could be distinguished. To obtain quantitative and detailed information about different wear phases, the AE energy was calculated using STFT and decomposed into a suitable number of frequency levels. The individual energy distribution and the cumulative AE energy of each frequency components were analyzed using CWT. Results show that the behavior of individual frequency component changes when the wear state changes. Here, specific frequency ranges are attributed to the different wear states. The study reveals that the application of the STFT-/CWT-based AE analysis is an appropriate approach to distinguish and to interpret the different damage states occurred during sliding contact. Based on this results a new generation of condition monitoring

  13. Adaptive Wavelet-Based Direct Numerical Simulations of Rayleigh-Taylor Instability

    NASA Astrophysics Data System (ADS)

    Reckinger, Scott J.

    The compressible Rayleigh-Taylor instability (RTI) occurs when a fluid of low molar mass supports a fluid of higher molar mass against a gravity-like body force or in the presence of an accelerating front. Intrinsic to the problem are highly stratified background states, acoustic waves, and a wide range of physical scales. The objective of this thesis is to develop a specialized computational framework that addresses these challenges and to apply the advanced methodologies for direct numerical simulations of compressible RTI. Simulations are performed using the Parallel Adaptive Wavelet Collocation Method (PAWCM). Due to the physics-based adaptivity and direct error control of the method, PAWCM is ideal for resolving the wide range of scales present in RTI growth. Characteristics-based non-reflecting boundary conditions are developed for highly stratified systems to be used in conjunction with PAWCM. This combination allows for extremely long domains, which is necessary for observing the late time growth of compressible RTI. Initial conditions that minimize acoustic disturbances are also developed. The initialization is consistent with linear stability theory, where the background state consists of two diffusively mixed stratified fluids of differing molar masses. The compressibility effects on the departure from the linear growth, the onset of strong non-linear interactions, and the late-time behavior of the fluid structures are investigated. It is discovered that, for the thermal equilibrium case, the background stratification acts to suppress the instability growth when the molar mass difference is small. A reversal in this monotonic behavior is observed for large molar mass differences, where stratification enhances the bubble growth. Stratification also affects the vortex creation and the associated induced velocities. The enhancement and suppression of the RTI growth has important consequences for a detailed understanding of supernovae flame front

  14. New fuzzy wavelet network for modeling and control: The modeling approach

    NASA Astrophysics Data System (ADS)

    Ebadat, Afrooz; Noroozi, Navid; Safavi, Ali Akbar; Mousavi, Seyyed Hossein

    2011-08-01

    In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while still preserve the property of linearity in parameters. In order to reduce the number of fuzzy rules, fuzzy clustering is invoked. In the clustering algorithm, those wavelets that are closer to each other in the sense of the Euclidean norm are placed in a group and are used in the consequent part of a fuzzy rule. Antecedent parts of the rules are Gaussian membership functions. Determination of the deviation parameter is performed with the help of gold partition method. Here, mean of each function is derived by averaging center of all wavelets that are related to that particular rule. The overall developed fuzzy wavelet network is called fuzzy wave-net and simulation results show superior performance over previous networks. The present work is complemented by a second part which focuses on the control aspects and to be published in this journal( [17]). This paper proposes an observer based self-structuring robust adaptive fuzzy wave-net (FWN) controller for a class of nonlinear uncertain multi-input multi-output systems.

  15. High-contrast active cavitation imaging technique based on multiple bubble wavelet transform.

    PubMed

    Lu, Shukuan; Xu, Shanshan; Liu, Runna; Hu, Hong; Wan, Mingxi

    2016-08-01

    In this study, a unique method that combines the ultrafast active cavitation imaging technique with multiple bubble wavelet transform (MBWT) for improving cavitation detection contrast was presented. The bubble wavelet was constructed by the modified Keller-Miksis equation that considered the mutual effect among bubbles. A three-dimensional spatial model was applied to simulate the spatial distribution of multiple bubbles. The effects of four parameters on the signal-to-noise ratio (SNR) of cavitation images were evaluated, including the following: initial radii of bubbles, scale factor in the wavelet transform, number of bubbles, and the minimum inter-bubble distance. And the other two spatial models and cavitation bubble size distributions were introduced in the MBWT method. The results suggested that in the free-field experiments, the averaged SNR of images acquired by the MBWT method was improved by 7.16 ± 0.09 dB and 3.14 ± 0.14 dB compared with the values of images acquired by the B-mode and single bubble wavelet transform (SBWT) methods. In addition, in the tissue experiments, the averaged cavitation-to-tissue ratio of cavitation images acquired by the MBWT method was improved by 4.69 ± 0.25 dB and 1.74± 0.29 dB compared with that of images acquired by B-mode and SBWT methods.

  16. Automatic detection of clustered microcalcifications in digital mammograms based on wavelet features and neural network classification

    NASA Astrophysics Data System (ADS)

    Yu, Songyang; Guan, Ling; Brown, Stephen

    1998-06-01

    The appearance of clustered microcalcifications in mammogram films is one of the important early signs of breast cancer. This paper presents a new image processing system for the automatic detection of clustered microcalcifications in digitized mammogram films. The detection method uses wavelet features and feed forward neural network to find possible microcalcifications pixels and a set of features to locate individual microcalcifications.

  17. Connection Between Group Based Quantum Tomography and Wavelet Transform in Banach Spaces

    NASA Astrophysics Data System (ADS)

    Jafarizadeh, M. A.; Mirzaee, M.; Rezaee, M.

    2005-04-01

    The intimate connection between the Banach space wavelet reconstruction method for each unitary representation of a given group and some of well-known quantum tomographies, such as, tomography of rotation group, spinor tomography and tomography of unitary group, is established. Also both the atomic decomposition and Banach frame nature of these quantum tomographic examples is revealed in details.

  18. Connection between Group Based Quantum Tomography and Wavelet Transform in Banach Spaces

    NASA Astrophysics Data System (ADS)

    Mirzaee, M.; Rezaei, M.; Jafarizadeh, M. A.

    2007-10-01

    The intimate connection between the Banach space wavelet reconstruction method for each unitary representation of a given group and some of well known quantum tomographies, such as: tomography of rotation group, Spinor tomography and tomography of Unitary group, is established. Also both the atomic decomposition and Banach frame nature of these quantum tomographic examples is revealed in details.

  19. Adaptive dynamic inversion robust control for BTT missile based on wavelet neural network

    NASA Astrophysics Data System (ADS)

    Li, Chuanfeng; Wang, Yongji; Deng, Zhixiang; Wu, Hao

    2009-10-01

    A new nonlinear control strategy incorporated the dynamic inversion method with wavelet neural networks is presented for the nonlinear coupling system of Bank-to-Turn(BTT) missile in reentry phase. The basic control law is designed by using the dynamic inversion feedback linearization method, and the online learning wavelet neural network is used to compensate the inversion error due to aerodynamic parameter errors, modeling imprecise and external disturbance in view of the time-frequency localization properties of wavelet transform. Weights adjusting laws are derived according to Lyapunov stability theory, which can guarantee the boundedness of all signals in the whole system. Furthermore, robust stability of the closed-loop system under this tracking law is proved. Finally, the six degree-of-freedom(6DOF) simulation results have shown that the attitude angles can track the anticipant command precisely under the circumstances of existing external disturbance and in the presence of parameter uncertainty. It means that the dependence on model by dynamic inversion method is reduced and the robustness of control system is enhanced by using wavelet neural network(WNN) to reconstruct inversion error on-line.

  20. Reconstruction of color images via Haar wavelet based on digital micromirror device

    NASA Astrophysics Data System (ADS)

    Liu, Xingjiong; He, Weiji; Gu, Guohua

    2015-10-01

    A digital micro mirror device( DMD) is introduced to form Haar wavelet basis , projecting on the color target image by making use of structured illumination, including red, green and blue light. The light intensity signals reflected from the target image are received synchronously by the bucket detector which has no spatial resolution, converted into voltage signals and then transferred into PC[1] .To reach the aim of synchronization, several synchronization processes are added during data acquisition. In the data collection process, according to the wavelet tree structure, the locations of significant coefficients at the finer scale are predicted by comparing the coefficients sampled at the coarsest scale with the threshold. The monochrome grayscale images are obtained under red , green and blue structured illumination by using Haar wavelet inverse transform algorithm, respectively. The color fusion algorithm is carried on the three monochrome grayscale images to obtain the final color image. According to the imaging principle, the experimental demonstration device is assembled. The letter "K" and the X-rite Color Checker Passport are projected and reconstructed as target images, and the final reconstructed color images have good qualities. This article makes use of the method of Haar wavelet reconstruction, reducing the sampling rate considerably. It provides color information without compromising the resolution of the final image.

  1. All-optical multiplexing schemes for multiple access networks based on wavelet packet filter banks

    NASA Astrophysics Data System (ADS)

    Cincotti, Gabriella; Svaluto Moreolo, Michela; Neri, Alessandro

    2004-08-01

    All optical architectures for Wavelet Packet Division Multiplexing (WPDM) are presented, that can be used in multiple access networks to increase the number of simultaneous users. Wavelet waveform coding spreads data signals both in time and frequency domains, with a large capacity improvement with respect to standard Optical-Code Division Multiple Access (O-CDMA) systems. In addition, the orthogonal property of the wavelet atoms ensures low InterSymbol Interference (ISI) and Multiple Access Interference (MAI) noises. To exploit the large bandwidth capacity of optical fibres, the Optical-Electrical-Optical (O-E-O) conversion is completely avoided, and we designed an all optical system that realizes the WPDM fully in the optical domain. A single Planar Lightwave Circuit (PLC) device multiplies/demultiplies N different users and a diffractive or an integrated optical device performs the waveform coding/decoding. The Wavelet Packet (WP) encoder/decoder is realized as a tree of lattice-form delay-line filters, and can be integrated on a single device along with the optical waveform modulator, resulting in a compact planar optical system. In addition, we show that different choices of WP encoders/decoders are possible to further enhance the system performances.

  2. Wavelet Based Multipurpose Color Image Watermarking by Using Dual Watermarks with Human Vision System Models

    NASA Astrophysics Data System (ADS)

    Tsai, Min-Jen; Lin, Chih-Wen

    In this study, we propose a complete architecture based on digital watermarking techniques to solve the issue of copyright protection and authentication for digital contents. We apply visible and semi-fragile watermarks as dual watermarks where visible watermarking is used to establish the copyright protection and semi-fragile watermarking authenticates and verifies the integrity of the watermarked image. In order to get the best tradeoff between the embedding energy of watermark and the perceptual translucence for visible watermark, the composite coefficients using global and local characteristics of the host and watermark images in the discrete wavelet transform (DWT) domain is considered with Human Vision System (HVS) models. To achieve the optimum noise reduction of the visibility thresholds for HVS in DWT domain, the contrast-sensitive function (CSF) and noise visible function (NVF) of perceptual model is applied which characterizes the global and local image properties and identifies texture and edge regions to determine the optimal watermark locations and strength at the watermark embedding stage. In addition, the perceptual weights according to the basis function amplitudes of DWT coefficients is fine tuned for the best quality of perceptual translucence in the design of the proposed watermarking algorithm. Furthermore, the semi-fragile watermark can detect and localize malicious attack effectively yet tolerate mild modifications such as JPEG compression and channel additive white Gaussian noise (AWGN). From the experimental results, our proposed technique not only improves the PSNR values and visual quality than other algorithms but also preserves the visibility of the watermark visible under various signal processing and advanced image recovery attacks.

  3. Wavelet-based cross-correlation analysis of structure scaling in turbulent clouds

    NASA Astrophysics Data System (ADS)

    Arshakian, Tigran G.; Ossenkopf, Volker

    2016-01-01

    Aims: We propose a statistical tool to compare the scaling behaviour of turbulence in pairs of molecular cloud maps. Using artificial maps with well-defined spatial properties, we calibrate the method and test its limitations to apply it ultimately to a set of observed maps. Methods: We develop the wavelet-based weighted cross-correlation (WWCC) method to study the relative contribution of structures of different sizes and their degree of correlation in two maps as a function of spatial scale, and the mutual displacement of structures in the molecular cloud maps. Results: We test the WWCC for circular structures having a single prominent scale and fractal structures showing a self-similar behaviour without prominent scales. Observational noise and a finite map size limit the scales on which the cross-correlation coefficients and displacement vectors can be reliably measured. For fractal maps containing many structures on all scales, the limitation from observational noise is negligible for signal-to-noise ratios ≳5. We propose an approach for the identification of correlated structures in the maps, which allows us to localize individual correlated structures and recognize their shapes and suggest a recipe for recovering enhanced scales in self-similar structures. The application of the WWCC to the observed line maps of the giant molecular cloud G 333 allows us to add specific scale information to the results obtained earlier using the principal component analysis. The WWCC confirms the chemical and excitation similarity of 13CO and C18O on all scales, but shows a deviation of HCN at scales of up to 7 pc. This can be interpreted as a chemical transition scale. The largest structures also show a systematic offset along the filament, probably due to a large-scale density gradient. Conclusions: The WWCC can compare correlated structures in different maps of molecular clouds identifying scales that represent structural changes, such as chemical and phase transitions

  4. Wavelet-based detection of crop zinc stress assessment using hyperspectral reflectance

    NASA Astrophysics Data System (ADS)

    Liu, Meiling; Liu, Xiangnan; Wu, Ling; Duan, Liqing; Zhong, Binqing

    2011-09-01

    Accurate detection of heavy metal-induced stress on the growth of crops is essential for agricultural ecological environment and food security. This study focuses on exploring singularity parameters as indicators for a crop's Zn stress level assessment by applying wavelet analysis to the hyperspectral reflectance. The field in which the experiment was conducted is located in the Changchun City, Jilin Province, China. The hyperspectral and biochemistry data from four crops growing in Zn contaminated soils: rice, maize, soybean and cabbage were collected. We performed a wavelet transform to the hyperspectral reflectance (350-1300 nm), and explored three categories of singularity parameters as indicators of crop Zn stress, including singularity range ( SR), singularity amplitude ( SA) and a Lipschitz exponent ( α). The results indicated that (i) the wavelet coefficient of the fifth decomposition level by applying Daubechies 5 (db5) mother wavelets proved successful for identifying crop Zn stress; the SR of crop concentrated on the region was around 550-850 nm of the spectral signal under Zn stress; (ii) the SR stabilized, but SA and α had developed some variations at the growth stages of the crop; (iii) the SR, SA and α were found among four crop species differentially; and moreover the SA increased in relation to an increase in the SR of crop species; (iv) the α had a strong non-linear relationship with the Zn concentration ( R2:0.7601-0.9451); the SA had a strong linear relationship with Zn concentration ( R2:0.5141-0.8281). Singularity parameters can be used as indicators for a crop's Zn stress level as well as offer a quantitative analysis of the singularity of spectrum signal. The wavelet transform technique has been shown to be very promising in detecting crops with heavy metal stress.

  5. Separation of coexisting dynamical regimes in multistate intermittency based on wavelet spectrum energies in an erbium-doped fiber laser.

    PubMed

    Hramov, Alexander E; Koronovskii, Alexey A; Moskalenko, Olga I; Zhuravlev, Maksim O; Jaimes-Reategui, Rider; Pisarchik, Alexander N

    2016-05-01

    We propose a method for the detection and localization of different types of coexisting oscillatory regimes that alternate with each other leading to multistate intermittency. Our approach is based on consideration of wavelet spectrum energies. The proposed technique is tested in an erbium-doped fiber laser with four coexisting periodic orbits, where external noise induces intermittent switches between the coexisting states. Statistical characteristics of multistate intermittency, such as the mean duration of the phases for every oscillation type, are examined with the help of the developed method. We demonstrate strong advantages of the proposed technique over previously used amplitude methods. PMID:27300891

  6. Separation of coexisting dynamical regimes in multistate intermittency based on wavelet spectrum energies in an erbium-doped fiber laser.

    PubMed

    Hramov, Alexander E; Koronovskii, Alexey A; Moskalenko, Olga I; Zhuravlev, Maksim O; Jaimes-Reategui, Rider; Pisarchik, Alexander N

    2016-05-01

    We propose a method for the detection and localization of different types of coexisting oscillatory regimes that alternate with each other leading to multistate intermittency. Our approach is based on consideration of wavelet spectrum energies. The proposed technique is tested in an erbium-doped fiber laser with four coexisting periodic orbits, where external noise induces intermittent switches between the coexisting states. Statistical characteristics of multistate intermittency, such as the mean duration of the phases for every oscillation type, are examined with the help of the developed method. We demonstrate strong advantages of the proposed technique over previously used amplitude methods.

  7. A simple smoother based on continuous wavelet transform: Comparative evaluation based on the fidelity, smoothness and efficiency in phenological estimation

    NASA Astrophysics Data System (ADS)

    Qiu, Bingwen; Feng, Min; Tang, Zhenghong

    2016-05-01

    This study proposed a simple Smoother without any local adjustments based on Continuous Wavelet Transform (SCWT). And then it evaluated its performance together with other commonly applied techniques in phenological estimation. These noise reduction methods included Savitzky-Golay filter (SG), Double Logistic function (DL), Asymmetric Gaussian function (AG), Whittaker Smoother (WS) and Harmonic Analysis of Time-Series (HANTS). They were evaluated based on fidelity and smoothness, and their efficiencies in deriving phenological parameters through the inflexion point-based method with the 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) in 2013 in China. The following conclusions were drawn: (1) The SG method exhibited strong fidelity, but weak smoothness and spatial continuity. (2) The HANTS method had very robust smoothness but weak fidelity. (3) The AG and DL methods performed weakly for vegetation with more than one growth cycle (i.e., multiple crops). (4) The WS and SCWT smoothers outperformed others with combined considerations of fidelity and smoothness, and consistent phenological patterns (correlation coefficients greater than 0.8 except evergreen broadleaf forests (0.68)). (5) Compared with WS methods, the SCWT smoother was capable in preservation of real local minima and maxima with fewer inflexions. (6) Large discrepancy was examined from the estimated phenological dates with SG and HANTS methods, particularly in evergreen forests and multiple cropping regions (the absolute mean deviation rates were 6.2-17.5 days and correlation coefficients less than 0.34 for estimated start dates).

  8. Assessment and Improvement of GOCE based Global Geopotential Models Using Wavelet Decomposition

    NASA Astrophysics Data System (ADS)

    Erol, Serdar; Erol, Bihter; Serkan Isik, Mustafa

    2016-07-01

    The contribution of recent Earth gravity field satellite missions, specifically GOCE mission, leads significant improvement in quality of gravity field models in both accuracy and resolution manners. However the performance and quality of each released model vary not only depending on the spatial location of the Earth but also the different bands of the spectral expansion. Therefore the assessment of the global model performances with validations using in situ-data in varying territories on the Earth is essential for clarifying their exact performances in local. Beside of this, their spectral evaluation and quality assessment of the signal in each part of the spherical harmonic expansion spectrum is essential to have a clear decision for the commission error content of the model and determining its optimal degree, revealed the best results, as well. The later analyses provide also a perspective and comparison on the global behavior of the models and opportunity to report the sequential improvement of the models depending on the mission developments and hence the contribution of the new data of missions. In this study a review on spectral assessment results of the recently released GOCE based global geopotential models DIR-R5, TIM-R5 with the enhancement using EGM2008, as reference model, in Turkey, versus the terrestrial data is provided. Beside of reporting the GOCE mission contribution to the models in Turkish territory, the possible improvement in the spectral quality of these models, via decomposition that are highly contaminated by noise, is purposed. In the analyses the motivation is on achieving an optimal amount of improvement that rely on conserving the useful component of the GOCE signal as much as possible, while fusing the filtered GOCE based models with EGM2008 in the appropriate spectral bands. The investigation also contain the assessment of the coherence and the correlation between the Earth gravity field parameters (free-air gravity anomalies and

  9. SEBAL-based Daily Actual Evapotranspiration Forecasting using Wavelets Decomposition Analysis and Multivariate Relevance Vector Machines

    NASA Astrophysics Data System (ADS)

    Torres, A. F.

    2011-12-01

    Agricultural lands are sources of food and energy for population around the globe. These lands are vulnerable to the impacts of climate change including variations in rainfall regimes, weather patterns, and decreased availability of water for irrigation. In addition, it is not unusual that irrigated agriculture is forced to divert less water in order to make it available for other uses, e.g. human consumption and others. As part of implementation of better policies for water control and management, irrigation companies and water user associations have been implemented water conveyance and distribution monitoring systems along with soil moisture sensors networks in the last decades. These systems allow them to manage and distribute water among the users based on their requirements and water availability while collecting information about actual soil moisture conditions in representative crop fields. In spite of this, requested water deliveries by farmers/water users is based typically on total water share, traditions and past experience on irrigation, which in most cases do not correspond to the actual crop evapotranspiration, already affected by climate change. Therefore it is necessary to provide actual information about the crop water requirements to water users/managers, so they can better quantify the required vs. available water for the irrigation events along the irrigation season. To estimate the actual evapotranspiration in a spatial extent the Sensitivity Analysis of the Surface Energy Balance Algorithm for Land (SEBAL) algorithm has demonstrated its effectiveness using satellite or airborne data. Nonetheless the estimation is restricted to the day when the geospatial information was obtained. Without information of precise future daily water crop demand there is a continuous challenge for the implementation of better water distribution and management policies in the irrigation system. The purpose of this study is to investigate the plausibility of using

  10. Definitions of non-stationary vibration power for time-frequency analysis and computational algorithms based upon harmonic wavelet transform

    NASA Astrophysics Data System (ADS)

    Heo, YongHwa; Kim, Kwang-joon

    2015-02-01

    While the vibration power for a set of harmonic force and velocity signals is well defined and known, it is not as popular yet for a set of stationary random force and velocity processes, although it can be found in some literatures. In this paper, the definition of the vibration power for a set of non-stationary random force and velocity signals will be derived for the purpose of a time-frequency analysis based on the definitions of the vibration power for the harmonic and stationary random signals. The non-stationary vibration power, defined as the short-time average of the product of the force and velocity over a given frequency range of interest, can be calculated by three methods: the Wigner-Ville distribution, the short-time Fourier transform, and the harmonic wavelet transform. The latter method is selected in this paper because band-pass filtering can be done without phase distortions, and the frequency ranges can be chosen very flexibly for the time-frequency analysis. Three algorithms for the time-frequency analysis of the non-stationary vibration power using the harmonic wavelet transform are discussed. The first is an algorithm for computation according to the full definition, while the others are approximate. Noting that the force and velocity decomposed into frequency ranges of interest by the harmonic wavelet transform are constructed with coefficients and basis functions, for the second algorithm, it is suggested to prepare a table of time integrals of the product of the basis functions in advance, which are independent of the signals under analysis. How to prepare and utilize the integral table are presented. The third algorithm is based on an evolutionary spectrum. Applications of the algorithms to the time-frequency analysis of the vibration power transmitted from an excitation source to a receiver structure in a simple mechanical system consisting of a cantilever beam and a reaction wheel are presented for illustration.

  11. ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA.

    PubMed

    Abbaspour, Sara; Lindén, Maria; Gholamhosseini, Hamid

    2015-01-01

    This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum. PMID:25980853

  12. A wavelet-based scheme for optimum measurement/monitoring of dynamic responses of structures

    NASA Astrophysics Data System (ADS)

    Mahdavi, S. H.; Razak, H. A.

    2015-07-01

    This paper presents an optimum measurement for the dynamic responses of structures from the operation of integration to respective derivation of acceleration's data using free-scaled wavelet functions. For this purpose, the numerical approach of integration and derivation has been developed for displacement measurement or determination of the third-order derivative (known as quantity of the jerk) from acquired accelerations. A simple improved algorithm is developed in order to optimally measure the dynamic quantities particularly, using Chebyshev and Haar wavelet functions. It is deduced that, stable measurement of dynamic quantities is independently achieved from the structural materials through a satisfactory optimum algorithm; that is capable of online monitoring, while emphasizing on maximum accuracy of the measurement with less computational time.

  13. High-contrast active cavitation imaging technique based on multiple bubble wavelet transform.

    PubMed

    Lu, Shukuan; Xu, Shanshan; Liu, Runna; Hu, Hong; Wan, Mingxi

    2016-08-01

    In this study, a unique method that combines the ultrafast active cavitation imaging technique with multiple bubble wavelet transform (MBWT) for improving cavitation detection contrast was presented. The bubble wavelet was constructed by the modified Keller-Miksis equation that considered the mutual effect among bubbles. A three-dimensional spatial model was applied to simulate the spatial distribution of multiple bubbles. The effects of four parameters on the signal-to-noise ratio (SNR) of cavitation images were evaluated, including the following: initial radii of bubbles, scale factor in the wavelet transform, number of bubbles, and the minimum inter-bubble distance. And the other two spatial models and cavitation bubble size distributions were introduced in the MBWT method. The results suggested that in the free-field experiments, the averaged SNR of images acquired by the MBWT method was improved by 7.16 ± 0.09 dB and 3.14 ± 0.14 dB compared with the values of images acquired by the B-mode and single bubble wavelet transform (SBWT) methods. In addition, in the tissue experiments, the averaged cavitation-to-tissue ratio of cavitation images acquired by the MBWT method was improved by 4.69 ± 0.25 dB and 1.74± 0.29 dB compared with that of images acquired by B-mode and SBWT methods. PMID:27586732

  14. [Super-resolution image reconstruction algorithm based on projection onto convex sets and wavelet fusion].

    PubMed

    Cao, Yuzhen; Liu, Xiaoting; Wang, Wei; Xing, Zhanfeng

    2009-10-01

    In this paper a new super-resolution image reconstruction algorithm was proposed. With the improvement of the classical projection onto convex sets (POCS) algorithm, as ground work, and with the combined use of POCS and wavelet fusion, a high resolution CT image was restored by using a group of low resolution CT images. The experimental results showed: the proposed algorithm improves the ability of fusing different information, the detail of the image is more prominent, and the image quality is better.

  15. Classification of the Gabon SAR Mosaic Using a Wavelet Based Rule Classifier

    NASA Technical Reports Server (NTRS)

    Simard, Marc; Saatchi, Sasan; DeGrandi, Gianfranco

    2000-01-01

    A method is developed for semi-automated classification of SAR images of the tropical forest. Information is extracted using the wavelet transform (WT). The transform allows for extraction of structural information in the image as a function of scale. In order to classify the SAR image, a Desicion Tree Classifier is used. The method of pruning is used to optimize classification rate versus tree size. The results give explicit insight on the type of information useful for a given class.

  16. Refinement trajectory and determination of eigenstates by a wavelet based adaptive method

    SciTech Connect

    Pipek, Janos; Nagy, Szilvia

    2006-11-07

    The detail structure of the wave function is analyzed at various refinement levels using the methods of wavelet analysis. The eigenvalue problem of a model system is solved in granular Hilbert spaces, and the trajectory of the eigenstates is traced in terms of the resolution. An adaptive method is developed for identifying the fine structure localization regions, where further refinement of the wave function is necessary.

  17. A Wavelet based Suboptimal Kalman Filter for Assimilation of Stratospheric Chemical Tracer Observations

    NASA Technical Reports Server (NTRS)

    Tangborn, Andrew; Auger, Ludovic

    2003-01-01

    A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. This scheme projects the discretized covariance propagation equations and covariance matrix onto an orthogonal set of compactly supported wavelets. Wavelet representation is localized in both location and scale, which allows for efficient representation of the inherently anisotropic structure of the error covariances. The truncation is carried out in such a way that the resolution of the error covariance is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance size by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the constituent field. This results indicate that propagation of error covariances for a global two-dimensional data assimilation system are currently feasible. Recommendations for further reduction in computational cost are made with the goal of extending this technique to three-dimensional global assimilation systems.

  18. Experimental validation of wavelet based solution for dynamic response of railway track subjected to a moving train

    NASA Astrophysics Data System (ADS)

    Koziol, Piotr

    2016-10-01

    New approaches allowing effective analysis of railway structures dynamic behaviour are needed for appropriate modelling and understanding of phenomena associated with train transportation. The literature highlights the fact that nonlinear assumptions are of importance in dynamic analysis of railway tracks. This paper presents wavelet based semi-analytical solution for the infinite Euler-Bernoulli beam resting on a nonlinear foundation and subjected to a set of moving forces, being representation of railway track with moving train, along with its preliminary experimental validation. It is shown that this model, although very simplified, with an assumption of viscous damping of foundation, can be considered as a good enough approximation of realistic structures behaviour. The steady-state response of the beam is obtained by applying the Galilean co-ordinate system and the Adomian's decomposition method combined with coiflet based approximation, leading to analytical estimation of transverse displacements. The applied approach, using parameters taken from real measurements carried out on the Polish Railways network for fast train Pendolino EMU-250, shows ability of the proposed method to analyse parametrically dynamic systems associated with transportation. The obtained results are in accordance with measurement data in wide range of physical parameters, which can be treated as a validation of the developed wavelet based approach. The conducted investigation is supplemented by several numerical examples.

  19. Standardized Software for Wind Load Forecast Error Analyses and Predictions Based on Wavelet-ARIMA Models - Applications at Multiple Geographically Distributed Wind Farms

    SciTech Connect

    Hou, Zhangshuan; Makarov, Yuri V.; Samaan, Nader A.; Etingov, Pavel V.

    2013-03-19

    Given the multi-scale variability and uncertainty of wind generation and forecast errors, it is a natural choice to use time-frequency representation (TFR) as a view of the corresponding time series represented over both time and frequency. Here we use wavelet transform (WT) to expand the signal in terms of wavelet functions which are localized in both time and frequency. Each WT component is more stationary and has consistent auto-correlation pattern. We combined wavelet analyses with time series forecast approaches such as ARIMA, and tested the approach at three different wind farms located far away from each other. The prediction capability is satisfactory -- the day-ahead prediction of errors match the original error values very well, including the patterns. The observations are well located within the predictive intervals. Integrating our wavelet-ARIMA (‘stochastic’) model with the weather forecast model (‘deterministic’) will improve our ability significantly to predict wind power generation and reduce predictive uncertainty.

  20. Wavelet-Based Analytical Algorithm for Solving Steady-State Concentration in Immobilized Glucose Isomerase of Packed-Bed Reactor Model.

    PubMed

    Selvi, M Salai Mathi; Hariharan, G

    2016-08-01

    Wavelet method is a recently developed tool in applied mathematics. The mathematical model of the steady-state immobilized enzyme electrodes is discussed. This theoretical model is based on one-dimensional heat conduction equations containing a non-linear term related to Michaelis-Menten kinetics. An efficient Chebyshev wavelet-based technique is applied to solve the non-linear diffusion equation for the steady-state condition. A simple expression of the substrate concentration is obtained as a function of the Thiele modulus [Formula: see text] and [Formula: see text](kinetic parameter). The wavelet results are compared with the numerical and HPM solutions and found to be in good agreement.

  1. Wavelet-Based Analytical Algorithm for Solving Steady-State Concentration in Immobilized Glucose Isomerase of Packed-Bed Reactor Model.

    PubMed

    Selvi, M Salai Mathi; Hariharan, G

    2016-08-01

    Wavelet method is a recently developed tool in applied mathematics. The mathematical model of the steady-state immobilized enzyme electrodes is discussed. This theoretical model is based on one-dimensional heat conduction equations containing a non-linear term related to Michaelis-Menten kinetics. An efficient Chebyshev wavelet-based technique is applied to solve the non-linear diffusion equation for the steady-state condition. A simple expression of the substrate concentration is obtained as a function of the Thiele modulus [Formula: see text] and [Formula: see text](kinetic parameter). The wavelet results are compared with the numerical and HPM solutions and found to be in good agreement. PMID:27161606

  2. Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image.

    PubMed

    Singh, Anushikha; Dutta, Malay Kishore; ParthaSarathi, M; Uher, Vaclav; Burget, Radim

    2016-02-01

    Glaucoma is a disease of the retina which is one of the most common causes of permanent blindness worldwide. This paper presents an automatic image processing based method for glaucoma diagnosis from the digital fundus image. In this paper wavelet feature extraction has been followed by optimized genetic feature selection combined with several learning algorithms and various parameter settings. Unlike the existing research works where the features are considered from the complete fundus or a sub image of the fundus, this work is based on feature extraction from the segmented and blood vessel removed optic disc to improve the accuracy of identification. The experimental results presented in this paper indicate that the wavelet features of the segmented optic disc image are clinically more significant in comparison to features of the whole or sub fundus image in the detection of glaucoma from fundus image. Accuracy of glaucoma identification achieved in this work is 94.7% and a comparison with existing methods of glaucoma detection from fundus image indicates that the proposed approach has improved accuracy of classification.

  3. Detection of Dendritic Spines Using Wavelet-Based Conditional Symmetric Analysis and Regularized Morphological Shared-Weight Neural Networks

    PubMed Central

    Wang, Shuihua; Chen, Mengmeng; Li, Yang; Zhang, Yudong; Han, Liangxiu; Wu, Jane; Du, Sidan

    2015-01-01

    Identification and detection of dendritic spines in neuron images are of high interest in diagnosis and treatment of neurological and psychiatric disorders (e.g., Alzheimer's disease, Parkinson's diseases, and autism). In this paper, we have proposed a novel automatic approach using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks (RMSNN) for dendritic spine identification involving the following steps: backbone extraction, localization of dendritic spines, and classification. First, a new algorithm based on wavelet transform and conditional symmetric analysis has been developed to extract backbone and locate the dendrite boundary. Then, the RMSNN has been proposed to classify the spines into three predefined categories (mushroom, thin, and stubby). We have compared our proposed approach against the existing methods. The experimental result demonstrates that the proposed approach can accurately locate the dendrite and accurately classify the spines into three categories with the accuracy of 99.1% for “mushroom” spines, 97.6% for “stubby” spines, and 98.6% for “thin” spines. PMID:26692046

  4. Multiscale peak detection in wavelet space.

    PubMed

    Zhang, Zhi-Min; Tong, Xia; Peng, Ying; Ma, Pan; Zhang, Ming-Jin; Lu, Hong-Mei; Chen, Xiao-Qing; Liang, Yi-Zeng

    2015-12-01

    Accurate peak detection is essential for analyzing high-throughput datasets generated by analytical instruments. Derivatives with noise reduction and matched filtration are frequently used, but they are sensitive to baseline variations, random noise and deviations in the peak shape. A continuous wavelet transform (CWT)-based method is more practical and popular in this situation, which can increase the accuracy and reliability by identifying peaks across scales in wavelet space and implicitly removing noise as well as the baseline. However, its computational load is relatively high and the estimated features of peaks may not be accurate in the case of peaks that are overlapping, dense or weak. In this study, we present multi-scale peak detection (MSPD) by taking full advantage of additional information in wavelet space including ridges, valleys, and zero-crossings. It can achieve a high accuracy by thresholding each detected peak with the maximum of its ridge. It has been comprehensively evaluated with MALDI-TOF spectra in proteomics, the CAMDA 2006 SELDI dataset as well as the Romanian database of Raman spectra, which is particularly suitable for detecting peaks in high-throughput analytical signals. Receiver operating characteristic (ROC) curves show that MSPD can detect more true peaks while keeping the false discovery rate lower than MassSpecWavelet and MALDIquant methods. Superior results in Raman spectra suggest that MSPD seems to be a more universal method for peak detection. MSPD has been designed and implemented efficiently in Python and Cython. It is available as an open source package at .

  5. Iterated oversampled filter banks and wavelet frames

    NASA Astrophysics Data System (ADS)

    Selesnick, Ivan W.; Sendur, Levent

    2000-12-01

    This paper takes up the design of wavelet tight frames that are analogous to Daubechies orthonormal wavelets - that is, the design of minimal length wavelet filters satisfying certain polynomial properties, but now in the oversampled case. The oversampled dyadic DWT considered in this paper is based on a single scaling function and tow distinct wavelets. Having more wavelets than necessary gives a closer spacing between adjacent wavelets within the same scale. As a result, the transform is nearly shift-invariant, and can be used to improve denoising. Because the associated time- frequency lattice preserves the dyadic structure of the critically sampled DWT it can be used with tree-based denoising algorithms that exploit parent-child correlation.

  6. Wavelet-based denoising of the Fourier metric in real-time wavefront correction for single molecule localization microscopy

    NASA Astrophysics Data System (ADS)

    Tehrani, Kayvan Forouhesh; Mortensen, Luke J.; Kner, Peter

    2016-03-01

    Wavefront sensorless schemes for correction of aberrations induced by biological specimens require a time invariant property of an image as a measure of fitness. Image intensity cannot be used as a metric for Single Molecule Localization (SML) microscopy because the intensity of blinking fluorophores follows exponential statistics. Therefore a robust intensity-independent metric is required. We previously reported a Fourier Metric (FM) that is relatively intensity independent. The Fourier metric has been successfully tested on two machine learning algorithms, a Genetic Algorithm and Particle Swarm Optimization, for wavefront correction about 50 μm deep inside the Central Nervous System (CNS) of Drosophila. However, since the spatial frequencies that need to be optimized fall into regions of the Optical Transfer Function (OTF) that are more susceptible to noise, adding a level of denoising can improve performance. Here we present wavelet-based approaches to lower the noise level and produce a more consistent metric. We compare performance of different wavelets such as Daubechies, Bi-Orthogonal, and reverse Bi-orthogonal of different degrees and orders for pre-processing of images.

  7. Joint discrepancy evaluation of an existing steel bridge using time-frequency and wavelet-based approach

    NASA Astrophysics Data System (ADS)

    Walia, Suresh Kumar; Patel, Raj Kumar; Vinayak, Hemant Kumar; Parti, Raman

    2013-12-01

    The objective of this study is to bring out the errors introduced during construction which are overlooked during the physical verification of the bridge. Such errors can be pointed out if the symmetry of the structure is challenged. This paper thus presents the study of downstream and upstream truss of newly constructed steel bridge using time-frequency and wavelet-based approach. The variation in the behavior of truss joints of bridge with variation in the vehicle speed has been worked out to determine their flexibility. The testing on the steel bridge was carried out with the same instrument setup on both the upstream and downstream trusses of the bridge at two different speeds with the same moving vehicle. The nodal flexibility investigation is carried out using power spectral density, short-time Fourier transform, and wavelet packet transform with respect to both the trusses and speed. The results obtained have shown that the joints of both upstream and downstream trusses of the bridge behave in a different manner even if designed for the same loading due to constructional variations and vehicle movement, in spite of the fact that the analytical models present a simplistic model for analysis and design. The difficulty of modal parameter extraction of the particular bridge under study increased with the increase in speed due to decreased excitation time.

  8. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    PubMed

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method.

  9. Wavelet-based multifractal analysis of nonlinear time series: the earthquake-driven tsunami of 27 February 2010 in Chile.

    PubMed

    Toledo, B A; Chian, A C-L; Rempel, E L; Miranda, R A; Muñoz, P R; Valdivia, J A

    2013-02-01

    We study general multifractal properties of tidal gauge and long-wave time series which show a well defined transition between two states, as is the case of sea level when a tsunami arrives. We adopt a method based on discrete wavelets, called wavelet leaders, which has been successfully used in a wide range of applications from image analysis to biomedical signals. First, we analyze an empirical time series of tidal gauge from the tsunami event of 27 February 2010 in Chile. Then, we study a numerical solution of the driven-damped regularized long-wave equation (RLWE) which displays on-off intermittency. Both time series are characterized by a sudden change between two sharply distinct dynamical states. Our analysis suggests a correspondence between the pre- and post-tsunami states (ocean background) and the on state in the RLWE, and also between the tsunami state (disturbed ocean) and the off state in the RLWE. A qualitative similarity in their singularity spectra is observed, and since the RLWE is used to model shallow water dynamics, this result could imply an underlying dynamical similarity.

  10. A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery.

    PubMed

    Liu, Zhiwen; He, Zhengjia; Guo, Wei; Tang, Zhangchun

    2016-03-01

    In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal-noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. PMID:26753616

  11. Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts

    NASA Astrophysics Data System (ADS)

    Maity, Rajib; Suman, Mayank; Verma, Nitesh Kumar

    2016-08-01

    Droughts are expected to propagate from one type to another - meteorological to agricultural to hydrological to socio-economic. However, they do not possess a universal, straightforward temporal dependence. Rather, assessment of one type of drought (successor) from another (predecessor) is a complex problem depending on the basin's physiographic and climatic characteristics, such as, spatial extent, topography, land use, land cover and climate regime. In this paper, a wavelet decomposition based approach is proposed to model the temporal dependence between different types of droughts. The idea behind is to separate the rapidly and slowly moving components of drought indices. It is shown that the temporal dependence of predecessor (say meteorological drought) on the successor (say hydrological drought) can be better captured at its constituting components level. Such components are obtained through wavelet decomposition retaining its temporal correspondence. Thus, in the proposed approach, predictand drought index is predicted using the decomposed components of predecessor drought. Several alternative models are investigated to arrive at the best possible model structure for predicting different types of drought. The proposed approach is found to be very useful for foreseeing the agricultural or hydrological droughts knowing the meteorological drought status, offering the scope for better management of drought consequences. The mathematical framework of the proposed approach is general in nature and can be applied to different basins. However, the limitation is the requirement of region/catchment specific calibration of some parameters before using the proposed model, which is not very difficult and uncommon though.

  12. Wavelet based rotation invariant texture feature for lung tissue classification and retrieval

    NASA Astrophysics Data System (ADS)

    Dash, Jatindra Kumar; Mukhopadhyay, Sudipta; Das Gupta, Rahul; Garg, Mandeep Kumar; Prabhakar, Nidhi; Khandelwal, Niranjan

    2014-03-01

    This paper evaluates the performance of recently proposed rotation invariant texture feature extraction method for the classi¯cation and retrieval of lung tissues a®ected with Interstitial Lung Diseases (ILDs). The method makes use of principle texture direction as the reference direction and extracts texture features using Discrete Wavelet Transform (DWT). A private database containing high resolution computed tomography (HRCT) images belonging to ¯ve category of lung tissue is used for the experiment. The experimental result shows that the texture appearances of lung tissues are anisotropic in nature and hence rotation invariant features achieve better retrieval as well as classi¯cation accuracy.

  13. EEG signal processing in anesthesia-using wavelet-based informational tools.

    PubMed

    Ye, Zhiqian; Tian, Fuying; Weng, Jianfeng

    2005-01-01

    A new tool of wavelet entropy (WE) is applied to characterize the dynamical properties of EEGs for purpose of evaluating the depth of anesthesia (DOA). 31 cases are analysed with WE, and the result shows the WE measure for EEGs can distinguish awake and asleep state in anesthesia with a high accuracy of 95%. Compared with C(n) complexity, the WE demonstrates the same good performance, and needs shorter data length for analysis. These remind that WE is a potential quantifier of DOA.

  14. Non-linear direct multi-scale image enhancement based on the luminance and contrast masking characteristics of the human visual system.

    PubMed

    Nercessian, Shahan C; Panetta, Karen A; Agaian, Sos S

    2013-09-01

    Image enhancement is a crucial pre-processing step for various image processing applications and vision systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the human visual system. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new human visual system-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to human visual system inspired multi-scale contrast coefficients; 3) the extension of human visual system-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

  15. Low-Oscillation Complex Wavelets

    NASA Astrophysics Data System (ADS)

    ADDISON, P. S.; WATSON, J. N.; FENG, T.

    2002-07-01

    In this paper we explore the use of two low-oscillation complex wavelets—Mexican hat and Morlet—as powerful feature detection tools for data analysis. These wavelets, which have been largely ignored to date in the scientific literature, allow for a decomposition which is more “temporal than spectral” in wavelet space. This is shown to be useful for the detection of small amplitude, short duration signal features which are masked by much larger fluctuations. Wavelet transform-based methods employing these wavelets (based on both wavelet ridges and modulus maxima) are developed and applied to sonic echo NDT signals used for the analysis of structural elements. A new mobility scalogram and associated reflectogram is defined for analysis of impulse response characteristics of structural elements and a novel signal compression technique is described in which the pertinent signal information is contained within a few modulus maxima coefficients. As an example of its usefulness, the signal compression method is employed as a pre-processor for a neural network classifier. The authors believe that low oscillation complex wavelets have wide applicability to other practical signal analysis problems. Their possible application to two such problems is discussed briefly—the interrogation of arrhythmic ECG signals and the detection and characterization of coherent structures in turbulent flow fields.

  16. Multi-Temporal and Multi-Sensor Image Matching Based on Local Frequency Information

    NASA Astrophysics Data System (ADS)

    Liu, X.; Yu, Q.; Zhang, X.; Shang, Y.; Zhu, X.; Lei, Z.

    2012-08-01

    Image Matching is often one of the first tasks in many Photogrammetry and Remote Sensing applications. This paper presents an efficient approach to automated multi-temporal and multi-sensor image matching based on local frequency information. Two new independent image representations, Local Average Phase (LAP) and Local Weighted Amplitude (LWA), are presented to emphasize the common scene information, while suppressing the non-common illumination and sensor-dependent information. In order to get the two representations, local frequency information is firstly obtained from Log-Gabor wavelet transformation, which is similar to that of the human visual system; then the outputs of odd and even symmetric filters are used to construct the LAP and LWA. The LAP and LWA emphasize on the phase and amplitude information respectively. As these two representations are both derivative-free and threshold-free, they are robust to noise and can keep as much of the image details as possible. A new Compositional Similarity Measure (CSM) is also presented to combine the LAP and LWA with the same weight for measuring the similarity of multi-temporal and multi-sensor images. The CSM can make the LAP and LWA compensate for each other and can make full use of the amplitude and phase of local frequency information. In many image matching applications, the template is usually selected without consideration of its matching robustness and accuracy. In order to overcome this problem, a local best matching point detection is presented to detect the best matching template. In the detection method, we employ self-similarity analysis to identify the template with the highest matching robustness and accuracy. Experimental results using some real images and simulation images demonstrate that the presented approach is effective for matching image pairs with significant scene and illumination changes and that it has advantages over other state-of-the-art approaches, which include: the Local Frequency

  17. A novel image enhancement algorithm based on stationary wavelet transform for infrared thermography to the de-bonding defect in solid rocket motors

    NASA Astrophysics Data System (ADS)

    Liu, Tao; Zhang, Wei; Yan, Shaoze

    2015-10-01

    In this paper, a multi-scale image enhancement algorithm based on low-passing filtering and nonlinear transformation is proposed for infrared testing image of the de-bonding defect in solid propellant rocket motors. Infrared testing images with high-level noise and low contrast are foundations for identifying defects and calculating the defects size. In order to improve quality of the infrared image, according to distribution properties of the detection image, within framework of stationary wavelet transform, the approximation coefficients at suitable decomposition level is processed by index low-passing filtering by using Fourier transform, after that, the nonlinear transformation is applied to further process the figure to improve the picture contrast. To verify validity of the algorithm, the image enhancement algorithm is applied to infrared testing pictures of two specimens with de-bonding defect. Therein, one specimen is made of a type of high-strength steel, and the other is a type of carbon fiber composite. As the result shown, in the images processed by the image enhancement algorithm presented in the paper, most of noises are eliminated, and contrast between defect areas and normal area is improved greatly; in addition, by using the binary picture of the processed figure, the continuous defect edges can be extracted, all of which show the validity of the algorithm. The paper provides a well-performing image enhancement algorithm for the infrared thermography.

  18. On the use of lossless integer wavelet transforms in medical image segmentation

    NASA Astrophysics Data System (ADS)

    Nagaraj, Nithin; Mallya, Yogish

    2005-04-01

    Recent trends in medical image processing involve computationally intensive processing techniques on large data sets, especially for 3D applications such as segmentation, registration, volume rendering etc. Multi-resolution image processing techniques have been used in order to speed-up these methods. However, all well-known techniques currently used in multi-resolution medical image processing rely on using Gaussain-based or other equivalent floating point representations that are lossy and irreversible. In this paper, we study the use of Integer Wavelet Transforms (IWT) to address the issue of lossless representation and reversible reconstruction for such medical image processing applications while still retaining all the benefits which floating-point transforms offer such as high speed and efficient memory usage. In particular, we consider three low-complexity reversible wavelet transforms namely the - Lazy-wavelet, the Haar wavelet or (1,1) and the S+P transform as against the Gaussian filter for multi-resolution speed-up of an automatic bone removal algorithm for abdomen CT Angiography. Perfect-reconstruction integer wavelet filters have the ability to perfectly recover the original data set at any step in the application. An additional advantage with the reversible wavelet representation is that it is suitable for lossless compression for purposes of storage, archiving and fast retrieval. Given the fact that even a slight loss of information in medical image processing can be detrimental to diagnostic accuracy, IWTs seem to be the ideal choice for multi-resolution based medical image segmentation algorithms. These could also be useful for other medical image processing methods.

  19. A wavelet-based Markov random field segmentation model in segmenting microarray experiments.

    PubMed

    Athanasiadis, Emmanouil; Cavouras, Dionisis; Kostopoulos, Spyros; Glotsos, Dimitris; Kalatzis, Ioannis; Nikiforidis, George

    2011-12-01

    In the present study, an adaptation of the Markov Random Field (MRF) segmentation model, by means of the stationary wavelet transform (SWT), applied to complementary DNA (cDNA) microarray images is proposed (WMRF). A 3-level decomposition scheme of the initial microarray image was performed, followed by a soft thresholding filtering technique. With the inverse process, a Denoised image was created. In addition, by using the Amplitudes of the filtered wavelet Horizontal and Vertical images at each level, three different Magnitudes were formed. These images were combined with the Denoised one to create the proposed SMRF segmentation model. For numerical evaluation of the segmentation accuracy, the segmentation matching factor (SMF), the Coefficient of Determination (r(2)), and the concordance correlation (p(c)) were calculated on the simulated images. In addition, the SMRF performance was contrasted to the Fuzzy C Means (FCM), Gaussian Mixture Models (GMM), Fuzzy GMM (FGMM), and the conventional MRF techniques. Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r(2), and p(c) (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images.

  20. Probabilistic uncertainty quantification of wavelet-transform-based structural health monitoring features

    NASA Astrophysics Data System (ADS)

    Sarrafi, Aral; Mao, Zhu

    2016-04-01

    In the application of Structural Health Monitoring (SHM), processing the online-acquired data plays a very important role, among which wavelet transform is an outstanding tool and compared to Fourier transform, it handles the nonstationary behaviors in the time series in an adaptive fashion. When dealing with time-variant data, there are uncertainties from numerous resources inherent to the feature estimation, such as measurement noise, operational and environmental variability, hardware limitation, etc. The corruption from uncertainty will make the data interpretation ambiguous and thereby dramatically degrades the decision quality with regard to the occurrence, location, severity, and extent of damages. This paper derives a probabilistic model to quantify analytically the uncertainty of wavelet transform feature as a random variable, and variance is derived analytically in this work. Considering central limit theorem, Gaussian probability density function characterizes the distribution and this has been validated via Monte Carlo testing. By fully characterizing the uncertainty, the damage detection implementations may be facilitated with a quantified false alarm rate and miss catch rate.

  1. Applications of wavelet-based compression to multidimensional earth science data

    SciTech Connect

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

    1993-01-01

    A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithm (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm axe reported, as are signal-to-noise ratio (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme.The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

  2. Applications of wavelet-based compression to multidimensional earth science data

    SciTech Connect

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

    1993-02-01

    A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithm (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm axe reported, as are signal-to-noise ratio (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme.The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

  3. Applications of wavelet-based compression to multidimensional Earth science data

    NASA Technical Reports Server (NTRS)

    Bradley, Jonathan N.; Brislawn, Christopher M.

    1993-01-01

    A data compression algorithm involving vector quantization (VQ) and the discrete wavelet transform (DWT) is applied to two different types of multidimensional digital earth-science data. The algorithms (WVQ) is optimized for each particular application through an optimization procedure that assigns VQ parameters to the wavelet transform subbands subject to constraints on compression ratio and encoding complexity. Preliminary results of compressing global ocean model data generated on a Thinking Machines CM-200 supercomputer are presented. The WVQ scheme is used in both a predictive and nonpredictive mode. Parameters generated by the optimization algorithm are reported, as are signal-to-noise (SNR) measurements of actual quantized data. The problem of extrapolating hydrodynamic variables across the continental landmasses in order to compute the DWT on a rectangular grid is discussed. Results are also presented for compressing Landsat TM 7-band data using the WVQ scheme. The formulation of the optimization problem is presented along with SNR measurements of actual quantized data. Postprocessing applications are considered in which the seven spectral bands are clustered into 256 clusters using a k-means algorithm and analyzed using the Los Alamos multispectral data analysis program, SPECTRUM, both before and after being compressed using the WVQ program.

  4. Low-coherence interferometry based roughness measurement on turbine blade surfaces using wavelet analysis

    NASA Astrophysics Data System (ADS)

    Zou, Yibo; Li, Yinan; Kaestner, Markus; Reithmeier, Eduard

    2016-07-01

    In this paper, a non-contact optical system, a low-coherence interferometer (LCI), is introduced for the purpose of measuring the surface roughness of turbine blades. The designed system not only possesses a high vertical resolution and is able to acquire the roughness topography, but also it has a large vertical scanning range compared to other commonly used optical systems. The latter characteristic allows us to measure turbine blades surfaces with large curvature without collisions between the lens and the measurement object. After obtaining the surface topography, wavelet analysis is applied to decompose the original surface into multiple bandwidths to conduct a multiscale analysis. The results show that the developed LCI system proofs a good performance not only in obtaining the surface topography in the roughness scale but also in being able to measure surfaces of objects that possess a complex geometry in a large vertical range. Furthermore, the applied biorthogonal wavelet in this study has performed good amplitude and phase properties in extracting the roughness microstructures from the whole surface. Finally, the traditional roughness parameters, such as the mean surface roughness Sa and the Root Mean Square (RMS) roughness Sq, are evaluated in each decomposed subband and their correlations with the scale of each subband are analyzed.

  5. A new wavelet-based reconstruction algorithm for twin image removal in digital in-line holography

    NASA Astrophysics Data System (ADS)

    Hattay, Jamel; Belaid, Samir; Aguili, Taoufik; Lebrun, Denis

    2016-07-01

    Two original methods are proposed here for digital in-line hologram processing. Firstly, we propose an entropy-based method to retrieve the focus plane which is very useful for digital hologram reconstruction. Secondly, we introduce a new approach to remove the so-called twin images reconstructed by holograms. This is achieved owing to the Blind Source Separation (BSS) technique. The proposed method is made up of two steps: an Adaptive Quincunx Lifting Scheme (AQLS) and a statistical unmixing algorithm. The AQLS tool is based on wavelet packet transform, whose role is to maximize the sparseness of the input holograms. The unmixing algorithm uses the Independent Component Analysis (ICA) tool. Experimental results confirm the ability of convolutive blind source separation to discard the unwanted twin image from in-line digital holograms.

  6. FIR filters for hardware-based real-time multi-band image blending

    NASA Astrophysics Data System (ADS)

    Popovic, Vladan; Leblebici, Yusuf

    2015-02-01

    Creating panoramic images has become a popular feature in modern smart phones, tablets, and digital cameras. A user can create a 360 degree field-of-view photograph from only several images. Quality of the resulting image is related to the number of source images, their brightness, and the used algorithm for their stitching and blending. One of the algorithms that provides excellent results in terms of background color uniformity and reduction of ghosting artifacts is the multi-band blending. The algorithm relies on decomposition of image into multiple frequency bands using dyadic filter bank. Hence, the results are also highly dependant on the used filter bank. In this paper we analyze performance of the FIR filters used for multi-band blending. We present a set of five filters that showed the best results in both literature and our experiments. The set includes Gaussian filter, biorthogonal wavelets, and custom-designed maximally flat and equiripple FIR filters. The presented results of filter comparison are based on several no-reference metrics for image quality. We conclude that 5/3 biorthogonal wavelet produces the best result in average, especially when its short length is considered. Furthermore, we propose a real-time FPGA implementation of the blending algorithm, using 2D non-separable systolic filtering scheme. Its pipeline architecture does not require hardware multipliers and it is able to achieve very high operating frequencies. The implemented system is able to process 91 fps for 1080p (1920×1080) image resolution.

  7. Wavelet based error correction and predictive uncertainty of a hydrological forecasting system

    NASA Astrophysics Data System (ADS)

    Bogner, Konrad; Pappenberger, Florian; Thielen, Jutta; de Roo, Ad

    2010-05-01

    River discharge predictions most often show errors with scaling properties of unknown source and statistical structure that degrade the quality of forecasts. This is especially true for lead-time ranges greater then a few days. Since the European Flood Alert System (EFAS) provides discharge forecasts up to ten days ahead, it is necessary to take these scaling properties into consideration. For example the range of scales for the error that occurs at the spring time will be caused by long lasting snowmelt processes, and is by far larger then the error, that appears during the summer period and is caused by convective rain fields of short duration. The wavelet decomposition is an excellent way to provide the detailed model error at different levels in order to estimate the (unobserved) state variables more precisely. A Vector-AutoRegressive model with eXogenous input (VARX) is fitted for the different levels of wavelet decomposition simultaneously and after predicting the next time steps ahead for each scale, a reconstruction formula is applied to transform the predictions in the wavelet domain back to the original time domain. The Bayesian Uncertainty Processor (BUP) developed by Krzysztofowicz is an efficient method to estimate the full predictive uncertainty, which is derived by integrating the hydrological model uncertainty and the meteorological input uncertainty. A hydrological uncertainty processor has been applied to the error corrected discharge series at first in order to derive the predictive conditional distribution under the hypothesis that there is no input uncertainty. The uncertainty of the forecasted meteorological input forcing the hydrological model is derived from the combination of deterministic weather forecasts and ensemble predictions systems (EPS) and the Input Processor maps this input uncertainty into the output uncertainty under the hypothesis that there is no hydrological uncertainty. The main objective of this Bayesian forecasting system

  8. Investment horizon heterogeneity and wavelet: Overview and further research directions

    NASA Astrophysics Data System (ADS)

    Chakrabarty, Anindya; De, Anupam; Gunasekaran, Angappa; Dubey, Rameshwar

    2015-07-01

    Wavelet based multi-scale analysis of financial time series has attracted much attention, lately, from both the academia and practitioners from all around the world. The unceasing metamorphosis of the discipline of finance from its humble beginning as applied economics to the more sophisticated depiction as applied physics and applied psychology has revolutionized the way we perceive the market and its complexities. One such complexity is the presence of heterogeneous horizon agents in the market. In this context, we have performed a generous review of different aspects of horizon heterogeneity that has been successfully elucidated through the synergy between wavelet theory and finance. The evolution of wavelet has been succinctly delineated to bestow necessary information to the readers who are new to this field. The migration of wavelet into finance and its subsequent branching into different sub-divisions have been sketched. The pertinent literature on the impact of horizon heterogeneity on risk, asset pricing and inter-dependencies of the financial time series are explored. The significant contributions are collated and classified in accordance to their purpose and approach so that potential researcher and practitioners, interested in this subject, can be benefited. Future research possibilities in the direction of "agency cost mitigation" and "synergy between econophysics and behavioral finance in stock market forecasting" are also suggested in the paper.

  9. Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

    NASA Astrophysics Data System (ADS)

    Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

    2015-11-01

    The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

  10. A blind detection scheme based on modified wavelet denoising algorithm for wireless optical communications

    NASA Astrophysics Data System (ADS)

    Li, Ruijie; Dang, Anhong

    2015-10-01

    This paper investigates a detection scheme without channel state information for wireless optical communication (WOC) systems in turbulence induced fading channel. The proposed scheme can effectively diminish the additive noise caused by background radiation and photodetector, as well as the intensity scintillation caused by turbulence. The additive noise can be mitigated significantly using the modified wavelet threshold denoising algorithm, and then, the intensity scintillation can be attenuated by exploiting the temporal correlation of the WOC channel. Moreover, to improve the performance beyond that of the maximum likelihood decision, the maximum a posteriori probability (MAP) criterion is considered. Compared with conventional blind detection algorithm, simulation results show that the proposed detection scheme can improve the signal-to-noise ratio (SNR) performance about 4.38 dB while the bit error rate and scintillation index (SI) are 1×10-6 and 0.02, respectively.

  11. Characterization of ultradian and circadian rhythms of core body temperature based on wavelet analysis.

    PubMed

    Ming Huang; Tamura, Toshiyo; Wenxi Chen; Kitamura, Kei-Ichiro; Nemoto, Tetsu; Kanaya, Shigehiko

    2014-01-01

    This study was motivated by the needs of precise characterization for the ultradian and circadian rhythmicity of human core body temperature (CBT). The CBT data, two-whole-days' data of two female bed-ridden old aged suffering from cerebral infarction sequelae, was detrended to eliminate the long-term components with periods longer than two days and normalized at first. It was then analyzed by the stationary wavelets transform (SWT) to get the time-frequency information. In the step of SWT, symlet 6 was used, and the approximation waveforms in the 5th, 6th and 7th levels were used to reveal the targeted rhythmicity. The results of the SWT show that SWT can faithfully reveal the time-frequency information of feature elements (peaks and troughs) of waveforms and rhythmicity can be characterized by analyzing temporal information of feature elements.

  12. A Wavelet Based Suboptimal Kalman Filter for Assimilation of Stratospheric Chemical Tracer Observations

    NASA Technical Reports Server (NTRS)

    Auger, Ludovic; Tangborn, Andrew; Atlas, Robert (Technical Monitor)

    2002-01-01

    A suboptimal Kalman filter system which evolves error covariances in terms of a truncated set of wavelet coefficients has been developed for the assimilation of chemical tracer observations of CH4. The truncation is carried out in such a way that the resolution of the error covariance, is reduced only in the zonal direction, where gradients are smaller. Assimilation experiments which last 24 days, and used different degrees of truncation were carried out. These reduced the covariance, by 90, 97 and 99 % and the computational cost of covariance propagation by 80, 93 and 96 % respectively. The difference in both error covariance and the tracer field between the truncated and full systems over this period were found to be not growing in the first case, and a growing relatively slowly in the later two cases. The largest errors in the tracer fields were found to occur in regions of largest zonal gradients in the tracer field.

  13. x-ray irradiation analysis based on wavelet transform in tokamak plasma.

    PubMed

    Ghanbari, K; Ghoranneviss, M; Elahi, A Salar; Saviz, S

    2014-01-01

    Hard x-ray emission from the Runaway electrons is an important issue in tokamaks. Suggesting methods to reduce the Runaway electrons and therefore the emitted hard x-ray is important for tokamak plasma operation. In this manuscript, we have investigated the effects of external fields on hard x-ray intensity and Magneto-Hydro-Dynamic (MHD) activity. In other words, we have presented the effects of positive biased limiter and Resonant Helical Field (RHF) on the MHD fluctuations and hard x-ray emission from the Runaway electrons. MHD activity and hard x-ray intensity were analyzed using Wavelet transform in the presence of external fields and without them. The results show that the MHD activity and therefore the hard x-ray intensity can be controlled by the external electric and magnetic fields.

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

    NASA Astrophysics Data System (ADS)

    Habib Huseni, Gulamhusenwala; Balaji, Ramakrishnan

    2016-06-01

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

  15. Wavelet De-noising of GNSS Based Bridge Health Monitoring Data

    NASA Astrophysics Data System (ADS)

    Ogundipe, Oluropo; Lee, Jae Kang; Roberts, Gethin Wyn

    2014-11-01

    GNSS signal multipath occurs when the GNSS signal reflects of objects in the antenna environment and arrives at the antenna via multiple paths. A bridge environment is one that is prone to multipath with the bridge structure, as well as passing vehicles providing static and dynamic sources of multipath. In this paper, the Wavelet Transform (WT) is applied to bridge data collected on the Machang cable stayed bridge in Korea. The WT algorithm was applied to the GNSS derived bridge defection data at the mid-span. Up to 41% improvement in RMS was observed afterwavelet shrinkage de-noisingwas applied.Application of this algorithm to the torsion data showed significant improvement with the residual average and RMS decreased by 40% and 45% respectively. This method enabled the generation of more accurate information for bridge health monitoring systems in terms of the analysis of frequency, mode shape and three dimensional defections.

  16. A wavelet-based evaluation of time-varying long memory of equity markets: A paradigm in crisis

    NASA Astrophysics Data System (ADS)

    Tan, Pei P.; Chin, Cheong W.; Galagedera, Don U. A.

    2014-09-01

    This study, using wavelet-based method investigates the dynamics of long memory in the returns and volatility of equity markets. In the sample of five developed and five emerging markets we find that the daily return series from January 1988 to June 2013 may be considered as a mix of weak long memory and mean-reverting processes. In the case of volatility in the returns, there is evidence of long memory, which is stronger in emerging markets than in developed markets. We find that although the long memory parameter may vary during crisis periods (1997 Asian financial crisis, 2001 US recession and 2008 subprime crisis) the direction of change may not be consistent across all equity markets. The degree of return predictability is likely to diminish during crisis periods. Robustness of the results is checked with de-trended fluctuation analysis approach.

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

    SciTech Connect

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

    2015-04-15

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

  18. Seizure onset detection based on a Uni- or multi-modal intelligent seizure acquisition (UISA/MISA) system.

    PubMed

    Conradsen, Isa; Beniczky, Sandor; Wolf, Peter; Henriksen, Jonas; Sams, Thomas; Sorensen, Helge B D

    2010-01-01

    An automatic Uni- or Multi-modal Intelligent Seizure Acquisition (UISA/MISA) system is highly applicable for onset detection of epileptic seizures based on motion data. The modalities used are surface electromyography (sEMG), acceleration (ACC) and angular velocity (ANG). The new proposed automatic algorithm on motion data is extracting features as "log-sum" measures of discrete wavelet components. Classification into the two groups "seizure" versus "non-seizure" is made based on the support vector machine (SVM) algorithm. The algorithm performs with a sensitivity of 91-100%, a median latency of 1 second and a specificity of 100% on multi-modal data from five healthy subjects simulating seizures. The uni-modal algorithm based on sEMG data from the subjects and patients performs satisfactorily in some cases. As expected, our results clearly show superiority of the multi-modal approach, as compared with the uni-modal one. PMID:21096611

  19. Assessments on GOCE-based Gravity Field Model Comparisons with Terrestrial Data Using Wavelet Decomposition and Spectral Enhancement Approaches

    NASA Astrophysics Data System (ADS)

    Erol, Serdar; Serkan Isık, Mustafa; Erol, Bihter

    2016-04-01

    The recent Earth gravity field satellite missions data lead significant improvement in Global Geopotential Models in terms of both accuracy and resolution. However the improvement in accuracy is not the same everywhere in the Earth and therefore quantifying the level of improvement locally is necessary using the independent data. The validations of the level-3 products from the gravity field satellite missions, independently from the estimation procedures of these products, are possible using various arbitrary data sets, as such the terrestrial gravity observations, astrogeodetic vertical deflections, GPS/leveling data, the stationary sea surface topography. Quantifying the quality of the gravity field functionals via recent products has significant importance for determination of the regional geoid modeling, base on the satellite and terrestrial data fusion with an optimal algorithm, beside the statistical reporting the improvement rates depending on spatial location. In the validations, the errors and the systematic differences between the data and varying spectral content of the compared signals should be considered in order to have comparable results. In this manner this study compares the performance of Wavelet decomposition and spectral enhancement techniques in validation of the GOCE/GRACE based Earth gravity field models using GPS/leveling and terrestrial gravity data in Turkey. The terrestrial validation data are filtered using Wavelet decomposition technique and the numerical results from varying levels of decomposition are compared with the results which are derived using the spectral enhancement approach with contribution of an ultra-high resolution Earth gravity field model. The tests include the GO-DIR-R5, GO-TIM-R5, GOCO05S, EIGEN-6C4 and EGM2008 global models. The conclusion discuss the superiority and drawbacks of both concepts as well as reporting the performance of tested gravity field models with an estimate of their contribution to modeling the

  20. Haar Wavelet Analysis of Climatic Time Series

    NASA Astrophysics Data System (ADS)

    Zhang, Zhihua; Moore, John; Grinsted, Aslak

    2014-05-01

    In order to extract the intrinsic information of climatic time series from background red noise, we will first give an analytic formula on the distribution of Haar wavelet power spectra of red noise in a rigorous statistical framework. The relation between scale aand Fourier period T for the Morlet wavelet is a= 0.97T . However, for Haar wavelet, the corresponding formula is a= 0.37T . Since for any time series of time step δt and total length Nδt, the range of scales is from the smallest resolvable scale 2δt to the largest scale Nδt in wavelet-based time series analysis, by using the Haar wavelet analysis, one can extract more low frequency intrinsic information. Finally, we use our method to analyze Arctic Oscillation which is a key aspect of climate variability in the Northern Hemisphere, and discover a great change in fundamental properties of the AO,-commonly called a regime shift or tripping point. Our partial results have been published as follows: [1] Z. Zhang, J.C. Moore and A. Grinsted, Haar wavelet analysis of climatic time series, Int. J. Wavelets, Multiresol. & Inf. Process., in press, 2013 [2] Z. Zhang, J.C. Moore, Comment on "Significance tests for the wavelet power and the wavelet power spectrum", Ann. Geophys., 30:12, 2012