Research on fusion algorithm of polarization image in tetrolet domain

NASA Astrophysics Data System (ADS)

Zhang, Dexiang; Yuan, BaoHong; Zhang, Jingjing

2015-12-01

Tetrolets are Haar-type wavelets whose supports are tetrominoes which are shapes made by connecting four equal-sized squares. A fusion method for polarization images based on tetrolet transform is proposed. Firstly, the magnitude of polarization image and angle of polarization image can be decomposed into low-frequency coefficients and high-frequency coefficients with multi-scales and multi-directions using tetrolet transform. For the low-frequency coefficients, the average fusion method is used. According to edge distribution differences in high frequency sub-band images, for the directional high-frequency coefficients are used to select the better coefficients by region spectrum entropy algorithm for fusion. At last the fused image can be obtained by utilizing inverse transform for fused tetrolet coefficients. Experimental results show that the proposed method can detect image features more effectively and the fused image has better subjective visual effect

An optimized digital watermarking algorithm in wavelet domain based on differential evolution for color image.

PubMed

Cui, Xinchun; Niu, Yuying; Zheng, Xiangwei; Han, Yingshuai

2018-01-01

In this paper, a new color watermarking algorithm based on differential evolution is proposed. A color host image is first converted from RGB space to YIQ space, which is more suitable for the human visual system. Then, apply three-level discrete wavelet transformation to luminance component Y and generate four different frequency sub-bands. After that, perform singular value decomposition on these sub-bands. In the watermark embedding process, apply discrete wavelet transformation to a watermark image after the scrambling encryption processing. Our new algorithm uses differential evolution algorithm with adaptive optimization to choose the right scaling factors. Experimental results show that the proposed algorithm has a better performance in terms of invisibility and robustness.

Onboard image compression schemes for modular airborne imaging spectrometer (MAIS) based on wavelet transform

NASA Astrophysics Data System (ADS)

Zhu, Zhenyu; Wang, Jianyu

1996-11-01

In this paper, two compression schemes are presented to meet the urgent needs of compressing the huge volume and high data rate of imaging spectrometer images. According to the multidimensional feature of the images and the high fidelity requirement of the reconstruction, both schemes were devised to exploit the high redundancy in both spatial and spectral dimension based on the mature wavelet transform technology. Wavelet transform was applied here in two ways: First, with the spatial wavelet transform and the spectral DPCM decorrelation, a ratio up to 84.3 with PSNR > 48db's near-lossless result was attained. This is based ont he fact that the edge structure among all the spectral bands are similar while WT has higher resolution in high frequency components. Secondly, with the wavelet's high efficiency in processing the 'wideband transient' signals, it was used to transform the raw nonstationary signals in the spectral dimension. A good result was also attained.

Wavelet compression techniques for hyperspectral data

NASA Technical Reports Server (NTRS)

Evans, Bruce; Ringer, Brian; Yeates, Mathew

1994-01-01

Hyperspectral sensors are electro-optic sensors which typically operate in visible and near infrared bands. Their characteristic property is the ability to resolve a relatively large number (i.e., tens to hundreds) of contiguous spectral bands to produce a detailed profile of the electromagnetic spectrum. In contrast, multispectral sensors measure relatively few non-contiguous spectral bands. Like multispectral sensors, hyperspectral sensors are often also imaging sensors, measuring spectra over an array of spatial resolution cells. The data produced may thus be viewed as a three dimensional array of samples in which two dimensions correspond to spatial position and the third to wavelength. Because they multiply the already large storage/transmission bandwidth requirements of conventional digital images, hyperspectral sensors generate formidable torrents of data. Their fine spectral resolution typically results in high redundancy in the spectral dimension, so that hyperspectral data sets are excellent candidates for compression. Although there have been a number of studies of compression algorithms for multispectral data, we are not aware of any published results for hyperspectral data. Three algorithms for hyperspectral data compression are compared. They were selected as representatives of three major approaches for extending conventional lossy image compression techniques to hyperspectral data. The simplest approach treats the data as an ensemble of images and compresses each image independently, ignoring the correlation between spectral bands. The second approach transforms the data to decorrelate the spectral bands, and then compresses the transformed data as a set of independent images. The third approach directly generalizes two-dimensional transform coding by applying a three-dimensional transform as part of the usual transform-quantize-entropy code procedure. The algorithms studied all use the discrete wavelet transform. In the first two cases, a wavelet transform coder was used for the two-dimensional compression. The third case used a three dimensional extension of this same algorithm.

Multi-resolution analysis for ear recognition using wavelet features

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

Optical asymmetric image encryption using gyrator wavelet transform

NASA Astrophysics Data System (ADS)

Mehra, Isha; Nishchal, Naveen K.

2015-11-01

In this paper, we propose a new optical information processing tool termed as gyrator wavelet transform to secure a fully phase image, based on amplitude- and phase-truncation approach. The gyrator wavelet transform constitutes four basic parameters; gyrator transform order, type and level of mother wavelet, and position of different frequency bands. These parameters are used as encryption keys in addition to the random phase codes to the optical cryptosystem. This tool has also been applied for simultaneous compression and encryption of an image. The system's performance and its sensitivity to the encryption parameters, such as, gyrator transform order, and robustness has also been analyzed. It is expected that this tool will not only update current optical security systems, but may also shed some light on future developments. The computer simulation results demonstrate the abilities of the gyrator wavelet transform as an effective tool, which can be used in various optical information processing applications, including image encryption, and image compression. Also this tool can be applied for securing the color image, multispectral, and three-dimensional images.

Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

USGS Publications Warehouse

Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

2002-01-01

Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.

Analysis of embolic signals with directional dual tree rational dilation wavelet transform.

PubMed

Serbes, Gorkem; Aydin, Nizamettin

2016-08-01

The dyadic discrete wavelet transform (dyadic-DWT), which is based on fixed integer sampling factor, has been used before for processing piecewise smooth biomedical signals. However, the dyadic-DWT has poor frequency resolution due to the low-oscillatory nature of its wavelet bases and therefore, it is less effective in processing embolic signals (ESs). To process ESs more effectively, a wavelet transform having better frequency resolution than the dyadic-DWT is needed. Therefore, in this study two ESs, containing micro-emboli and artifact waveforms, are analyzed with the Directional Dual Tree Rational-Dilation Wavelet Transform (DDT-RADWT). The DDT-RADWT, which can be directly applied to quadrature signals, is based on rational dilation factors and has adjustable frequency resolution. The analyses are done for both low and high Q-factors. It is proved that, when high Q-factor filters are employed in the DDT-RADWT, clearer representations of ESs can be attained in decomposed sub-bands and artifacts can be successfully separated.

Wavelet transformation to determine impedance spectra of lithium-ion rechargeable battery

NASA Astrophysics Data System (ADS)

Hoshi, Yoshinao; Yakabe, Natsuki; Isobe, Koichiro; Saito, Toshiki; Shitanda, Isao; Itagaki, Masayuki

2016-05-01

A new analytical method is proposed to determine the electrochemical impedance of lithium-ion rechargeable batteries (LIRB) from time domain data by wavelet transformation (WT). The WT is a waveform analysis method that can transform data in the time domain to the frequency domain while retaining time information. In this transformation, the frequency domain data are obtained by the convolution integral of a mother wavelet and original time domain data. A complex Morlet mother wavelet (CMMW) is used to obtain the complex number data in the frequency domain. The CMMW is expressed by combining a Gaussian function and sinusoidal term. The theory to select a set of suitable conditions for variables and constants related to the CMMW, i.e., band, scale, and time parameters, is established by determining impedance spectra from wavelet coefficients using input voltage to the equivalent circuit and the output current. The impedance spectrum of LIRB determined by WT agrees well with that measured using a frequency response analyzer.

[Estimation of organic matter content of north fluvo-aquic soil based on the coupling model of wavelet transform and partial least squares].

PubMed

Wang, Yan-Cang; Yang, Gui-Jun; Zhu, Jin-Shan; Gu, Xiao-He; Xu, Peng; Liao, Qin-Hong

2014-07-01

For improving the estimation accuracy of soil organic matter content of the north fluvo-aquic soil, wavelet transform technology is introduced. The soil samples were collected from Tongzhou district and Shunyi district in Beijing city. And the data source is from soil hyperspectral data obtained under laboratory condition. First, discrete wavelet transform efficiently decomposes hyperspectral into approximate coefficients and detail coefficients. Then, the correlation between approximate coefficients, detail coefficients and organic matter content was analyzed, and the sensitive bands of the organic matter were screened. Finally, models were established to estimate the soil organic content by using the partial least squares regression (PLSR). Results show that the NIR bands made more contributions than the visible band in estimating organic matter content models; the ability of approximate coefficients to estimate organic matter content is better than that of detail coefficients; The estimation precision of the detail coefficients fir soil organic matter content decreases with the spectral resolution being lower; Compared with the commonly used three types of soil spectral reflectance transforms, the wavelet transform can improve the estimation ability of soil spectral fir organic content; The accuracy of the best model established by the approximate coefficients or detail coefficients is higher, and the coefficient of determination (R2) and the root mean square error (RMSE) of the best model for approximate coefficients are 0.722 and 0.221, respectively. The R2 and RMSE of the best model for detail coefficients are 0.670 and 0.255, respectively.

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

PubMed Central

Chen, Duo; Wan, Suiren; Xiang, Jing; Bao, Forrest Sheng

2017-01-01

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets. PMID:28278203

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.

Performance of the Wavelet Decomposition on Massively Parallel Architectures

NASA Technical Reports Server (NTRS)

El-Ghazawi, Tarek A.; LeMoigne, Jacqueline; Zukor, Dorothy (Technical Monitor)

2001-01-01

Traditionally, Fourier Transforms have been utilized for performing signal analysis and representation. But although it is straightforward to reconstruct a signal from its Fourier transform, no local description of the signal is included in its Fourier representation. To alleviate this problem, Windowed Fourier transforms and then wavelet transforms have been introduced, and it has been proven that wavelets give a better localization than traditional Fourier transforms, as well as a better division of the time- or space-frequency plane than Windowed Fourier transforms. Because of these properties and after the development of several fast algorithms for computing the wavelet representation of any signal, in particular the Multi-Resolution Analysis (MRA) developed by Mallat, wavelet transforms have increasingly been applied to signal analysis problems, especially real-life problems, in which speed is critical. In this paper we present and compare efficient wavelet decomposition algorithms on different parallel architectures. We report and analyze experimental measurements, using NASA remotely sensed images. Results show that our algorithms achieve significant performance gains on current high performance parallel systems, and meet scientific applications and multimedia requirements. The extensive performance measurements collected over a number of high-performance computer systems have revealed important architectural characteristics of these systems, in relation to the processing demands of the wavelet decomposition of digital images.

Comparison of data transformation procedures to enhance topographical accuracy in time-series analysis of the human EEG.

PubMed

Hauk, O; Keil, A; Elbert, T; Müller, M M

2002-01-30

We describe a methodology to apply current source density (CSD) and minimum norm (MN) estimation as pre-processing tools for time-series analysis of single trial EEG data. The performance of these methods is compared for the case of wavelet time-frequency analysis of simulated gamma-band activity. A reasonable comparison of CSD and MN on the single trial level requires regularization such that the corresponding transformed data sets have similar signal-to-noise ratios (SNRs). For region-of-interest approaches, it should be possible to optimize the SNR for single estimates rather than for the whole distributed solution. An effective implementation of the MN method is described. Simulated data sets were created by modulating the strengths of a radial and a tangential test dipole with wavelets in the frequency range of the gamma band, superimposed with simulated spatially uncorrelated noise. The MN and CSD transformed data sets as well as the average reference (AR) representation were subjected to wavelet frequency-domain analysis, and power spectra were mapped for relevant frequency bands. For both CSD and MN, the influence of noise can be sufficiently suppressed by regularization to yield meaningful information, but only MN represents both radial and tangential dipole sources appropriately as single peaks. Therefore, when relating wavelet power spectrum topographies to their neuronal generators, MN should be preferred.

Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification

NASA Astrophysics Data System (ADS)

Du, Peijun; Tan, Kun; Xing, Xiaoshi

2010-12-01

Combining Support Vector Machine (SVM) with wavelet analysis, we constructed wavelet SVM (WSVM) classifier based on wavelet kernel functions in Reproducing Kernel Hilbert Space (RKHS). In conventional kernel theory, SVM is faced with the bottleneck of kernel parameter selection which further results in time-consuming and low classification accuracy. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. Airborne Operational Modular Imaging Spectrometer II (OMIS II) hyperspectral remote sensing image with 64 bands and Reflective Optics System Imaging Spectrometer (ROSIS) data with 115 bands were used to experiment the performance and accuracy of the proposed WSVM classifier. The experimental results indicate that the WSVM classifier can obtain the highest accuracy when using the Coiflet Kernel function in wavelet transform. In contrast with some traditional classifiers, including Spectral Angle Mapping (SAM) and Minimum Distance Classification (MDC), and SVM classifier using Radial Basis Function kernel, the proposed wavelet SVM classifier using the wavelet kernel function in Reproducing Kernel Hilbert Space is capable of improving classification accuracy obviously.

Wavelet entropy: a new tool for analysis of short duration brain electrical signals.

PubMed

Rosso, O A; Blanco, S; Yordanova, J; Kolev, V; Figliola, A; Schürmann, M; Başar, E

2001-01-30

Since traditional electrical brain signal analysis is mostly qualitative, the development of new quantitative methods is crucial for restricting the subjectivity in the study of brain signals. These methods are particularly fruitful when they are strongly correlated with intuitive physical concepts that allow a better understanding of brain dynamics. Here, new method based on orthogonal discrete wavelet transform (ODWT) is applied. It takes as a basic element the ODWT of the EEG signal, and defines the relative wavelet energy, the wavelet entropy (WE) and the relative wavelet entropy (RWE). The relative wavelet energy provides information about the relative energy associated with different frequency bands present in the EEG and their corresponding degree of importance. The WE carries information about the degree of order/disorder associated with a multi-frequency signal response, and the RWE measures the degree of similarity between different segments of the signal. In addition, the time evolution of the WE is calculated to give information about the dynamics in the EEG records. Within this framework, the major objective of the present work was to characterize in a quantitative way functional dynamics of order/disorder microstates in short duration EEG signals. For that aim, spontaneous EEG signals under different physiological conditions were analyzed. Further, specific quantifiers were derived to characterize how stimulus affects electrical events in terms of frequency synchronization (tuning) in the event related potentials.

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. © 2015 Elsevier Inc. All rights reserved.

Joint sparse reconstruction of multi-contrast MRI images with graph based redundant wavelet transform.

PubMed

Lai, Zongying; Zhang, Xinlin; Guo, Di; Du, Xiaofeng; Yang, Yonggui; Guo, Gang; Chen, Zhong; Qu, Xiaobo

2018-05-03

Multi-contrast images in magnetic resonance imaging (MRI) provide abundant contrast information reflecting the characteristics of the internal tissues of human bodies, and thus have been widely utilized in clinical diagnosis. However, long acquisition time limits the application of multi-contrast MRI. One efficient way to accelerate data acquisition is to under-sample the k-space data and then reconstruct images with sparsity constraint. However, images are compromised at high acceleration factor if images are reconstructed individually. We aim to improve the images with a jointly sparse reconstruction and Graph-based redundant wavelet transform (GBRWT). First, a sparsifying transform, GBRWT, is trained to reflect the similarity of tissue structures in multi-contrast images. Second, joint multi-contrast image reconstruction is formulated as a ℓ 2, 1 norm optimization problem under GBRWT representations. Third, the optimization problem is numerically solved using a derived alternating direction method. Experimental results in synthetic and in vivo MRI data demonstrate that the proposed joint reconstruction method can achieve lower reconstruction errors and better preserve image structures than the compared joint reconstruction methods. Besides, the proposed method outperforms single image reconstruction with joint sparsity constraint of multi-contrast images. The proposed method explores the joint sparsity of multi-contrast MRI images under graph-based redundant wavelet transform and realizes joint sparse reconstruction of multi-contrast images. Experiment demonstrate that the proposed method outperforms the compared joint reconstruction methods as well as individual reconstructions. With this high quality image reconstruction method, it is possible to achieve the high acceleration factors by exploring the complementary information provided by multi-contrast MRI.

Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation

NASA Astrophysics Data System (ADS)

Ji, Zhan-Huai; Yan, Sheng-Gang

2017-12-01

This paper presents an analytical study of the complete transform of improved Gabor wavelets (IGWs), and discusses its application to the processing and interpretation of seismic signals. The complete Gabor wavelet transform has the following properties. First, unlike the conventional transform, the improved Gabor wavelet transform (IGWT) maps time domain signals to the time-frequency domain instead of the time-scale domain. Second, the IGW's dominant frequency is fixed, so the transform can perform signal frequency division, where the dominant frequency components of the extracted sub-band signal carry essentially the same information as the corresponding components of the original signal, and the subband signal bandwidth can be regulated effectively by the transform's resolution factor. Third, a time-frequency filter consisting of an IGWT and its inverse transform can accurately locate target areas in the time-frequency field and perform filtering in a given time-frequency range. The complete IGW transform's properties are investigated using simulation experiments and test cases, showing positive results for seismic signal processing and interpretation, such as enhancing seismic signal resolution, permitting signal frequency division, and allowing small faults to be identified.

Directional dual-tree complex wavelet packet transforms for processing quadrature signals.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2016-03-01

Quadrature signals containing in-phase and quadrature-phase components are used in many signal processing applications in every field of science and engineering. Specifically, Doppler ultrasound systems used to evaluate cardiovascular disorders noninvasively also result in quadrature format signals. In order to obtain directional blood flow information, the quadrature outputs have to be preprocessed using methods such as asymmetrical and symmetrical phasing filter techniques. These resultant directional signals can be employed in order to detect asymptomatic embolic signals caused by small emboli, which are indicators of a possible future stroke, in the cerebral circulation. Various transform-based methods such as Fourier and wavelet were frequently used in processing embolic signals. However, most of the times, the Fourier and discrete wavelet transforms are not appropriate for the analysis of embolic signals due to their non-stationary time-frequency behavior. Alternatively, discrete wavelet packet transform can perform an adaptive decomposition of the time-frequency axis. In this study, directional discrete wavelet packet transforms, which have the ability to map directional information while processing quadrature signals and have less computational complexity than the existing wavelet packet-based methods, are introduced. The performances of proposed methods are examined in detail by using single-frequency, synthetic narrow-band, and embolic quadrature signals.

Image Fusion Algorithms Using Human Visual System in Transform Domain

NASA Astrophysics Data System (ADS)

Vadhi, Radhika; Swamy Kilari, Veera; Samayamantula, Srinivas Kumar

2017-08-01

The endeavor of digital image fusion is to combine the important visual parts from various sources to advance the visibility eminence of the image. The fused image has a more visual quality than any source images. In this paper, the Human Visual System (HVS) weights are used in the transform domain to select appropriate information from various source images and then to attain a fused image. In this process, mainly two steps are involved. First, apply the DWT to the registered source images. Later, identify qualitative sub-bands using HVS weights. Hence, qualitative sub-bands are selected from different sources to form high quality HVS based fused image. The quality of the HVS based fused image is evaluated with general fusion metrics. The results show the superiority among the state-of-the art resolution Transforms (MRT) such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Contourlet Transform (CT), and Non Sub Sampled Contourlet Transform (NSCT) using maximum selection fusion rule.

Reversible integer wavelet transform for blind image hiding method

PubMed Central

Bibi, Nargis; Mahmood, Zahid; Akram, Tallha; Naqvi, Syed Rameez

2017-01-01

In this article, a blind data hiding reversible methodology to embed the secret data for hiding purpose into cover image is proposed. The key advantage of this research work is to resolve the privacy and secrecy issues raised during the data transmission over the internet. Firstly, data is decomposed into sub-bands using the integer wavelets. For decomposition, the Fresnelet transform is utilized which encrypts the secret data by choosing a unique key parameter to construct a dummy pattern. The dummy pattern is then embedded into an approximated sub-band of the cover image. Our proposed method reveals high-capacity and great imperceptibility of the secret embedded data. With the utilization of family of integer wavelets, the proposed novel approach becomes more efficient for hiding and retrieving process. It retrieved the secret hidden data from the embedded data blindly, without the requirement of original cover image. PMID:28498855

Simultaneous compression and encryption for secure real-time secure transmission of sensitive video transmission

NASA Astrophysics Data System (ADS)

Al-Hayani, Nazar; Al-Jawad, Naseer; Jassim, Sabah A.

2014-05-01

Video compression and encryption became very essential in a secured real time video transmission. Applying both techniques simultaneously is one of the challenges where the size and the quality are important in multimedia transmission. In this paper we proposed a new technique for video compression and encryption. Both encryption and compression are based on edges extracted from the high frequency sub-bands of wavelet decomposition. The compression algorithm based on hybrid of: discrete wavelet transforms, discrete cosine transform, vector quantization, wavelet based edge detection, and phase sensing. The compression encoding algorithm treats the video reference and non-reference frames in two different ways. The encryption algorithm utilized A5 cipher combined with chaotic logistic map to encrypt the significant parameters and wavelet coefficients. Both algorithms can be applied simultaneously after applying the discrete wavelet transform on each individual frame. Experimental results show that the proposed algorithms have the following features: high compression, acceptable quality, and resistance to the statistical and bruteforce attack with low computational processing.

Multi-threshold de-noising of electrical imaging logging data based on the wavelet packet transform

NASA Astrophysics Data System (ADS)

Xie, Fang; Xiao, Chengwen; Liu, Ruilin; Zhang, Lili

2017-08-01

A key problem of effectiveness evaluation for fractured-vuggy carbonatite reservoir is how to accurately extract fracture and vug information from electrical imaging logging data. Drill bits quaked during drilling and resulted in rugged surfaces of borehole walls and thus conductivity fluctuations in electrical imaging logging data. The occurrence of the conductivity fluctuations (formation background noise) directly affects the fracture/vug information extraction and reservoir effectiveness evaluation. We present a multi-threshold de-noising method based on wavelet packet transform to eliminate the influence of rugged borehole walls. The noise is present as fluctuations in button-electrode conductivity curves and as pockmarked responses in electrical imaging logging static images. The noise has responses in various scales and frequency ranges and has low conductivity compared with fractures or vugs. Our de-noising method is to decompose the data into coefficients with wavelet packet transform on a quadratic spline basis, then shrink high-frequency wavelet packet coefficients in different resolutions with minimax threshold and hard-threshold function, and finally reconstruct the thresholded coefficients. We use electrical imaging logging data collected from fractured-vuggy Ordovician carbonatite reservoir in Tarim Basin to verify the validity of the multi-threshold de-noising method. Segmentation results and extracted parameters are shown as well to prove the effectiveness of the de-noising procedure.

Poisson denoising on the sphere

NASA Astrophysics Data System (ADS)

Schmitt, J.; Starck, J. L.; Fadili, J.; Grenier, I.; Casandjian, J. M.

2009-08-01

In the scope of the Fermi mission, Poisson noise removal should improve data quality and make source detection easier. This paper presents a method for Poisson data denoising on sphere, called Multi-Scale Variance Stabilizing Transform on Sphere (MS-VSTS). This method is based on a Variance Stabilizing Transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has an (asymptotically) constant variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. Thus, MS-VSTS consists in decomposing the data into a sparse multi-scale dictionary (wavelets, curvelets, ridgelets...), and then applying a VST on the coefficients in order to get quasi-Gaussian stabilized coefficients. In this present article, the used multi-scale transform is the Isotropic Undecimated Wavelet Transform. Then, hypothesis tests are made to detect significant coefficients, and the denoised image is reconstructed with an iterative method based on Hybrid Steepest Descent (HST). The method is tested on simulated Fermi data.

The use of multiwavelets for uncertainty estimation in seismic surface wave dispersion.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poppeliers, Christian

This report describes a new single-station analysis method to estimate the dispersion and uncer- tainty of seismic surface waves using the multiwavelet transform. Typically, when estimating the dispersion of a surface wave using only a single seismic station, the seismogram is decomposed into a series of narrow-band realizations using a bank of narrow-band filters. By then enveloping and normalizing the filtered seismograms and identifying the maximum power as a function of frequency, the group velocity can be estimated if the source-receiver distance is known. However, using the filter bank method, there is no robust way to estimate uncertainty. In thismore » report, I in- troduce a new method of estimating the group velocity that includes an estimate of uncertainty. The method is similar to the conventional filter bank method, but uses a class of functions, called Slepian wavelets, to compute a series of wavelet transforms of the data. Each wavelet transform is mathematically similar to a filter bank, however, the time-frequency tradeoff is optimized. By taking multiple wavelet transforms, I form a population of dispersion estimates from which stan- dard statistical methods can be used to estimate uncertainty. I demonstrate the utility of this new method by applying it to synthetic data as well as ambient-noise surface-wave cross-correlelograms recorded by the University of Nevada Seismic Network.« less

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Behera, Abhinna K., E-mail: abhinna@iiserkol.ac.in; Panigrahi, Prasanta K., E-mail: pprasanta@iiserkol.ac.in; Sekar Iyengar, A. N., E-mail: ansekar.iyengar@saha.ac.in

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 tomore » neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.« less

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

NASA Astrophysics Data System (ADS)

Huang, Yan; Wang, Zhihui

2015-12-01

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

Fast measurement of proton exchange membrane fuel cell impedance based on pseudo-random binary sequence perturbation signals and continuous wavelet transform

NASA Astrophysics Data System (ADS)

Debenjak, Andrej; Boškoski, Pavle; Musizza, Bojan; Petrovčič, Janko; Juričić, Đani

2014-05-01

This paper proposes an approach to the estimation of PEM fuel cell impedance by utilizing pseudo-random binary sequence as a perturbation signal and continuous wavelet transform with Morlet mother wavelet. With the approach, the impedance characteristic in the frequency band from 0.1 Hz to 500 Hz is identified in 60 seconds, approximately five times faster compared to the conventional single-sine approach. The proposed approach was experimentally evaluated on a single PEM fuel cell of a larger fuel cell stack. The quality of the results remains at the same level compared to the single-sine approach.

Comparative Study of Speckle Filtering Methods in PolSAR Radar Images

NASA Astrophysics Data System (ADS)

Boutarfa, S.; Bouchemakh, L.; Smara, Y.

2015-04-01

Images acquired by polarimetric SAR (PolSAR) radar systems are characterized by the presence of a noise called speckle. This noise has a multiplicative nature, corrupts both the amplitude and phase images, which complicates data interpretation, degrades segmentation performance and reduces the detectability of targets. Hence, the need to preprocess the images by adapted filtering methods before analysis.In this paper, we present a comparative study of implemented methods for reducing speckle in PolSAR images. These developed filters are: refined Lee filter based on the estimation of the minimum mean square error MMSE, improved Sigma filter with detection of strong scatterers based on the calculation of the coherency matrix to detect the different scatterers in order to preserve the polarization signature and maintain structures that are necessary for image interpretation, filtering by stationary wavelet transform SWT using multi-scale edge detection and the technique for improving the wavelet coefficients called SSC (sum of squared coefficients), and Turbo filter which is a combination between two complementary filters the refined Lee filter and the wavelet transform SWT. One filter can boost up the results of the other.The originality of our work is based on the application of these methods to several types of images: amplitude, intensity and complex, from a satellite or an airborne radar, and on the optimization of wavelet filtering by adding a parameter in the calculation of the threshold. This parameter will control the filtering effect and get a good compromise between smoothing homogeneous areas and preserving linear structures.The methods are applied to the fully polarimetric RADARSAT-2 images (HH, HV, VH, VV) acquired on Algiers, Algeria, in C-band and to the three polarimetric E-SAR images (HH, HV, VV) acquired on Oberpfaffenhofen area located in Munich, Germany, in P-band.To evaluate the performance of each filter, we used the following criteria: smoothing homogeneous areas, preserving edges and polarimetric information.Experimental results are included to illustrate the different implemented methods.

Study of low insertion loss and miniaturization wavelet transform and inverse transform processor using SAW devices.

PubMed

Jiang, Hua; Lu, Wenke; Zhang, Guoan

2013-07-01

In this paper, we propose a low insertion loss and miniaturization wavelet transform and inverse transform processor using surface acoustic wave (SAW) devices. The new SAW wavelet transform devices (WTDs) use the structure with two electrode-widths-controlled (EWC) single phase unidirectional transducers (SPUDT-SPUDT). This structure consists of the input withdrawal weighting interdigital transducer (IDT) and the output overlap weighting IDT. Three experimental devices for different scales 2(-1), 2(-2), and 2(-3) are designed and measured. The minimum insertion loss of the three devices reaches 5.49dB, 4.81dB, and 5.38dB respectively which are lower than the early results. Both the electrode width and the number of electrode pairs are reduced, thus making the three devices much smaller than the early devices. Therefore, the method described in this paper is suitable for implementing an arbitrary multi-scale low insertion loss and miniaturization wavelet transform and inverse transform processor using SAW devices. Copyright © 2013 Elsevier B.V. All rights reserved.

Detection and classification of Breast Cancer in Wavelet Sub-bands of Fractal Segmented Cancerous Zones.

PubMed

Shirazinodeh, Alireza; Noubari, Hossein Ahmadi; Rabbani, Hossein; Dehnavi, Alireza Mehri

2015-01-01

Recent studies on wavelet transform and fractal modeling applied on mammograms for the detection of cancerous tissues indicate that microcalcifications and masses can be utilized for the study of the morphology and diagnosis of cancerous cases. It is shown that the use of fractal modeling, as applied to a given image, can clearly discern cancerous zones from noncancerous areas. In this paper, for fractal modeling, the original image is first segmented into appropriate fractal boxes followed by identifying the fractal dimension of each windowed section using a computationally efficient two-dimensional box-counting algorithm. Furthermore, using appropriate wavelet sub-bands and image Reconstruction based on modified wavelet coefficients, it is shown that it is possible to arrive at enhanced features for detection of cancerous zones. In this paper, we have attempted to benefit from the advantages of both fractals and wavelets by introducing a new algorithm. By using a new algorithm named F1W2, the original image is first segmented into appropriate fractal boxes, and the fractal dimension of each windowed section is extracted. Following from that, by applying a maximum level threshold on fractal dimensions matrix, the best-segmented boxes are selected. In the next step, the segmented Cancerous zones which are candidates are then decomposed by utilizing standard orthogonal wavelet transform and db2 wavelet in three different resolution levels, and after nullifying wavelet coefficients of the image at the first scale and low frequency band of the third scale, the modified reconstructed image is successfully utilized for detection of breast cancer regions by applying an appropriate threshold. For detection of cancerous zones, our simulations indicate the accuracy of 90.9% for masses and 88.99% for microcalcifications detection results using the F1W2 method. For classification of detected mictocalcification into benign and malignant cases, eight features are identified and utilized in radial basis function neural network. Our simulation results indicate the accuracy of 92% classification using F1W2 method.

Response of Autonomic Nervous System to Body Positions:

NASA Astrophysics Data System (ADS)

Xu, Aiguo; Gonnella, G.; Federici, A.; Stramaglia, S.; Simone, F.; Zenzola, A.; Santostasi, R.

Two mathematical methods, the Fourier and wavelet transforms, were used to study the short term cardiovascular control system. Time series, picked from electrocardiogram and arterial blood pressure lasting 6 minutes, were analyzed in supine position (SUP), during the first (HD1) and the second parts (HD2) of 90° head down tilt, and during recovery (REC). The wavelet transform was performed using the Haar function of period T=2j (j=1,2,...,6) to obtain wavelet coefficients. Power spectra components were analyzed within three bands, VLF (0.003-0.04), LF (0.04-0.15) and HF (0.15-0.4) with the frequency unit cycle/interval. Wavelet transform demonstrated a higher discrimination among all analyzed periods than the Fourier transform. For the Fourier analysis, the LF of R-R intervals and VLF of systolic blood pressure show more evident difference for different body positions. For the wavelet analysis, the systolic blood pressures show much more evident differences than the R-R intervals. This study suggests a difference in the response of the vessels and the heart to different body positions. The partial dissociation between VLF and LF results is a physiologically relevant finding of this work.

Wavelet Filter Banks for Super-Resolution SAR Imaging

NASA Technical Reports Server (NTRS)

Sheybani, Ehsan O.; Deshpande, Manohar; Memarsadeghi, Nargess

2011-01-01

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Emotion recognition from multichannel EEG signals using K-nearest neighbor classification.

PubMed

Li, Mi; Xu, Hongpei; Liu, Xingwang; Lu, Shengfu

2018-04-27

Many studies have been done on the emotion recognition based on multi-channel electroencephalogram (EEG) signals. This paper explores the influence of the emotion recognition accuracy of EEG signals in different frequency bands and different number of channels. We classified the emotional states in the valence and arousal dimensions using different combinations of EEG channels. Firstly, DEAP default preprocessed data were normalized. Next, EEG signals were divided into four frequency bands using discrete wavelet transform, and entropy and energy were calculated as features of K-nearest neighbor Classifier. The classification accuracies of the 10, 14, 18 and 32 EEG channels based on the Gamma frequency band were 89.54%, 92.28%, 93.72% and 95.70% in the valence dimension and 89.81%, 92.24%, 93.69% and 95.69% in the arousal dimension. As the number of channels increases, the classification accuracy of emotional states also increases, the classification accuracy of the gamma frequency band is greater than that of the beta frequency band followed by the alpha and theta frequency bands. This paper provided better frequency bands and channels reference for emotion recognition based on EEG.

Built-Up Area Detection from High-Resolution Satellite Images Using Multi-Scale Wavelet Transform and Local Spatial Statistics

NASA Astrophysics Data System (ADS)

Chen, Y.; Zhang, Y.; Gao, J.; Yuan, Y.; Lv, Z.

2018-04-01

Recently, built-up area detection from high-resolution satellite images (HRSI) has attracted increasing attention because HRSI can provide more detailed object information. In this paper, multi-resolution wavelet transform and local spatial autocorrelation statistic are introduced to model the spatial patterns of built-up areas. First, the input image is decomposed into high- and low-frequency subbands by wavelet transform at three levels. Then the high-frequency detail information in three directions (horizontal, vertical and diagonal) are extracted followed by a maximization operation to integrate the information in all directions. Afterward, a cross-scale operation is implemented to fuse different levels of information. Finally, local spatial autocorrelation statistic is introduced to enhance the saliency of built-up features and an adaptive threshold algorithm is used to achieve the detection of built-up areas. Experiments are conducted on ZY-3 and Quickbird panchromatic satellite images, and the results show that the proposed method is very effective for built-up area detection.

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.

Prediction of the outcome in cardiac arrest patients undergoing hypothermia using EEG wavelet entropy.

PubMed

Moshirvaziri, Hana; Ramezan-Arab, Nima; Asgari, Shadnaz

2016-08-01

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients' outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (p-value <; 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia.

Development of an FBG Sensor Array for Multi-Impact Source Localization on CFRP Structures.

PubMed

Jiang, Mingshun; Sai, Yaozhang; Geng, Xiangyi; Sui, Qingmei; Liu, Xiaohui; Jia, Lei

2016-10-24

We proposed and studied an impact detection system based on a fiber Bragg grating (FBG) sensor array and multiple signal classification (MUSIC) algorithm to determine the location and the number of low velocity impacts on a carbon fiber-reinforced polymer (CFRP) plate. A FBG linear array, consisting of seven FBG sensors, was used for detecting the ultrasonic signals from impacts. The edge-filter method was employed for signal demodulation. Shannon wavelet transform was used to extract narrow band signals from the impacts. The Gerschgorin disc theorem was used for estimating the number of impacts. We used the MUSIC algorithm to obtain the coordinates of multi-impacts. The impact detection system was tested on a 500 mm × 500 mm × 1.5 mm CFRP plate. The results show that the maximum error and average error of the multi-impacts' localization are 9.2 mm and 7.4 mm, respectively.

Discrete wavelet transform: a tool in smoothing kinematic data.

PubMed

Ismail, A R; Asfour, S S

1999-03-01

Motion analysis systems typically introduce noise to the displacement data recorded. Butterworth digital filters have been used to smooth the displacement data in order to obtain smoothed velocities and accelerations. However, this technique does not yield satisfactory results, especially when dealing with complex kinematic motions that occupy the low- and high-frequency bands. The use of the discrete wavelet transform, as an alternative to digital filters, is presented in this paper. The transform passes the original signal through two complementary low- and high-pass FIR filters and decomposes the signal into an approximation function and a detail function. Further decomposition of the signal results in transforming the signal into a hierarchy set of orthogonal approximation and detail functions. A reverse process is employed to perfectly reconstruct the signal (inverse transform) back from its approximation and detail functions. The discrete wavelet transform was applied to the displacement data recorded by Pezzack et al., 1977. The smoothed displacement data were twice differentiated and compared to Pezzack et al.'s acceleration data in order to choose the most appropriate filter coefficients and decomposition level on the basis of maximizing the percentage of retained energy (PRE) and minimizing the root mean square error (RMSE). Daubechies wavelet of the fourth order (Db4) at the second decomposition level showed better results than both the biorthogonal and Coiflet wavelets (PRE = 97.5%, RMSE = 4.7 rad s-2). The Db4 wavelet was then used to compress complex displacement data obtained from a noisy mathematically generated function. Results clearly indicate superiority of this new smoothing approach over traditional filters.

Wavelet based analysis of multi-electrode EEG-signals in epilepsy

NASA Astrophysics Data System (ADS)

Hein, Daniel A.; Tetzlaff, Ronald

2005-06-01

For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

On wavelet analysis of auditory evoked potentials.

PubMed

Bradley, A P; Wilson, W J

2004-05-01

To determine a preferred wavelet transform (WT) procedure for multi-resolution analysis (MRA) of auditory evoked potentials (AEP). A number of WT algorithms, mother wavelets, and pre-processing techniques were examined by way of critical theoretical discussion followed by experimental testing of key points using real and simulated auditory brain-stem response (ABR) waveforms. Conclusions from these examinations were then tested on a normative ABR dataset. The results of the various experiments are reported in detail. Optimal AEP WT MRA is most likely to occur when an over-sampled discrete wavelet transformation (DWT) is used, utilising a smooth (regularity >or=3) and symmetrical (linear phase) mother wavelet, and a reflection boundary extension policy. This study demonstrates the practical importance of, and explains how to minimize potential artefacts due to, 4 inter-related issues relevant to AEP WT MRA, namely shift variance, phase distortion, reconstruction smoothness, and boundary artefacts.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Objective research of auscultation signals in Traditional Chinese Medicine based on wavelet packet energy and support vector machine.

PubMed

Yan, Jianjun; Shen, Xiaojing; Wang, Yiqin; Li, Fufeng; Xia, Chunming; Guo, Rui; Chen, Chunfeng; Shen, Qingwei

2010-01-01

This study aims at utilising Wavelet Packet Transform (WPT) and Support Vector Machine (SVM) algorithm to make objective analysis and quantitative research for the auscultation in Traditional Chinese Medicine (TCM) diagnosis. First, Wavelet Packet Decomposition (WPD) at level 6 was employed to split more elaborate frequency bands of the auscultation signals. Then statistic analysis was made based on the extracted Wavelet Packet Energy (WPE) features from WPD coefficients. Furthermore, the pattern recognition was used to distinguish mixed subjects' statistical feature values of sample groups through SVM. Finally, the experimental results showed that the classification accuracies were at a high level.

Multi-frequency data analysis in AFM by wavelet transform

NASA Astrophysics Data System (ADS)

Pukhova, V.; Ferrini, G.

2017-10-01

Interacting cantilevers in AFM experiments generate non-stationary, multi-frequency signals consisting of numerous excited flexural and torsional modes and their harmonics. The analysis of such signals is challenging, requiring special methodological approaches and a powerful mathematical apparatus. The most common approach to the signal analysis is to apply Fourier transform analysis. However, FT gives accurate spectra for stationary signals, and for signals changing their spectral content over time, FT provides only an averaged spectrum. Hence, for non-stationary and rapidly varying signals, such as those from interacting cantilevers, a method that shows the spectral evolution in time is needed. One of the most powerful techniques, allowing detailed time-frequency representation of signals, is the wavelet transform. It is a method of analysis that allows representation of energy associated to the signal at a particular frequency and time, providing correlation between the spectral and temporal features of the signal, unlike FT. This is particularly important in AFM experiments because signals nonlinearities contains valuable information about tip-sample interactions and consequently surfaces properties. The present work is aimed to show the advantages of wavelet transform in comparison with FT using as an example the force curve analysis in dynamic force spectroscopy.

The parallel algorithm for the 2D discrete wavelet transform

NASA Astrophysics Data System (ADS)

Barina, David; Najman, Pavel; Kleparnik, Petr; Kula, Michal; Zemcik, Pavel

2018-04-01

The discrete wavelet transform can be found at the heart of many image-processing algorithms. Until now, the transform on general-purpose processors (CPUs) was mostly computed using a separable lifting scheme. As the lifting scheme consists of a small number of operations, it is preferred for processing using single-core CPUs. However, considering a parallel processing using multi-core processors, this scheme is inappropriate due to a large number of steps. On such architectures, the number of steps corresponds to the number of points that represent the exchange of data. Consequently, these points often form a performance bottleneck. Our approach appropriately rearranges calculations inside the transform, and thereby reduces the number of steps. In other words, we propose a new scheme that is friendly to parallel environments. When evaluating on multi-core CPUs, we consistently overcome the original lifting scheme. The evaluation was performed on 61-core Intel Xeon Phi and 8-core Intel Xeon processors.

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats.

PubMed

Richard, Nelly; Laursen, Bettina; Grupe, Morten; Drewes, Asbjørn M; Graversen, Carina; Sørensen, Helge B D; Bastlund, Jesper F

2017-04-01

Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats. An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task. While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones. The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.

Damage localization in aluminum plate with compact rectangular phased piezoelectric transducer array

NASA Astrophysics Data System (ADS)

Liu, Zenghua; Sun, Kunming; Song, Guorong; He, Cunfu; Wu, Bin

2016-03-01

In this work, a detection method for the damage in plate-like structure with a compact rectangular phased piezoelectric transducer array of 16 piezoelectric elements was presented. This compact array can not only detect and locate a single defect (through hole) in plate, but also identify multi-defects (through holes and surface defect simulated by an iron pillar glued to the plate). The experiments proved that the compact rectangular phased transducer array could detect the full range of plate structures and implement multiple-defect detection simultaneously. The processing algorithm proposed in this paper contains two parts: signal filtering and damage imaging. The former part was used to remove noise from signals. Continuous wavelet transform was applicable to signal filtering. Continuous wavelet transform can provide a plot of wavelet coefficients and the signal with narrow frequency band can be easily extracted from the plot. The latter part of processing algorithm was to implement damage detection and localization. In order to accurately locate defects and improve the imaging quality, two images were obtained from amplitude and phase information. One image was obtained with the Total Focusing Method (TFM) and another phase image was obtained with the Sign Coherence Factor (SCF). Furthermore, an image compounding technique for compact rectangular phased piezoelectric transducer array was proposed in this paper. With the proposed technique, the compounded image can be obtained by combining TFM image with SCF image, thus greatly improving the resolution and contrast of image.

Electroencephalographic compression based on modulated filter banks and wavelet transform.

PubMed

Bazán-Prieto, Carlos; Cárdenas-Barrera, Julián; Blanco-Velasco, Manuel; Cruz-Roldán, Fernando

2011-01-01

Due to the large volume of information generated in an electroencephalographic (EEG) study, compression is needed for storage, processing or transmission for analysis. In this paper we evaluate and compare two lossy compression techniques applied to EEG signals. It compares the performance of compression schemes with decomposition by filter banks or wavelet Packets transformation, seeking the best value for compression, best quality and more efficient real time implementation. Due to specific properties of EEG signals, we propose a quantization stage adapted to the dynamic range of each band, looking for higher quality. The results show that the compressor with filter bank performs better than transform methods. Quantization adapted to the dynamic range significantly enhances the quality.

Plasma plume oscillations monitoring during laser welding of stainless steel by discrete wavelet transform application.

PubMed

Sibillano, Teresa; Ancona, Antonio; Rizzi, Domenico; Lupo, Valentina; Tricarico, Luigi; Lugarà, Pietro Mario

2010-01-01

The plasma optical radiation emitted during CO2 laser welding of stainless steel samples has been detected with a Si-PIN photodiode and analyzed under different process conditions. The discrete wavelet transform (DWT) has been used to decompose the optical signal into various discrete series of sequences over different frequency bands. The results show that changes of the process settings may yield different signal features in the range of frequencies between 200 Hz and 30 kHz. Potential applications of this method to monitor in real time the laser welding processes are also discussed.

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. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

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. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Multispectral multisensor image fusion using wavelet transforms

USGS Publications Warehouse

Lemeshewsky, George P.

1999-01-01

Fusion techniques can be applied to multispectral and higher spatial resolution panchromatic images to create a composite image that is easier to interpret than the individual images. Wavelet transform-based multisensor, multiresolution fusion (a type of band sharpening) was applied to Landsat thematic mapper (TM) multispectral and coregistered higher resolution SPOT panchromatic images. The objective was to obtain increased spatial resolution, false color composite products to support the interpretation of land cover types wherein the spectral characteristics of the imagery are preserved to provide the spectral clues needed for interpretation. Since the fusion process should not introduce artifacts, a shift invariant implementation of the discrete wavelet transform (SIDWT) was used. These results were compared with those using the shift variant, discrete wavelet transform (DWT). Overall, the process includes a hue, saturation, and value color space transform to minimize color changes, and a reported point-wise maximum selection rule to combine transform coefficients. The performance of fusion based on the SIDWT and DWT was evaluated with a simulated TM 30-m spatial resolution test image and a higher resolution reference. Simulated imagery was made by blurring higher resolution color-infrared photography with the TM sensors' point spread function. The SIDWT based technique produced imagery with fewer artifacts and lower error between fused images and the full resolution reference. Image examples with TM and SPOT 10-m panchromatic illustrate the reduction in artifacts due to the SIDWT based fusion.

Time delay estimation using new spectral and adaptive filtering methods with applications to underwater target detection

NASA Astrophysics Data System (ADS)

Hasan, Mohammed A.

1997-11-01

In this dissertation, we present several novel approaches for detection and identification of targets of arbitrary shapes from the acoustic backscattered data and using the incident waveform. This problem is formulated as time- delay estimation and sinusoidal frequency estimation problems which both have applications in many other important areas in signal processing. Solving time-delay estimation problem allows the identification of the specular components in the backscattered signal from elastic and non-elastic targets. Thus, accurate estimation of these time delays would help in determining the existence of certain clues for detecting targets. Several new methods for solving these two problems in the time, frequency and wavelet domains are developed. In the time domain, a new block fast transversal filter (BFTF) is proposed for a fast implementation of the least squares (LS) method. This BFTF algorithm is derived by using data-related constrained block-LS cost function to guarantee global optimality. The new soft-constrained algorithm provides an efficient way of transferring weight information between blocks of data and thus it is computationally very efficient compared with other LS- based schemes. Additionally, the tracking ability of the algorithm can be controlled by varying the block length and/or a soft constrained parameter. The effectiveness of this algorithm is tested on several underwater acoustic backscattered data for elastic targets and non-elastic (cement chunk) objects. In the frequency domain, the time-delay estimation problem is converted to a sinusoidal frequency estimation problem by using the discrete Fourier transform. Then, the lagged sample covariance matrices of the resulting signal are computed and studied in terms of their eigen- structure. These matrices are shown to be robust and effective in extracting bases for the signal and noise subspaces. New MUSIC and matrix pencil-based methods are derived these subspaces. The effectiveness of the method is demonstrated on the problem of detection of multiple specular components in the acoustic backscattered data. Finally, a method for the estimation of time delays using wavelet decomposition is derived. The sub-band adaptive filtering uses discrete wavelet transform for multi- resolution or sub-band decomposition. Joint time delay estimation for identifying multi-specular components and subsequent adaptive filtering processes are performed on the signal in each sub-band. This would provide multiple 'look' of the signal at different resolution scale which results in more accurate estimates for delays associated with the specular components. Simulation results on the simulated and real shallow water data are provided which show the promise of this new scheme for target detection in a heavy cluttered environment.

Defect detection of helical gears based on time-frequency analysis and using multi-layer fusion network

NASA Astrophysics Data System (ADS)

Ebrahimi Orimi, H.; Esmaeili, M.; Refahi Oskouei, A.; Mirhadizadehd, S. A.; Tse, P. W.

2017-10-01

Condition monitoring of rotary devices such as helical gears is an issue of great significance in industrial projects. This paper introduces a feature extraction method for gear fault diagnosis using wavelet packet due to its higher frequency resolution. During this investigation, the mother wavelet Daubechies 10 (Db-10) was applied to calculate the coefficient entropy of each frequency band of 5th level (32 frequency bands) as features. In this study, the peak value of the signal entropies was selected as applicable features in order to improve frequency band differentiation and reduce feature vectors' dimension. Feature extraction is followed by the fusion network where four different structured multi-layer perceptron networks are trained to classify the recorded signals (healthy/faulty). The robustness of fusion network outputs is greater compared to perceptron networks. The results provided by the fusion network indicate a classification of 98.88 and 97.95% for healthy and faulty classes, respectively.

Analysis and Synthesis of Pseudo-Periodic[InlineEquation not available: see fulltext.]-Like Noise by Means of Wavelets with Applications to Digital Audio

NASA Astrophysics Data System (ADS)

Polotti, Pietro; Evangelista, Gianpaolo

2001-12-01

Voiced musical sounds have nonzero energy in sidebands of the frequency partials. Our work is based on the assumption, often experimentally verified, that the energy distribution of the sidebands is shaped as powers of the inverse of the distance from the closest partial. The power spectrum of these pseudo-periodic processes is modeled by means of a superposition of modulated[InlineEquation not available: see fulltext.] components, that is, by a pseudo-periodic[InlineEquation not available: see fulltext.]-like process. Due to the fundamental selfsimilar character of the wavelet transform,[InlineEquation not available: see fulltext.] processes can be fruitfully analyzed and synthesized by means of wavelets. We obtain a set of very loosely correlated coefficients at each scale level that can be well approximated by white noise in the synthesis process. Our computational scheme is based on an orthogonal[InlineEquation not available: see fulltext.]-band filter bank and a dyadic wavelet transform per channel. The[InlineEquation not available: see fulltext.] channels are tuned to the left and right sidebands of the harmonics so that sidebands are mutually independent. The structure computes the expansion coefficients of a new orthogonal and complete set of harmonic-band wavelets. The main point of our scheme is that we need only two parameters per harmonic in order to model the stochastic fluctuations of sounds from a pure periodic behavior.

Exact reconstruction with directional wavelets on the sphere

NASA Astrophysics Data System (ADS)

Wiaux, Y.; McEwen, J. D.; Vandergheynst, P.; Blanc, O.

2008-08-01

A new formalism is derived for the analysis and exact reconstruction of band-limited signals on the sphere with directional wavelets. It represents an evolution of a previously developed wavelet formalism developed by Antoine & Vandergheynst and Wiaux et al. The translations of the wavelets at any point on the sphere and their proper rotations are still defined through the continuous three-dimensional rotations. The dilations of the wavelets are directly defined in harmonic space through a new kernel dilation, which is a modification of an existing harmonic dilation. A family of factorized steerable functions with compact harmonic support which are suitable for this kernel dilation are first identified. A scale-discretized wavelet formalism is then derived, relying on this dilation. The discrete nature of the analysis scales allows the exact reconstruction of band-limited signals. A corresponding exact multi-resolution algorithm is finally described and an implementation is tested. The formalism is of interest notably for the denoising or the deconvolution of signals on the sphere with a sparse expansion in wavelets. In astrophysics, it finds a particular application for the identification of localized directional features in the cosmic microwave background data, such as the imprint of topological defects, in particular, cosmic strings, and for their reconstruction after separation from the other signal components.

Nonlinear analysis of EEG in major depression with fractal dimensions.

PubMed

Akar, Saime A; Kara, Sadik; Agambayev, Sumeyra; Bilgic, Vedat

2015-01-01

Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.

Spectral information enhancement using wavelet-based iterative filtering for in vivo gamma spectrometry.

PubMed

Paul, Sabyasachi; Sarkar, P K

2013-04-01

Use of wavelet transformation in stationary signal processing has been demonstrated for denoising the measured spectra and characterisation of radionuclides in the in vivo monitoring analysis, where difficulties arise due to very low activity level to be estimated in biological systems. The large statistical fluctuations often make the identification of characteristic gammas from radionuclides highly uncertain, particularly when interferences from progenies are also present. A new wavelet-based noise filtering methodology has been developed for better detection of gamma peaks in noisy data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for noise rejection and an inverse transform after soft 'thresholding' over the generated coefficients. Analyses of in vivo monitoring data of (235)U and (238)U were carried out using this method without disturbing the peak position and amplitude while achieving a 3-fold improvement in the signal-to-noise ratio, compared with the original measured spectrum. When compared with other data-filtering techniques, the wavelet-based method shows the best results.

Impact of multi-resolution analysis of artificial intelligence models inputs on multi-step ahead river flow forecasting

NASA Astrophysics Data System (ADS)

Badrzadeh, Honey; Sarukkalige, Ranjan; Jayawardena, A. W.

2013-12-01

Discrete wavelet transform was applied to decomposed ANN and ANFIS inputs.Novel approach of WNF with subtractive clustering applied for flow forecasting.Forecasting was performed in 1-5 step ahead, using multi-variate inputs.Forecasting accuracy of peak values and longer lead-time significantly improved.

Application of lifting wavelet and random forest in compound fault diagnosis of gearbox

NASA Astrophysics Data System (ADS)

Chen, Tang; Cui, Yulian; Feng, Fuzhou; Wu, Chunzhi

2018-03-01

Aiming at the weakness of compound fault characteristic signals of a gearbox of an armored vehicle and difficult to identify fault types, a fault diagnosis method based on lifting wavelet and random forest is proposed. First of all, this method uses the lifting wavelet transform to decompose the original vibration signal in multi-layers, reconstructs the multi-layer low-frequency and high-frequency components obtained by the decomposition to get multiple component signals. Then the time-domain feature parameters are obtained for each component signal to form multiple feature vectors, which is input into the random forest pattern recognition classifier to determine the compound fault type. Finally, a variety of compound fault data of the gearbox fault analog test platform are verified, the results show that the recognition accuracy of the fault diagnosis method combined with the lifting wavelet and the random forest is up to 99.99%.

Wavelet analysis for wind fields estimation.

PubMed

Leite, Gladeston C; Ushizima, Daniela M; Medeiros, Fátima N S; de Lima, Gilson G

2010-01-01

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B(3) spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms(-1). Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms.

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

NASA Astrophysics Data System (ADS)

Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali

2017-07-01

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.

Development of a classification method for a crack on a pavement surface images using machine learning

NASA Astrophysics Data System (ADS)

Hizukuri, Akiyoshi; Nagata, Takeshi

2017-03-01

The purpose of this study is to develop a classification method for a crack on a pavement surface image using machine learning to reduce a maintenance fee. Our database consists of 3500 pavement surface images. This includes 800 crack and 2700 normal pavement surface images. The pavement surface images first are decomposed into several sub-images using a discrete wavelet transform (DWT) decomposition. We then calculate the wavelet sub-band histogram from each several sub-images at each level. The support vector machine (SVM) with computed wavelet sub-band histogram is employed for distinguishing between a crack and normal pavement surface images. The accuracies of the proposed classification method are 85.3% for crack and 84.4% for normal pavement images. The proposed classification method achieved high performance. Therefore, the proposed method would be useful in maintenance inspection.

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.

Periodicity and Multi-scale Analysis of Runoff and Sediment Load in the Wulanghe River, Jinsha River

NASA Astrophysics Data System (ADS)

Chen, Yiming

2018-01-01

Based on the annual runoff and sediment data (1959-2014 ) of Zongguantian hydrological station, time-frequency wavelet transform characteristics and their periodic rules of high and low flow alternating change were analyzed in multi-time scales by the Morlet continue wavelet transformation (CWT). It is concluded that the primary periods of runoff and sediment load time series of the high and low annual flow in the different time scales were 12-year, 3-year and 26-year, 18-year, 13-year, 5-year, respectively, and predicted that the major variant trend of the two time series would been gradually decreasing and been in the high flow period around 8-year (from 2014 to 2022) and 10-year (from 2014 to 2020).

Visibility of wavelet quantization noise

NASA Technical Reports Server (NTRS)

Watson, A. B.; Yang, G. Y.; Solomon, J. A.; Villasenor, J.

1997-01-01

The discrete wavelet transform (DWT) decomposes an image into bands that vary in spatial frequency and orientation. It is widely used for image compression. Measures of the visibility of DWT quantization errors are required to achieve optimal compression. Uniform quantization of a single band of coefficients results in an artifact that we call DWT uniform quantization noise; it is the sum of a lattice of random amplitude basis functions of the corresponding DWT synthesis filter. We measured visual detection thresholds for samples of DWT uniform quantization noise in Y, Cb, and Cr color channels. The spatial frequency of a wavelet is r 2-lambda, where r is display visual resolution in pixels/degree, and lambda is the wavelet level. Thresholds increase rapidly with wavelet spatial frequency. Thresholds also increase from Y to Cr to Cb, and with orientation from lowpass to horizontal/vertical to diagonal. We construct a mathematical model for DWT noise detection thresholds that is a function of level, orientation, and display visual resolution. This allows calculation of a "perceptually lossless" quantization matrix for which all errors are in theory below the visual threshold. The model may also be used as the basis for adaptive quantization schemes.

Wavelet-like bases for thin-wire integral equations in electromagnetics

NASA Astrophysics Data System (ADS)

Francomano, E.; Tortorici, A.; Toscano, E.; Ala, G.; Viola, F.

2005-03-01

In this paper, wavelets are used in solving, by the method of moments, a modified version of the thin-wire electric field integral equation, in frequency domain. The time domain electromagnetic quantities, are obtained by using the inverse discrete fast Fourier transform. The retarded scalar electric and vector magnetic potentials are employed in order to obtain the integral formulation. The discretized model generated by applying the direct method of moments via point-matching procedure, results in a linear system with a dense matrix which have to be solved for each frequency of the Fourier spectrum of the time domain impressed source. Therefore, orthogonal wavelet-like basis transform is used to sparsify the moment matrix. In particular, dyadic and M-band wavelet transforms have been adopted, so generating different sparse matrix structures. This leads to an efficient solution in solving the resulting sparse matrix equation. Moreover, a wavelet preconditioner is used to accelerate the convergence rate of the iterative solver employed. These numerical features are used in analyzing the transient behavior of a lightning protection system. In particular, the transient performance of the earth termination system of a lightning protection system or of the earth electrode of an electric power substation, during its operation is focused. The numerical results, obtained by running a complex structure, are discussed and the features of the used method are underlined.

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.

Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis.

PubMed

Hu, Shan; Xu, Chao; Guan, Weiqiao; Tang, Yong; Liu, Yana

2014-01-01

Osteosarcoma is the most common malignant bone tumor among children and adolescents. In this study, image texture analysis was made to extract texture features from bone CR images to evaluate the recognition rate of osteosarcoma. To obtain the optimal set of features, Sym4 and Db4 wavelet transforms and gray-level co-occurrence matrices were applied to the image, with statistical methods being used to maximize the feature selection. To evaluate the performance of these methods, a support vector machine algorithm was used. The experimental results demonstrated that the Sym4 wavelet had a higher classification accuracy (93.44%) than the Db4 wavelet with respect to osteosarcoma occurrence in the epiphysis, whereas the Db4 wavelet had a higher classification accuracy (96.25%) for osteosarcoma occurrence in the diaphysis. Results including accuracy, sensitivity, specificity and ROC curves obtained using the wavelets were all higher than those obtained using the features derived from the GLCM method. It is concluded that, a set of texture features can be extracted from the wavelets and used in computer-aided osteosarcoma diagnosis systems. In addition, this study also confirms that multi-resolution analysis is a useful tool for texture feature extraction during bone CR image processing.

Wavelet Analysis for Wind Fields Estimation

PubMed Central

Leite, Gladeston C.; Ushizima, Daniela M.; Medeiros, Fátima N. S.; de Lima, Gilson G.

2010-01-01

Wind field analysis from synthetic aperture radar images allows the estimation of wind direction and speed based on image descriptors. In this paper, we propose a framework to automate wind direction retrieval based on wavelet decomposition associated with spectral processing. We extend existing undecimated wavelet transform approaches, by including à trous with B3 spline scaling function, in addition to other wavelet bases as Gabor and Mexican-hat. The purpose is to extract more reliable directional information, when wind speed values range from 5 to 10 ms−1. Using C-band empirical models, associated with the estimated directional information, we calculate local wind speed values and compare our results with QuikSCAT scatterometer data. The proposed approach has potential application in the evaluation of oil spills and wind farms. PMID:22219699

Double image encryption in Fresnel domain using wavelet transform, gyrator transform and spiral phase masks

NASA Astrophysics Data System (ADS)

Kumar, Ravi; Bhaduri, Basanta

2017-06-01

In this paper, we propose a new technique for double image encryption in the Fresnel domain using wavelet transform (WT), gyrator transform (GT) and spiral phase masks (SPMs). The two input mages are first phase encoded and each of them are then multiplied with SPMs and Fresnel propagated with distances d1 and d2, respectively. The single-level discrete WT is applied to Fresnel propagated complex images to decompose each into sub-band matrices i.e. LL, HL, LH and HH. Further, the sub-band matrices of two complex images are interchanged after modulation with random phase masks (RPMs) and subjected to inverse discrete WT. The resulting images are then both added and subtracted to get intermediate images which are further Fresnel propagated with distances d3 and d4, respectively. These outputs are finally gyrator transformed with the same angle α to get the encrypted images. The proposed technique provides enhanced security in terms of a large set of security keys. The sensitivity of security keys such as SPM parameters, GT angle α, Fresnel propagation distances are investigated. The robustness of the proposed techniques against noise and occlusion attacks are also analysed. The numerical simulation results are shown in support of the validity and effectiveness of the proposed technique.

An unsupervised machine learning method for delineating stratum corneum in reflectance confocal microscopy stacks of human skin in vivo

NASA Astrophysics Data System (ADS)

Bozkurt, Alican; Kose, Kivanc; Fox, Christi A.; Dy, Jennifer; Brooks, Dana H.; Rajadhyaksha, Milind

2016-02-01

Study of the stratum corneum (SC) in human skin is important for research in barrier structure and function, drug delivery, and water permeability of skin. The optical sectioning and high resolution of reflectance confocal microscopy (RCM) allows visual examination of SC non-invasively. Here, we present an unsupervised segmentation algorithm that can automatically delineate thickness of the SC in RCM images of human skin in-vivo. We mimic clinicians visual process by applying complex wavelet transform over non-overlapping local regions of size 16 x 16 μm called tiles, and analyze the textural changes in between consecutive tiles in axial (depth) direction. We use dual-tree complex wavelet transform to represent textural structures in each tile. This transform is almost shift-invariant, and directionally selective, which makes it highly efficient in texture representation. Using DT-CWT, we decompose each tile into 6 directional sub-bands with orientations in +/-15, 45, and 75 degrees and a low-pass band, which is the decimated version of the input. We apply 3 scales of decomposition by recursively transforming the low-pass bands and obtain 18 bands of different directionality at different scales. We then calculate mean and variance of each band resulting in a feature vector of 36 entries. Feature vectors obtained for each stack of tiles in axial direction are then clustered using spectral clustering in order to detect the textural changes in depth direction. Testing on a set of 15 RCM stacks produced a mean error of 5.45+/-1.32 μm, compared to the "ground truth" segmentation provided by a clinical expert reader.

Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

PubMed

Ghorbanian, Parham; Devilbiss, David M; Hess, Terry; Bernstein, Allan; Simon, Adam J; Ashrafiuon, Hashem

2015-09-01

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

Poisson noise removal with pyramidal multi-scale transforms

NASA Astrophysics Data System (ADS)

Woiselle, Arnaud; Starck, Jean-Luc; Fadili, Jalal M.

2013-09-01

In this paper, we introduce a method to stabilize the variance of decimated transforms using one or two variance stabilizing transforms (VST). These VSTs are applied to the 3-D Meyer wavelet pyramidal transform which is the core of the first generation 3D curvelets. This allows us to extend these 3-D curvelets to handle Poisson noise, that we apply to the denoising of a simulated cosmological volume.

Automated transformation-invariant shape recognition through wavelet multiresolution

NASA Astrophysics Data System (ADS)

Brault, Patrice; Mounier, Hugues

2001-12-01

We present here new results in Wavelet Multi-Resolution Analysis (W-MRA) applied to shape recognition in automatic vehicle driving applications. Different types of shapes have to be recognized in this framework. They pertain to most of the objects entering the sensors field of a car. These objects can be road signs, lane separation lines, moving or static obstacles, other automotive vehicles, or visual beacons. The recognition process must be invariant to global, affine or not, transformations which are : rotation, translation and scaling. It also has to be invariant to more local, elastic, deformations like the perspective (in particular with wide angle camera lenses), and also like deformations due to environmental conditions (weather : rain, mist, light reverberation) or optical and electrical signal noises. To demonstrate our method, an initial shape, with a known contour, is compared to the same contour altered by rotation, translation, scaling and perspective. The curvature computed for each contour point is used as a main criterion in the shape matching process. The original part of this work is to use wavelet descriptors, generated with a fast orthonormal W-MRA, rather than Fourier descriptors, in order to provide a multi-resolution description of the contour to be analyzed. In such way, the intrinsic spatial localization property of wavelet descriptors can be used and the recognition process can be speeded up. The most important part of this work is to demonstrate the potential performance of Wavelet-MRA in this application of shape recognition.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock.

PubMed

Papaioannou, Vasilios E; Chouvarda, Ioanna G; Maglaveras, Nikos K; Pneumatikos, Ioannis A

2012-12-12

Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness.

Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock

PubMed Central

2012-01-01

Background Even though temperature is a continuous quantitative variable, its measurement has been considered a snapshot of a process, indicating whether a patient is febrile or afebrile. Recently, other diagnostic techniques have been proposed for the association between different properties of the temperature curve with severity of illness in the Intensive Care Unit (ICU), based on complexity analysis of continuously monitored body temperature. In this study, we tried to assess temperature complexity in patients with systemic inflammation during a suspected ICU-acquired infection, by using wavelets transformation and multiscale entropy of temperature signals, in a cohort of mixed critically ill patients. Methods Twenty-two patients were enrolled in the study. In five, systemic inflammatory response syndrome (SIRS, group 1) developed, 10 had sepsis (group 2), and seven had septic shock (group 3). All temperature curves were studied during the first 24 hours of an inflammatory state. A wavelet transformation was applied, decomposing the signal in different frequency components (scales) that have been found to reflect neurogenic and metabolic inputs on temperature oscillations. Wavelet energy and entropy per different scales associated with complexity in specific frequency bands and multiscale entropy of the whole signal were calculated. Moreover, a clustering technique and a linear discriminant analysis (LDA) were applied for permitting pattern recognition in data sets and assessing diagnostic accuracy of different wavelet features among the three classes of patients. Results Statistically significant differences were found in wavelet entropy between patients with SIRS and groups 2 and 3, and in specific ultradian bands between SIRS and group 3, with decreased entropy in sepsis. Cluster analysis using wavelet features in specific bands revealed concrete clusters closely related with the groups in focus. LDA after wrapper-based feature selection was able to classify with an accuracy of more than 80% SIRS from the two sepsis groups, based on multiparametric patterns of entropy values in the very low frequencies and indicating reduced metabolic inputs on local thermoregulation, probably associated with extensive vasodilatation. Conclusions We suggest that complexity analysis of temperature signals can assess inherent thermoregulatory dynamics during systemic inflammation and has increased discriminating value in patients with infectious versus noninfectious conditions, probably associated with severity of illness. PMID:22424316

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

NASA Astrophysics Data System (ADS)

Richard, Nelly; Laursen, Bettina; Grupe, Morten; Drewes, Asbjørn M.; Graversen, Carina; Sørensen, Helge B. D.; Bastlund, Jesper F.

2017-04-01

Objective. Active auditory oddball paradigms are simple tone discrimination tasks used to study the P300 deflection of event-related potentials (ERPs). These ERPs may be quantified by time-frequency analysis. As auditory stimuli cause early high frequency and late low frequency ERP oscillations, the continuous wavelet transform (CWT) is often chosen for decomposition due to its multi-resolution properties. However, as the conventional CWT traditionally applies only one mother wavelet to represent the entire spectrum, the time-frequency resolution is not optimal across all scales. To account for this, we developed and validated a novel method specifically refined to analyse P300-like ERPs in rats. Approach. An adapted CWT (aCWT) was implemented to preserve high time-frequency resolution across all scales by commissioning of multiple wavelets operating at different scales. First, decomposition of simulated ERPs was illustrated using the classical CWT and the aCWT. Next, the two methods were applied to EEG recordings obtained from prefrontal cortex in rats performing a two-tone auditory discrimination task. Main results. While only early ERP frequency changes between responses to target and non-target tones were detected by the CWT, both early and late changes were successfully described with strong accuracy by the aCWT in rat ERPs. Increased frontal gamma power and phase synchrony was observed particularly within theta and gamma frequency bands during deviant tones. Significance. The study suggests superior performance of the aCWT over the CWT in terms of detailed quantification of time-frequency properties of ERPs. Our methodological investigation indicates that accurate and complete assessment of time-frequency components of short-time neural signals is feasible with the novel analysis approach which may be advantageous for characterisation of several types of evoked potentials in particularly rodents.

[A quality controllable algorithm for ECG compression based on wavelet transform and ROI coding].

PubMed

Zhao, An; Wu, Baoming

2006-12-01

This paper presents an ECG compression algorithm based on wavelet transform and region of interest (ROI) coding. The algorithm has realized near-lossless coding in ROI and quality controllable lossy coding outside of ROI. After mean removal of the original signal, multi-layer orthogonal discrete wavelet transform is performed. Simultaneously,feature extraction is performed on the original signal to find the position of ROI. The coefficients related to the ROI are important coefficients and kept. Otherwise, the energy loss of the transform domain is calculated according to the goal PRDBE (Percentage Root-mean-square Difference with Baseline Eliminated), and then the threshold of the coefficients outside of ROI is determined according to the loss of energy. The important coefficients, which include the coefficients of ROI and the coefficients that are larger than the threshold outside of ROI, are put into a linear quantifier. The map, which records the positions of the important coefficients in the original wavelet coefficients vector, is compressed with a run-length encoder. Huffman coding has been applied to improve the compression ratio. ECG signals taken from the MIT/BIH arrhythmia database are tested, and satisfactory results in terms of clinical information preserving, quality and compress ratio are obtained.

[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.

Deep learning for classification of islanding and grid disturbance based on multi-resolution singular spectrum entropy

NASA Astrophysics Data System (ADS)

Li, Tie; He, Xiaoyang; Tang, Junci; Zeng, Hui; Zhou, Chunying; Zhang, Nan; Liu, Hui; Lu, Zhuoxin; Kong, Xiangrui; Yan, Zheng

2018-02-01

Forasmuch as the distinguishment of islanding is easy to be interfered by grid disturbance, island detection device may make misjudgment thus causing the consequence of photovoltaic out of service. The detection device must provide with the ability to differ islanding from grid disturbance. In this paper, the concept of deep learning is introduced into classification of islanding and grid disturbance for the first time. A novel deep learning framework is proposed to detect and classify islanding or grid disturbance. The framework is a hybrid of wavelet transformation, multi-resolution singular spectrum entropy, and deep learning architecture. As a signal processing method after wavelet transformation, multi-resolution singular spectrum entropy combines multi-resolution analysis and spectrum analysis with entropy as output, from which we can extract the intrinsic different features between islanding and grid disturbance. With the features extracted, deep learning is utilized to classify islanding and grid disturbance. Simulation results indicate that the method can achieve its goal while being highly accurate, so the photovoltaic system mistakenly withdrawing from power grids can be avoided.

Improved CEEMDAN-wavelet transform de-noising method and its application in well logging noise reduction

NASA Astrophysics Data System (ADS)

Zhang, Jingxia; Guo, Yinghai; Shen, Yulin; Zhao, Difei; Li, Mi

2018-06-01

The use of geophysical logging data to identify lithology is an important groundwork in logging interpretation. Inevitably, noise is mixed in during data collection due to the equipment and other external factors and this will affect the further lithological identification and other logging interpretation. Therefore, to get a more accurate lithological identification it is necessary to adopt de-noising methods. In this study, a new de-noising method, namely improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet transform, is proposed, which integrates the superiorities of improved CEEMDAN and wavelet transform. Improved CEEMDAN, an effective self-adaptive multi-scale analysis method, is used to decompose non-stationary signals as the logging data to obtain the intrinsic mode function (IMF) of N different scales and one residual. Moreover, one self-adaptive scale selection method is used to determine the reconstruction scale k. Simultaneously, given the possible frequency aliasing problem between adjacent IMFs, a wavelet transform threshold de-noising method is used to reduce the noise of the (k-1)th IMF. Subsequently, the de-noised logging data are reconstructed by the de-noised (k-1)th IMF and the remaining low-frequency IMFs and the residual. Finally, empirical mode decomposition, improved CEEMDAN, wavelet transform and the proposed method are applied for analysis of the simulation and the actual data. Results show diverse performance of these de-noising methods with regard to accuracy for lithological identification. Compared with the other methods, the proposed method has the best self-adaptability and accuracy in lithological identification.

Autocorrelation Analysis Combined with a Wavelet Transform Method to Detect and Remove Cosmic Rays in a Single Raman Spectrum.

PubMed

Maury, Augusto; Revilla, Reynier I

2015-08-01

Cosmic rays (CRs) occasionally affect charge-coupled device (CCD) detectors, introducing large spikes with very narrow bandwidth in the spectrum. These CR features can distort the chemical information expressed by the spectra. Consequently, we propose here an algorithm to identify and remove significant spikes in a single Raman spectrum. An autocorrelation analysis is first carried out to accentuate the CRs feature as outliers. Subsequently, with an adequate selection of the threshold, a discrete wavelet transform filter is used to identify CR spikes. Identified data points are then replaced by interpolated values using the weighted-average interpolation technique. This approach only modifies the data in a close vicinity of the CRs. Additionally, robust wavelet transform parameters are proposed (a desirable property for automation) after optimizing them with the application of the method in a great number of spectra. However, this algorithm, as well as all the single-spectrum analysis procedures, is limited to the cases in which CRs have much narrower bandwidth than the Raman bands. This might not be the case when low-resolution Raman instruments are used.

HRV analysis in local anesthesia using Continuous Wavelet Transform (CWT).

PubMed

Shafqat, K; Pal, S K; Kumari, S; Kyriacou, P A

2011-01-01

Spectral analysis of Heart Rate Variability (HRV) is used for the assessment of cardiovascular autonomic control. In this study Continuous Wavelet Transform (CWT) has been used to evaluate the effect of local anesthesia on HRV parameters in a group of fourteen patients undergoing axillary brachial plexus block. A new method which takes signal characteristics into account has been presented for the estimation of the variable boundaries associated with the low and the high frequency band of the HRV signal. The variable boundary method might be useful in cases when the power related to respiration component extends beyond the traditionally excepted range of the high frequency band (0.15-0.4 Hz). The statistical analysis (non-parametric Wilcoxon signed rank test) showed that the LF/HF ratio decreased within an hour of the application of the brachial plexus block compared to the values fifteen minutes prior to the application of the block. These changes were observed in thirteen of the fourteen patients included in this study.

Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges

USGS Publications Warehouse

Lemeshewsky, George P.; Schowengerdt, Robert A.

2000-01-01

Several reported image fusion or sharpening techniques are based on the discrete wavelet transform (DWT). The technique described here uses a pixel-based maximum selection rule to combine respective transform coefficients of lower spatial resolution near-infrared (NIR) and higher spatial resolution panchromatic (pan) imagery to produce a sharpened NIR image. Sharpening assumes a radiometric correlation between the spectral band images. However, there can be poor correlation, including edge contrast reversals (e.g., at soil-vegetation boundaries), between the fused images and, consequently, degraded performance. To improve sharpening, a local area-based correlation technique originally reported for edge comparison with image pyramid fusion is modified for application with the DWT process. Further improvements are obtained by using redundant, shift-invariant implementation of the DWT. Example images demonstrate the improvements in NIR image sharpening with higher resolution pan imagery.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Wavelet Transform Based Filter to Remove the Notches from Signal Under Harmonic Polluted Environment

NASA Astrophysics Data System (ADS)

Das, Sukanta; Ranjan, Vikash

2017-12-01

The work proposes to annihilate the notches present in the synchronizing signal required for converter operation appearing due to switching of semiconductor devices connected to the system in the harmonic polluted environment. The disturbances in the signal are suppressed by wavelet based novel filtering technique. In the proposed technique, the notches in the signal are determined and eliminated by the wavelet based multi-rate filter using `Daubechies4' (db4) as mother wavelet. The computational complexity of the adapted technique is very less as compared to any other conventional notch filtering techniques. The proposed technique is developed in MATLAB/Simulink and finally validated with dSPACE-1103 interface. The recovered signal, thus obtained, is almost free of the notches.

Time frequency power profile of QRS complex obtained with wavelet transform in spontaneously hypertensive rats.

PubMed

Takano, Nami K; Tsutsumi, Takeshi; Suzuki, Hiroshi; Okamoto, Yoshiwo; Nakajima, Toshiaki

2012-02-01

We evaluated whether frequency analysis could detect the development of interstitial fibrosis in rats. SHR/Izm and age-matched WKY/Izm were used. Limb lead II electrocardiograms were recorded. Continuous wavelet transform (CWT) was applied for the time-frequency analysis. The integrated time-frequency power (ITFP) between QRS complexes was measured and compared between groups. The ITFP at low-frequency bands (≤125Hz) was significantly higher in SHR/Izm. The percent change of ITFP showed the different patterns between groups. Prominent interstitial fibrosis with an increase in TIMP-1 mRNA expression was also observed in SHR/Izm. These results were partly reproduced in a computer simulation. Copyright © 2011 Elsevier Ltd. All rights reserved.

SU-E-I-100: Heterogeneity Studying for Primary and Lymphoma Tumors by Using Multi-Scale Image Texture Analysis with PET-CT Images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Dengwang; Wang, Qinfen; Li, H

Purpose: The purpose of this research is studying tumor heterogeneity of the primary and lymphoma by using multi-scale texture analysis with PET-CT images, where the tumor heterogeneity is expressed by texture features. Methods: Datasets were collected from 12 lung cancer patients, and both of primary and lymphoma tumors were detected with all these patients. All patients underwent whole-body 18F-FDG PET/CT scan before treatment.The regions of interest (ROI) of primary and lymphoma tumor were contoured by experienced clinical doctors. Then the ROI of primary and lymphoma tumor is extracted automatically by using Matlab software. According to the geometry size of contourmore » structure, the images of tumor are decomposed by multi-scale method.Wavelet transform was performed on ROI structures within images by L layers sampling, and then wavelet sub-bands which have the same size of the original image are obtained. The number of sub-bands is 3L+1.The gray level co-occurrence matrix (GLCM) is calculated within different sub-bands, thenenergy, inertia, correlation and gray in-homogeneity were extracted from GLCM.Finally, heterogeneity statistical analysis was studied for primary and lymphoma tumor using the texture features. Results: Energy, inertia, correlation and gray in-homogeneity are calculated with our experiments for heterogeneity statistical analysis.Energy for primary and lymphomatumor is equal with the same patient, while gray in-homogeneity and inertia of primaryare 2.59595±0.00855, 0.6439±0.0007 respectively. Gray in-homogeneity and inertia of lymphoma are 2.60115±0.00635, 0.64435±0.00055 respectively. The experiments showed that the volume of lymphoma is smaller than primary tumor, but thegray in-homogeneity and inertia were higher than primary tumor with the same patient, and the correlation with lymphoma tumors is zero, while the correlation with primary tumor isslightly strong. Conclusion: This studying showed that there were effective heterogeneity differences between primary and lymphoma tumor by multi-scale image texture analysis. This work is supported by National Natural Science Foundation of China (No. 61201441), Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (No. BS2012DX038), Project of Shandong Province Higher Educational Science and Technology Program (No. J12LN23), Jinan youth science and technology star (No.20120109)« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hudgins, L.H.

After a brief review of the elementary properties of Fourier Transforms, the Wavelet Transform is defined in Part I. Basic results are given for admissable wavelets. The Multiresolution Analysis, or MRA (a mathematical structure which unifies a large class of wavelets with Quadrature Mirror Filters) is then introduced. Some fundamental aspects of wavelet design are then explored. The Discrete Wavelet Transform is discussed and, in the context of an MRA, is seen to supply a Fast Wavelet Transform which competes with the Fast Fourier Transform for efficiency. In Part II, the Wavelet Transform is developed in terms of the scalemore » number variable s instead of the scale length variable a where a = 1/s. Basic results such as the admissibility condition, conservation of energy, and the reconstruction theorem are proven in this context. After reviewing some motivation for the usual Fourier power spectrum, a definition is given for the wavelet power spectrum. This `spectral density` is then intepreted in the context of spectral estimation theory. Parseval`s theorem for Wavelets then leads naturally to the Wavelet Cross Spectrum, Wavelet Cospectrum, and Wavelet Quadrature Spectrum. Wavelet Transforms are then applied in Part III to the analysis of atmospheric turbulence. Data collected over the ocean is examined in the wavelet transform domain for underlying structure. A brief overview of atmospheric turbulence is provided. Then the overall method of applying Wavelet Transform techniques to time series data is described. A trace study is included, showing some of the aspects of choosing the computational algorithm, and selection of a specific analyzing wavelet. A model for generating synthetic turbulence data is developed, and seen to yield useful results in comparing with real data for structural transitions. Results from the theory of Wavelet Spectral Estimation and Wavelength Cross-Transforms are applied to studying the momentum transport and the heat flux.« less

Wavelet transforms with discrete-time continuous-dilation wavelets

NASA Astrophysics Data System (ADS)

Zhao, Wei; Rao, Raghuveer M.

1999-03-01

Wavelet constructions and transforms have been confined principally to the continuous-time domain. Even the discrete wavelet transform implemented through multirate filter banks is based on continuous-time wavelet functions that provide orthogonal or biorthogonal decompositions. This paper provides a novel wavelet transform construction based on the definition of discrete-time wavelets that can undergo continuous parameter dilations. The result is a transformation that has the advantage of discrete-time or digital implementation while circumventing the problem of inadequate scaling resolution seen with conventional dyadic or M-channel constructions. Examples of constructing such wavelets are presented.

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.

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 manner similar to that of a baseline hyperspectral- image-compression method. The mean values are encoded in the compressed bit stream and added back to the data at the appropriate decompression step. The overhead incurred by encoding the mean values only a few bits per spectral band is negligible with respect to the huge size of a typical hyperspectral data set. The other method is denoted modified decomposition. This method is so named because it involves a modified version of a commonly used multiresolution wavelet decomposition, known in the art as the 3D Mallat decomposition, in which (a) the first of multiple stages of a 3D wavelet transform is applied to the entire dataset and (b) subsequent stages are applied only to the horizontally-, vertically-, and spectrally-low-pass subband from the preceding stage. In the modified decomposition, in stages after the first, not only is the spatially-low-pass, spectrally-low-pass subband further decomposed, but also spatially-low-pass, spectrally-high-pass subbands are further decomposed spatially. Either method can be used alone to improve the quality of a reconstructed image (see figure). Alternatively, the two methods can be combined by first performing modified decomposition, then subtracting the mean values from spatial planes of spatially-low-pass subbands.

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.

Lifting wavelet method of target detection

NASA Astrophysics Data System (ADS)

Han, Jun; Zhang, Chi; Jiang, Xu; Wang, Fang; Zhang, Jin

2009-11-01

Image target recognition plays a very important role in the areas of scientific exploration, aeronautics and space-to-ground observation, photography and topographic mapping. Complex environment of the image noise, fuzzy, all kinds of interference has always been to affect the stability of recognition algorithm. In this paper, the existence of target detection in real-time, accuracy problems, as well as anti-interference ability, using lifting wavelet image target detection methods. First of all, the use of histogram equalization, the goal difference method to obtain the region, on the basis of adaptive threshold and mathematical morphology operations to deal with the elimination of the background error. Secondly, the use of multi-channel wavelet filter wavelet transform of the original image de-noising and enhancement, to overcome the general algorithm of the noise caused by the sensitive issue of reducing the rate of miscarriage of justice will be the multi-resolution characteristics of wavelet and promotion of the framework can be designed directly in the benefits of space-time region used in target detection, feature extraction of targets. The experimental results show that the design of lifting wavelet has solved the movement of the target due to the complexity of the context of the difficulties caused by testing, which can effectively suppress noise, and improve the efficiency and speed of detection.

Denoising embolic Doppler ultrasound signals using Dual Tree Complex Discrete Wavelet Transform.

PubMed

Serbes, Gorkem; Aydin, Nizamettin

2010-01-01

Early and accurate detection of asymptomatic emboli is important for monitoring of preventive therapy in stroke-prone patients. One of the problems in detection of emboli is the identification of an embolic signal caused by very small emboli. The amplitude of the embolic signal may be so small that advanced processing methods are required to distinguish these signals from Doppler signals arising from red blood cells. In this study instead of conventional discrete wavelet transform, the Dual Tree Complex Discrete Wavelet Transform was used for denoising embolic signals. Performances of both approaches were compared. Unlike the conventional discrete wavelet transform discrete complex wavelet transform is a shift invariant transform with limited redundancy. Results demonstrate that the Dual Tree Complex Discrete Wavelet Transform based denoising outperforms conventional discrete wavelet denoising. Approximately 8 dB improvement is obtained by using the Dual Tree Complex Discrete Wavelet Transform compared to the improvement provided by the conventional Discrete Wavelet Transform (less than 5 dB).

Change Detection of Remote Sensing Images by Dt-Cwt and Mrf

NASA Astrophysics Data System (ADS)

Ouyang, S.; Fan, K.; Wang, H.; Wang, Z.

2017-05-01

Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

Application of wavelet-based multi-model Kalman filters to real-time flood forecasting

NASA Astrophysics Data System (ADS)

Chou, Chien-Ming; Wang, Ru-Yih

2004-04-01

This paper presents the application of a multimodel method using a wavelet-based Kalman filter (WKF) bank to simultaneously estimate decomposed state variables and unknown parameters for real-time flood forecasting. Applying the Haar wavelet transform alters the state vector and input vector of the state space. In this way, an overall detail plus approximation describes each new state vector and input vector, which allows the WKF to simultaneously estimate and decompose state variables. The wavelet-based multimodel Kalman filter (WMKF) is a multimodel Kalman filter (MKF), in which the Kalman filter has been substituted for a WKF. The WMKF then obtains M estimated state vectors. Next, the M state-estimates, each of which is weighted by its possibility that is also determined on-line, are combined to form an optimal estimate. Validations conducted for the Wu-Tu watershed, a small watershed in Taiwan, have demonstrated that the method is effective because of the decomposition of wavelet transform, the adaptation of the time-varying Kalman filter and the characteristics of the multimodel method. Validation results also reveal that the resulting method enhances the accuracy of the runoff prediction of the rainfall-runoff process in the Wu-Tu watershed.

Wavelet transforms as solutions of partial differential equations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zweig, G.

This is the final report of a three-year, Laboratory Directed Research and Development (LDRD) project at Los Alamos National Laboratory (LANL). Wavelet transforms are useful in representing transients whose time and frequency structure reflect the dynamics of an underlying physical system. Speech sound, pressure in turbulent fluid flow, or engine sound in automobiles are excellent candidates for wavelet analysis. This project focused on (1) methods for choosing the parent wavelet for a continuous wavelet transform in pattern recognition applications and (2) the more efficient computation of continuous wavelet transforms by understanding the relationship between discrete wavelet transforms and discretized continuousmore » wavelet transforms. The most interesting result of this research is the finding that the generalized wave equation, on which the continuous wavelet transform is based, can be used to understand phenomena that relate to the process of hearing.« less

Wavelet Based Protection Scheme for Multi Terminal Transmission System with PV and Wind Generation

NASA Astrophysics Data System (ADS)

Manju Sree, Y.; Goli, Ravi kumar; Ramaiah, V.

2017-08-01

A hybrid generation is a part of large power system in which number of sources usually attached to a power electronic converter and loads are clustered can operate independent of the main power system. The protection scheme is crucial against faults based on traditional over current protection since there are adequate problems due to fault currents in the mode of operation. This paper adopts a new approach for detection, discrimination of the faults for multi terminal transmission line protection in presence of hybrid generation. Transient current based protection scheme is developed with discrete wavelet transform. Fault indices of all phase currents at all terminals are obtained by analyzing the detail coefficients of current signals using bior 1.5 mother wavelet. This scheme is tested for different types of faults and is found effective for detection and discrimination of fault with various fault inception angle and fault impedance.

Wavelet maxima curves of surface latent heat flux associated with two recent Greek earthquakes

NASA Astrophysics Data System (ADS)

Cervone, G.; Kafatos, M.; Napoletani, D.; Singh, R. P.

2004-05-01

Multi sensor data available through remote sensing satellites provide information about changes in the state of the oceans, land and atmosphere. Recent studies have shown anomalous changes in oceans, land, atmospheric and ionospheric parameters prior to earthquakes events. This paper introduces an innovative data mining technique to identify precursory signals associated with earthquakes. The proposed methodology is a multi strategy approach which employs one dimensional wavelet transformations to identify singularities in the data, and an analysis of the continuity of the wavelet maxima in time and space to identify the singularities associated with earthquakes. The proposed methodology has been employed using Surface Latent Heat Flux (SLHF) data to study the earthquakes which occurred on 14 August 2003 and on 1 March 2004 in Greece. A single prominent SLHF anomaly has been found about two weeks prior to each of the earthquakes.

Pedestrian detection based on redundant wavelet transform

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Windowed and Wavelet Analysis of Marine Stratocumulus Cloud Inhomogeneity

NASA Technical Reports Server (NTRS)

Gollmer, Steven M.; Harshvardhan; Cahalan, Robert F.; Snider, Jack B.

1995-01-01

To improve radiative transfer calculations for inhomogeneous clouds, a consistent means of modeling inhomogeneity is needed. One current method of modeling cloud inhomogeneity is through the use of fractal parameters. This method is based on the supposition that cloud inhomogeneity over a large range of scales is related. An analysis technique named wavelet analysis provides a means of studying the multiscale nature of cloud inhomogeneity. In this paper, the authors discuss the analysis and modeling of cloud inhomogeneity through the use of wavelet analysis. Wavelet analysis as well as other windowed analysis techniques are used to study liquid water path (LWP) measurements obtained during the marine stratocumulus phase of the First ISCCP (International Satellite Cloud Climatology Project) Regional Experiment. Statistics obtained using analysis windows, which are translated to span the LWP dataset, are used to study the local (small scale) properties of the cloud field as well as their time dependence. The LWP data are transformed onto an orthogonal wavelet basis that represents the data as a number of times series. Each of these time series lies within a frequency band and has a mean frequency that is half the frequency of the previous band. Wavelet analysis combined with translated analysis windows reveals that the local standard deviation of each frequency band is correlated with the local standard deviation of the other frequency bands. The ratio between the standard deviation of adjacent frequency bands is 0.9 and remains constant with respect to time. This ratio defined as the variance coupling parameter is applicable to all of the frequency bands studied and appears to be related to the slope of the data's power spectrum. Similar analyses are performed on two cloud inhomogeneity models, which use fractal-based concepts to introduce inhomogeneity into a uniform cloud field. The bounded cascade model does this by iteratively redistributing LWP at each scale using the value of the local mean. This model is reformulated into a wavelet multiresolution framework, thereby presenting a number of variants of the bounded cascade model. One variant introduced in this paper is the 'variance coupled model,' which redistributes LWP using the local standard deviation and the variance coupling parameter. While the bounded cascade model provides an elegant two- parameter model for generating cloud inhomogeneity, the multiresolution framework provides more flexibility at the expense of model complexity. Comparisons are made with the results from the LWP data analysis to demonstrate both the strengths and weaknesses of these models.

Fusion of Modis and Palsar Principal Component Images Through Curvelet Transform for Land Cover Classification

NASA Astrophysics Data System (ADS)

Singh, Dharmendra; Kumar, Harish

Earth observation satellites provide data that covers different portions of the electromagnetic spectrum at different spatial and spectral resolutions. The increasing availability of information products generated from satellite images are extending the ability to understand the patterns and dynamics of the earth resource systems at all scales of inquiry. In which one of the most important application is the generation of land cover classification from satellite images for understanding the actual status of various land cover classes. The prospect for the use of satel-lite images in land cover classification is an extremely promising one. The quality of satellite images available for land-use mapping is improving rapidly by development of advanced sensor technology. Particularly noteworthy in this regard is the improved spatial and spectral reso-lution of the images captured by new satellite sensors like MODIS, ASTER, Landsat 7, and SPOT 5. For the full exploitation of increasingly sophisticated multisource data, fusion tech-niques are being developed. Fused images may enhance the interpretation capabilities. The images used for fusion have different temporal, and spatial resolution. Therefore, the fused image provides a more complete view of the observed objects. It is one of the main aim of image fusion to integrate different data in order to obtain more information that can be de-rived from each of the single sensor data alone. A good example of this is the fusion of images acquired by different sensors having a different spatial resolution and of different spectral res-olution. Researchers are applying the fusion technique since from three decades and propose various useful methods and techniques. The importance of high-quality synthesis of spectral information is well suited and implemented for land cover classification. More recently, an underlying multiresolution analysis employing the discrete wavelet transform has been used in image fusion. It was found that multisensor image fusion is a tradeoff between the spectral information from a low resolution multi-spectral images and the spatial information from a high resolution multi-spectral images. With the wavelet transform based fusion method, it is easy to control this tradeoff. A new transform, the curvelet transform was used in recent years by Starck. A ridgelet transform is applied to square blocks of detail frames of undecimated wavelet decomposition, consequently the curvelet transform is obtained. Since the ridgelet transform possesses basis functions matching directional straight lines therefore, the curvelet transform is capable of representing piecewise linear contours on multiple scales through few significant coefficients. This property leads to a better separation between geometric details and background noise, which may be easily reduced by thresholding curvelet coefficients before they are used for fusion. The Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instrument provides high radiometric sensitivity (12 bit) in 36 spectral bands ranging in wavelength from 0.4 m to 14.4 m and also it is freely available. Two bands are imaged at a nominal resolution of 250 m at nadir, with five bands at 500 m, and the remaining 29 bands at 1 km. In this paper, the band 1 of spatial resolution 250 m and bandwidth 620-670 nm, and band 2, of spatial resolution of 250m and bandwidth 842-876 nm is considered as these bands has special features to identify the agriculture and other land covers. In January 2006, the Advanced Land Observing Satellite (ALOS) was successfully launched by the Japan Aerospace Exploration Agency (JAXA). The Phased Arraytype L-band SAR (PALSAR) sensor onboard the satellite acquires SAR imagery at a wavelength of 23.5 cm (frequency 1.27 GHz) with capabilities of multimode and multipolarization observation. PALSAR can operate in several modes: the fine-beam single (FBS) polarization mode (HH), fine-beam dual (FBD) polariza-tion mode (HH/HV or VV/VH), polarimetric (PLR) mode (HH/HV/VH/VV), and ScanSAR (WB) mode (HH/VV) [15]. These makes PALSAR imagery very attractive for spatially and temporally consistent monitoring system. The Overview of Principal Component Analysis is that the most of the information within all the bands can be compressed into a much smaller number of bands with little loss of information. It allows us to extract the low-dimensional subspaces that capture the main linear correlation among the high-dimensional image data. This facilitates viewing the explained variance or signal in the available imagery, allowing both gross and more subtle features in the imagery to be seen. In this paper we have explored the fusion technique for enhancing the land cover classification of low resolution satellite data espe-cially freely available satellite data. For this purpose, we have considered to fuse the PALSAR principal component data with MODIS principal component data. Initially, the MODIS band 1 and band 2 is considered, its principal component is computed. Similarly the PALSAR HH, HV and VV polarized data are considered, and there principal component is also computed. con-sequently, the PALSAR principal component image is fused with MODIS principal component image. The aim of this paper is to analyze the effect of classification accuracy on major type of land cover types like agriculture, water and urban bodies with fusion of PALSAR data to MODIS data. Curvelet transformation has been applied for fusion of these two satellite images and Minimum Distance classification technique has been applied for the resultant fused image. It is qualitatively and visually observed that the overall classification accuracy of MODIS image after fusion is enhanced. This type of fusion technique may be quite helpful in near future to use freely available satellite data to develop monitoring system for different land cover classes on the earth.

Enhancement of Signal-to-noise Ratio in Natural-source Transient Magnetotelluric Data with Wavelet Transform

NASA Astrophysics Data System (ADS)

Zhang, Y.; Paulson, K. V.

For audio-frequency magnetotelluric surveys where the signals are lightning-stroke transients, the conventional Fourier transform method often fails to produce a high quality impedance tensor. An alternative approach is to use the wavelet transform method which is capable of localizing target information simultaneously in both the temporal and frequency domains. Unlike Fourier analysis that yields an average amplitude and phase, the wavelet transform produces an instantaneous estimate of the amplitude and phase of a signal. In this paper a complex well-localized wavelet, the Morlet wavelet, has been used to transform and analyze audio-frequency magnetotelluric data. With the Morlet wavelet, the magnetotelluric impedance tensor can be computed directly in the wavelet transform domain. The lightning-stroke transients are easily identified on the dilation-translation plane. Choosing those wavelet transform values where the signals are located, a higher signal-to-noise ratio estimation of the impedance tensor can be obtained. In a test using real data, the wavelet transform showed a significant improvement in the signal-to-noise ratio over the conventional Fourier transform.

Correlation analysis of motor current and chatter vibration in grinding using complex continuous wavelet coherence

NASA Astrophysics Data System (ADS)

Liu, Yao; Wang, Xiufeng; Lin, Jing; Zhao, Wei

2016-11-01

Motor current is an emerging and popular signal which can be used to detect machining chatter with its multiple advantages. To achieve accurate and reliable chatter detection using motor current, it is important to make clear the quantitative relationship between motor current and chatter vibration, which has not yet been studied clearly. In this study, complex continuous wavelet coherence, including cross wavelet transform and wavelet coherence, is applied to the correlation analysis of motor current and chatter vibration in grinding. Experimental results show that complex continuous wavelet coherence performs very well in demonstrating and quantifying the intense correlation between these two signals in frequency, amplitude and phase. When chatter occurs, clear correlations in frequency and amplitude in the chatter frequency band appear and the phase difference of current signal to vibration signal turns from random to stable. The phase lead of the most correlated chatter frequency is the largest. With the further development of chatter, the correlation grows up in intensity and expands to higher order chatter frequency band. The analyzing results confirm that there is a consistent correlation between motor current and vibration signals in the grinding chatter process. However, to achieve accurate and reliable chatter detection using motor current, the frequency response bandwidth of current loop of the feed drive system must be wide enough to response chatter effectively.

Wavelet Transforms using VTK-m

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Shaomeng; Sewell, Christopher Meyer

2016-09-27

These are a set of slides that deal with the topics of wavelet transforms using VTK-m. First, wavelets are discussed and detailed, then VTK-m is discussed and detailed, then wavelets and VTK-m are looked at from a performance comparison, then from an accuracy comparison, and finally lessons learned, conclusion, and what is next. Lessons learned are the following: Launching worklets is expensive; Natural logic of performing 2D wavelet transform: Repeat the same 1D wavelet transform on every row, repeat the same 1D wavelet transform on every column, invoke the 1D wavelet worklet every time: num_rows x num_columns; VTK-m approach ofmore » performing 2D wavelet transform: Create a worklet for 2D that handles both rows and columns, invoke this new worklet only one time; Fast calculation, but cannot reuse 1D implementations.« less

Analysis of marine multi-channel seismic data using a 2D continuous wavelet transform

NASA Astrophysics Data System (ADS)

Vuong, A. K.; Zhang, J.; Gibson, R. L.; Sager, W. W.

2011-12-01

Marine multi-channel seismic (MCS) profiles provide important constraints on crustal structure beneath the sea floor. MCS data usually provide good images of the upper part of the oceanic crust, especially in sedimentary layers. In contrast, it is often difficult to interpret deeper layers, especially those within the igneous basement, which is often nearly seismically transparent. That difference in interpretability occurs because sediments typically have continuous, well-layered and easily-traced structural features, whereas volcanic materials are characterized by smaller features with poorer lateral continuity and often with weak impedance contrasts. Since the basement tends to create weaker reflections, the signal-to-noise ratio decreases, creating additional difficulties that can be exacerbated by the presence of multiples generated by the sea floor and other sources of noise. However, it is still important to characterize the basement accurately to better understand oceanic crust formation and associated basaltic volcanism. We analyzed marine MCS data collected by R/V Marcus G. Langseth across the TAMU Massif of Shatsky Rise in the Northwest Pacific. The seismic data from this experiment display the typical problems in imaging basement features. Therefore, we seek to facilitate interpretation by applying 2-D continuous wavelet transforms to the data. Conventional Fourier methods transform 2-D seismic data from space and time domains to wavenumber and frequency, but the results are global in that there is no knowledge of temporal or spatial variations in frequency or wavenumber content. In contrast, wavelet transforms provide estimates of the local frequency and wavenumber content of the seismic image. The transform achieves this result by utilizing a localized, 2D wavelet function instead of the infinite sines and cosines applied in Fourier transforms. We utilize an anisotropic Mexican hat wavelet, where the horizontal and vertical scales are related to wavelength and period of the data, respectively. When analyzing the Shatsky Rise data set, we find, for example, that much of the noise in the seismic image of the basement is at small wavelengths corresponding to several traces, about 25 m. Using the wavelet transforms, we can extract reflection events at longer wavelengths corresponding to expected features in the subsurface. Observing reflections at a certain wavelength provides an estimate of the size scale of the associated geologic structures. The results at a frequency of 31.25 Hz, near the dominant frequency of the data, provide images of reflectors in the deep part of oceanic crust with scales from 200 m to 2000 m that are much easier to interpret than in the original seismic image. In particular, at scales from 200 m to 1000 m, we can see many reflectors with consistent with sizes and locations for localized magma intrusions into the oceanic crust. However, for spatial scales of about 2000 m, only a few reflectors are observed, suggesting there are fewer intrusions of this dimension. These features can also be examined at a range of frequencies to provide additional insights, and the wavelet transform can also be generalized to estimate dips of reflectors.

Dynamic Bayesian wavelet transform: New methodology for extraction of repetitive transients

NASA Astrophysics Data System (ADS)

Wang, Dong; Tsui, Kwok-Leung

2017-05-01

Thanks to some recent research works, dynamic Bayesian wavelet transform as new methodology for extraction of repetitive transients is proposed in this short communication to reveal fault signatures hidden in rotating machine. The main idea of the dynamic Bayesian wavelet transform is to iteratively estimate posterior parameters of wavelet transform via artificial observations and dynamic Bayesian inference. First, a prior wavelet parameter distribution can be established by one of many fast detection algorithms, such as the fast kurtogram, the improved kurtogram, the enhanced kurtogram, the sparsogram, the infogram, continuous wavelet transform, discrete wavelet transform, wavelet packets, multiwavelets, empirical wavelet transform, empirical mode decomposition, local mean decomposition, etc.. Second, artificial observations can be constructed based on one of many metrics, such as kurtosis, the sparsity measurement, entropy, approximate entropy, the smoothness index, a synthesized criterion, etc., which are able to quantify repetitive transients. Finally, given artificial observations, the prior wavelet parameter distribution can be posteriorly updated over iterations by using dynamic Bayesian inference. More importantly, the proposed new methodology can be extended to establish the optimal parameters required by many other signal processing methods for extraction of repetitive transients.

Analysis of dual tree M-band wavelet transform based features for brain image classification.

PubMed

Ayalapogu, Ratna Raju; Pabboju, Suresh; Ramisetty, Rajeswara Rao

2018-04-29

The most complex organ in the human body is the brain. The unrestrained growth of cells in the brain is called a brain tumor. The cause of a brain tumor is still unknown and the survival rate is lower than other types of cancers. Hence, early detection is very important for proper treatment. In this study, an efficient computer-aided diagnosis (CAD) system is presented for brain image classification by analyzing MRI of the brain. At first, the MRI brain images of normal and abnormal categories are modeled by using the statistical features of dual tree m-band wavelet transform (DTMBWT). A maximum margin classifier, support vector machine (SVM) is then used for the classification and validated with k-fold approach. Results show that the system provides promising results on a repository of molecular brain neoplasia data (REMBRANDT) with 97.5% accuracy using 4 th level statistical features of DTMBWT. Viewing the experimental results, we conclude that the system gives a satisfactory performance for the brain image classification. © 2018 International Society for Magnetic Resonance in Medicine.

Group theoretical methods and wavelet theory: coorbit theory and applications

NASA Astrophysics Data System (ADS)

Feichtinger, Hans G.

2013-05-01

Before the invention of orthogonal wavelet systems by Yves Meyer1 in 1986 Gabor expansions (viewed as discretized inversion of the Short-Time Fourier Transform2 using the overlap and add OLA) and (what is now perceived as) wavelet expansions have been treated more or less at an equal footing. The famous paper on painless expansions by Daubechies, Grossman and Meyer3 is a good example for this situation. The description of atomic decompositions for functions in modulation spaces4 (including the classical Sobolev spaces) given by the author5 was directly modeled according to the corresponding atomic characterizations by Frazier and Jawerth,6, 7 more or less with the idea of replacing the dyadic partitions of unity of the Fourier transform side by uniform partitions of unity (so-called BUPU's, first named as such in the early work on Wiener-type spaces by the author in 19808). Watching the literature in the subsequent two decades one can observe that the interest in wavelets "took over", because it became possible to construct orthonormal wavelet systems with compact support and of any given degree of smoothness,9 while in contrast the Balian-Low theorem is prohibiting the existence of corresponding Gabor orthonormal bases, even in the multi-dimensional case and for general symplectic lattices.10 It is an interesting historical fact that* his construction of band-limited orthonormal wavelets (the Meyer wavelet, see11) grew out of an attempt to prove the impossibility of the existence of such systems, and the final insight was that it was not impossible to have such systems, and in fact quite a variety of orthonormal wavelet system can be constructed as we know by now. Meanwhile it is established wisdom that wavelet theory and time-frequency analysis are two different ways of decomposing signals in orthogonal resp. non-orthogonal ways. The unifying theory, covering both cases, distilling from these two situations the common group theoretical background lead to the theory of coorbit spaces,12, 13 established by the author jointly with K. Gröchenig. Starting from an integrable and irreducible representation of some locally compact group (such as the "ax+b"-group or the Heisenberg group) one can derive families of Banach spaces having natural atomic characterizations, or alternatively a continuous transform associated to it. So at the end function spaces of locally compact groups come into play, and their generic properties help to explain why and how it is possible to obtain (nonorthogonal) decompositions. While unification of these two groups was one important aspect of the approach given in the late 80th, it was also clear that this approach allows to formulate and exploit the analogy to Banach spaces of analytic functions invariant under the Moebius group have been at the heart in this context. Recent years have seen further new instances and generalizations. Among them shearlets or the Blaschke product should be mentioned here, and the increased interest in the connections between wavelet theory and complex analysis. The talk will try to summarize a few of the general principles which can be derived from the general theory, but also highlight the difference between the different groups and signal expansions arising from corresponding group representations. There is still a lot more to be done, also from the point of view of applications and the numerical realization of such non-orthogonal expansions.

Polar Wavelet Transform and the Associated Uncertainty Principles

NASA Astrophysics Data System (ADS)

Shah, Firdous A.; Tantary, Azhar Y.

2018-06-01

The polar wavelet transform- a generalized form of the classical wavelet transform has been extensively used in science and engineering for finding directional representations of signals in higher dimensions. The aim of this paper is to establish new uncertainty principles associated with the polar wavelet transforms in L2(R2). Firstly, we study some basic properties of the polar wavelet transform and then derive the associated generalized version of Heisenberg-Pauli-Weyl inequality. Finally, following the idea of Beckner (Proc. Amer. Math. Soc. 123, 1897-1905 1995), we drive the logarithmic version of uncertainty principle for the polar wavelet transforms in L2(R2).

Wavelets

NASA Astrophysics Data System (ADS)

Strang, Gilbert

1994-06-01

Several methods are compared that are used to analyze and synthesize a signal. Three ways are mentioned to transform a symphony: into cosine waves (Fourier transform), into pieces of cosines (short-time Fourier transform), and into wavelets (little waves that start and stop). Choosing the best basis, higher dimensions, fast wavelet transform, and Daubechies wavelets are discussed. High-definition television is described. The use of wavelets in identifying fingerprints in the future is related.

Use of wavelet-packet transforms to develop an engineering model for multifractal characterization of mutation dynamics in pathological and nonpathological gene sequences

NASA Astrophysics Data System (ADS)

Walker, David Lee

1999-12-01

This study uses dynamical analysis to examine in a quantitative fashion the information coding mechanism in DNA sequences. This exceeds the simple dichotomy of either modeling the mechanism by comparing DNA sequence walks as Fractal Brownian Motion (fbm) processes. The 2-D mappings of the DNA sequences for this research are from Iterated Function System (IFS) (Also known as the ``Chaos Game Representation'' (CGR)) mappings of the DNA sequences. This technique converts a 1-D sequence into a 2-D representation that preserves subsequence structure and provides a visual representation. The second step of this analysis involves the application of Wavelet Packet Transforms, a recently developed technique from the field of signal processing. A multi-fractal model is built by using wavelet transforms to estimate the Hurst exponent, H. The Hurst exponent is a non-parametric measurement of the dynamism of a system. This procedure is used to evaluate gene- coding events in the DNA sequence of cystic fibrosis mutations. The H exponent is calculated for various mutation sites in this gene. The results of this study indicate the presence of anti-persistent, random walks and persistent ``sub-periods'' in the sequence. This indicates the hypothesis of a multi-fractal model of DNA information encoding warrants further consideration. This work examines the model's behavior in both pathological (mutations) and non-pathological (healthy) base pair sequences of the cystic fibrosis gene. These mutations both natural and synthetic were introduced by computer manipulation of the original base pair text files. The results show that disease severity and system ``information dynamics'' correlate. These results have implications for genetic engineering as well as in mathematical biology. They suggest that there is scope for more multi-fractal models to be developed.

Image sharpness assessment based on wavelet energy of edge area

NASA Astrophysics Data System (ADS)

Li, Jin; Zhang, Hong; Zhang, Lei; Yang, Yifan; He, Lei; Sun, Mingui

2018-04-01

Image quality assessment is needed in multiple image processing areas and blur is one of the key reasons of image deterioration. Although great full-reference image quality assessment metrics have been proposed in the past few years, no-reference method is still an area of current research. Facing this problem, this paper proposes a no-reference sharpness assessment method based on wavelet transformation which focuses on the edge area of image. Based on two simple characteristics of human vision system, weights are introduced to calculate weighted log-energy of each wavelet sub band. The final score is given by the ratio of high-frequency energy to the total energy. The algorithm is tested on multiple databases. Comparing with several state-of-the-art metrics, proposed algorithm has better performance and less runtime consumption.

Wavelet transform: fundamentals, applications, and implementation using acousto-optic correlators

NASA Astrophysics Data System (ADS)

DeCusatis, Casimer M.; Koay, J.; Litynski, Daniel M.; Das, Pankaj K.

1995-10-01

In recent years there has been a great deal of interest in the use of wavelets to supplement or replace conventional Fourier transform signal processing. This paper provides a review of wavelet transforms for signal processing applications, and discusses several emerging applications which benefit from the advantages of wavelets. The wavelet transform can be implemented as an acousto-optic correlator; perfect reconstruction of digital signals may also be achieved using acousto-optic finite impulse response filter banks. Acousto-optic image correlators are discussed as a potential implementation of the wavelet transform, since a 1D wavelet filter bank may be encoded as a 2D image. We discuss applications of the wavelet transform including nondestructive testing of materials, biomedical applications in the analysis of EEG signals, and interference excision in spread spectrum communication systems. Computer simulations and experimental results for these applications are also provided.

Improving multispectral satellite image compression using onboard subpixel registration

NASA Astrophysics Data System (ADS)

Albinet, Mathieu; Camarero, Roberto; Isnard, Maxime; Poulet, Christophe; Perret, Jokin

2013-09-01

Future CNES earth observation missions will have to deal with an ever increasing telemetry data rate due to improvements in resolution and addition of spectral bands. Current CNES image compressors implement a discrete wavelet transform (DWT) followed by a bit plane encoding (BPE) but only on a mono spectral basis and do not profit from the multispectral redundancy of the observed scenes. Recent CNES studies have proven a substantial gain on the achievable compression ratio, +20% to +40% on selected scenarios, by implementing a multispectral compression scheme based on a Karhunen Loeve transform (KLT) followed by the classical DWT+BPE. But such results can be achieved only on perfectly registered bands; a default of registration as low as 0.5 pixel ruins all the benefits of multispectral compression. In this work, we first study the possibility to implement a multi-bands subpixel onboard registration based on registration grids generated on-the-fly by the satellite attitude control system and simplified resampling and interpolation techniques. Indeed bands registration is usually performed on ground using sophisticated techniques too computationally intensive for onboard use. This fully quantized algorithm is tuned to meet acceptable registration performances within stringent image quality criteria, with the objective of onboard real-time processing. In a second part, we describe a FPGA implementation developed to evaluate the design complexity and, by extrapolation, the data rate achievable on a spacequalified ASIC. Finally, we present the impact of this approach on the processing chain not only onboard but also on ground and the impacts on the design of the instrument.

Analysis on Behaviour of Wavelet Coefficient during Fault Occurrence in Transformer

NASA Astrophysics Data System (ADS)

Sreewirote, Bancha; Ngaopitakkul, Atthapol

2018-03-01

The protection system for transformer has play significant role in avoiding severe damage to equipment when disturbance occur and ensure overall system reliability. One of the methodology that widely used in protection scheme and algorithm is discrete wavelet transform. However, characteristic of coefficient under fault condition must be analyzed to ensure its effectiveness. So, this paper proposed study and analysis on wavelet coefficient characteristic when fault occur in transformer in both high- and low-frequency component from discrete wavelet transform. The effect of internal and external fault on wavelet coefficient of both fault and normal phase has been taken into consideration. The fault signal has been simulate using transmission connected to transformer experimental setup on laboratory level that modelled after actual system. The result in term of wavelet coefficient shown a clearly differentiate between wavelet characteristic in both high and low frequency component that can be used to further design and improve detection and classification algorithm that based on discrete wavelet transform methodology in the future.

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. Copyright © 2013 Elsevier Ltd. All rights reserved.

Double Density Dual Tree Discrete Wavelet Transform implementation for Degraded Image Enhancement

NASA Astrophysics Data System (ADS)

Vimala, C.; Aruna Priya, P.

2018-04-01

Wavelet transform is a main tool for image processing applications in modern existence. A Double Density Dual Tree Discrete Wavelet Transform is used and investigated for image denoising. Images are considered for the analysis and the performance is compared with discrete wavelet transform and the Double Density DWT. Peak Signal to Noise Ratio values and Root Means Square error are calculated in all the three wavelet techniques for denoised images and the performance has evaluated. The proposed techniques give the better performance when comparing other two wavelet techniques.

OBS Data Denoising Based on Compressed Sensing Using Fast Discrete Curvelet Transform

NASA Astrophysics Data System (ADS)

Nan, F.; Xu, Y.

2017-12-01

OBS (Ocean Bottom Seismometer) data denoising is an important step of OBS data processing and inversion. It is necessary to get clearer seismic phases for further velocity structure analysis. Traditional methods for OBS data denoising include band-pass filter, Wiener filter and deconvolution etc. (Liu, 2015). Most of these filtering methods are based on Fourier Transform (FT). Recently, the multi-scale transform methods such as wavelet transform (WT) and Curvelet transform (CvT) are widely used for data denoising in various applications. The FT, WT and CvT could represent signal sparsely and separate noise in transform domain. They could be used in different cases. Compared with Curvelet transform, the FT has Gibbs phenomenon and it cannot handle points discontinuities well. WT is well localized and multi scale, but it has poor orientation selectivity and could not handle curves discontinuities well. CvT is a multiscale directional transform that could represent curves with only a small number of coefficients. It provide an optimal sparse representation of objects with singularities along smooth curves, which is suitable for seismic data processing. As we know, different seismic phases in OBS data are showed as discontinuous curves in time domain. Hence, we promote to analysis the OBS data via CvT and separate the noise in CvT domain. In this paper, our sparsity-promoting inversion approach is restrained by L1 condition and we solve this L1 problem by using modified iteration thresholding. Results show that the proposed method could suppress the noise well and give sparse results in Curvelet domain. Figure 1 compares the Curvelet denoising method with Wavelet method on the same iterations and threshold through synthetic example. a)Original data. b) Add-noise data. c) Denoised data using CvT. d) Denoised data using WT. The CvT can well eliminate the noise and has better result than WT. Further we applied the CvT denoise method for the OBS data processing. Figure 2a is a common receiver gather collected in the Bohai Sea, China. The whole profile is 120km long with 987 shots. The horizontal axis is shot number. The vertical axis is travel time reduced by 6km/s. We use our method to process the data and get a denoised profile figure 2b. After denoising, most of the high frequency noise was suppressed and the seismic phases were clearer.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering.

PubMed

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-09-10

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

PubMed Central

Kang, Wonseok; Yu, Soohwan; Seo, Doochun; Jeong, Jaeheon; Paik, Joonki

2015-01-01

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments. PMID:26378532

Adaptive Filtering in the Wavelet Transform Domain via Genetic Algorithms

DTIC Science & Technology

2004-08-06

wavelet transforms. Whereas the term “evolved” pertains only to the altered wavelet coefficients used during the inverse transform process. 2...words, the inverse transform produces the original signal x(t) from the wavelet and scaling coefficients. )()( ,, tdtx nk n nk k ψ...reconstruct the original signal as accurately as possible. The inverse transform reconstructs an approximation of the original signal (Burrus

Wavelet denoising during optical coherence tomography of the prostate nerves using the complex wavelet transform.

PubMed

Chitchian, Shahab; Fiddy, Michael; Fried, Nathaniel M

2008-01-01

Preservation of the cavernous nerves during prostate cancer surgery is critical in preserving sexual function after surgery. Optical coherence tomography (OCT) of the prostate nerves has recently been studied for potential use in nerve-sparing prostate surgery. In this study, the discrete wavelet transform and complex dual-tree wavelet transform are implemented for wavelet shrinkage denoising in OCT images of the rat prostate. Applying the complex dual-tree wavelet transform provides improved results for speckle noise reduction in the OCT prostate image. Image quality metrics of the cavernous nerves and signal-to-noise ratio (SNR) were improved significantly using this complex wavelet denoising technique.

Combining a wavelet transform with a channelized Hotelling observer for tumor detection in 3D PET oncology imaging

NASA Astrophysics Data System (ADS)

Lartizien, Carole; Tomei, Sandrine; Maxim, Voichita; Odet, Christophe

2007-03-01

This study evaluates new observer models for 3D whole-body Positron Emission Tomography (PET) imaging based on a wavelet sub-band decomposition and compares them with the classical constant-Q CHO model. Our final goal is to develop an original method that performs guided detection of abnormal activity foci in PET oncology imaging based on these new observer models. This computer-aided diagnostic method would highly benefit to clinicians for diagnostic purpose and to biologists for massive screening of rodents populations in molecular imaging. Method: We have previously shown good correlation of the channelized Hotelling observer (CHO) using a constant-Q model with human observer performance for 3D PET oncology imaging. We propose an alternate method based on combining a CHO observer with a wavelet sub-band decomposition of the image and we compare it to the standard CHO implementation. This method performs an undecimated transform using a biorthogonal B-spline 4/4 wavelet basis to extract the features set for input to the Hotelling observer. This work is based on simulated 3D PET images of an extended MCAT phantom with randomly located lesions. We compare three evaluation criteria: classification performance using the signal-to-noise ratio (SNR), computation efficiency and visual quality of the derived 3D maps of the decision variable λ. The SNR is estimated on a series of test images for a variable number of training images for both observers. Results: Results show that the maximum SNR is higher with the constant-Q CHO observer, especially for targets located in the liver, and that it is reached with a smaller number of training images. However, preliminary analysis indicates that the visual quality of the 3D maps of the decision variable λ is higher with the wavelet-based CHO and the computation time to derive a 3D λ-map is about 350 times shorter than for the standard CHO. This suggests that the wavelet-CHO observer is a good candidate for use in our guided detection method.

Experimental phase synchronization detection in non-phase coherent chaotic systems by using the discrete complex wavelet approach

NASA Astrophysics Data System (ADS)

Ferreira, Maria Teodora; Follmann, Rosangela; Domingues, Margarete O.; Macau, Elbert E. N.; Kiss, István Z.

2017-08-01

Phase synchronization may emerge from mutually interacting non-linear oscillators, even under weak coupling, when phase differences are bounded, while amplitudes remain uncorrelated. However, the detection of this phenomenon can be a challenging problem to tackle. In this work, we apply the Discrete Complex Wavelet Approach (DCWA) for phase assignment, considering signals from coupled chaotic systems and experimental data. The DCWA is based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is a discrete transformation. Due to its multi-scale properties in the context of phase characterization, it is possible to obtain very good results from scalar time series, even with non-phase-coherent chaotic systems without state space reconstruction or pre-processing. The method correctly predicts the phase synchronization for a chemical experiment with three locally coupled, non-phase-coherent chaotic processes. The impact of different time-scales is demonstrated on the synchronization process that outlines the advantages of DCWA for analysis of experimental data.

Applying wavelet transforms to analyse aircraft-measured turbulence and turbulent fluxes in the atmospheric boundary layer over eastern Siberia

NASA Astrophysics Data System (ADS)

Strunin, M. A.; Hiyama, T.

2004-11-01

The wavelet spectral method was applied to aircraft-based measurements of atmospheric turbulence obtained during joint Russian-Japanese research on the atmospheric boundary layer near Yakutsk (eastern Siberia) in April-June 2000. Practical ways to apply Fourier and wavelet methods for aircraft-based turbulence data are described. Comparisons between Fourier and wavelet transform results are shown and they demonstrate, in conjunction with theoretical and experimental restrictions, that the Fourier transform method is not useful for studying non-homogeneous turbulence. The wavelet method is free from many disadvantages of Fourier analysis and can yield more informative results. Comparison of Fourier and Morlet wavelet spectra showed good agreement at high frequencies (small scales). The quality of the wavelet transform and corresponding software was estimated by comparing the original data with restored data constructed with an inverse wavelet transform. A Haar wavelet basis was inappropriate for the turbulence data; the mother wavelet function recommended in this study is the Morlet wavelet. Good agreement was also shown between variances and covariances estimated with different mathematical techniques, i.e. through non-orthogonal wavelet spectra and through eddy correlation methods.

Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting.

PubMed

Capizzi, Giacomo; Napoli, Christian; Bonanno, Francesco

2012-11-01

Solar radiation prediction is an important challenge for the electrical engineer because it is used to estimate the power developed by commercial photovoltaic modules. This paper deals with the problem of solar radiation prediction based on observed meteorological data. A 2-day forecast is obtained by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS are used to exploit the correlation between solar radiation and timescale-related variations of wind speed, humidity, and temperature. The input to the selected WRNN is provided by timescale-related bands of wavelet coefficients obtained from meteorological time series. The experimental setup available at the University of Catania, Italy, provided this information. The novelty of this approach is that the proposed WRNN performs the prediction in the wavelet domain and, in addition, also performs the inverse wavelet transform, giving the predicted signal as output. The obtained simulation results show a very low root-mean-square error compared to the results of the solar radiation prediction approaches obtained by hybrid neural networks reported in the recent literature.

BOOK REVIEW: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance

NASA Astrophysics Data System (ADS)

Ng, J.; Kingsbury, N. G.

2004-02-01

This book provides an overview of the theory and practice of continuous and discrete wavelet transforms. Divided into seven chapters, the first three chapters of the book are introductory, describing the various forms of the wavelet transform and their computation, while the remaining chapters are devoted to applications in fluids, engineering, medicine and miscellaneous areas. Each chapter is well introduced, with suitable examples to demonstrate key concepts. Illustrations are included where appropriate, thus adding a visual dimension to the text. A noteworthy feature is the inclusion, at the end of each chapter, of a list of further resources from the academic literature which the interested reader can consult. The first chapter is purely an introduction to the text. The treatment of wavelet transforms begins in the second chapter, with the definition of what a wavelet is. The chapter continues by defining the continuous wavelet transform and its inverse and a description of how it may be used to interrogate signals. The continuous wavelet transform is then compared to the short-time Fourier transform. Energy and power spectra with respect to scale are also discussed and linked to their frequency counterparts. Towards the end of the chapter, the two-dimensional continuous wavelet transform is introduced. Examples of how the continuous wavelet transform is computed using the Mexican hat and Morlet wavelets are provided throughout. The third chapter introduces the discrete wavelet transform, with its distinction from the discretized continuous wavelet transform having been made clear at the end of the second chapter. In the first half of the chapter, the logarithmic discretization of the wavelet function is described, leading to a discussion of dyadic grid scaling, frames, orthogonal and orthonormal bases, scaling functions and multiresolution representation. The fast wavelet transform is introduced and its computation is illustrated with an example using the Haar wavelet. The second half of the chapter groups together miscellaneous points about the discrete wavelet transform, including coefficient manipulation for signal denoising and smoothing, a description of Daubechies’ wavelets, the properties of translation invariance and biorthogonality, the two-dimensional discrete wavelet transforms and wavelet packets. The fourth chapter is dedicated to wavelet transform methods in the author’s own specialty, fluid mechanics. Beginning with a definition of wavelet-based statistical measures for turbulence, the text proceeds to describe wavelet thresholding in the analysis of fluid flows. The remainder of the chapter describes wavelet analysis of engineering flows, in particular jets, wakes, turbulence and coherent structures, and geophysical flows, including atmospheric and oceanic processes. The fifth chapter describes the application of wavelet methods in various branches of engineering, including machining, materials, dynamics and information engineering. Unlike previous chapters, this (and subsequent) chapters are styled more as literature reviews that describe the findings of other authors. The areas addressed in this chapter include: the monitoring of machining processes, the monitoring of rotating machinery, dynamical systems, chaotic systems, non-destructive testing, surface characterization and data compression. The sixth chapter continues in this vein with the attention now turned to wavelets in the analysis of medical signals. Most of the chapter is devoted to the analysis of one-dimensional signals (electrocardiogram, neural waveforms, acoustic signals etc.), although there is a small section on the analysis of two-dimensional medical images. The seventh and final chapter of the book focuses on the application of wavelets in three seemingly unrelated application areas: fractals, finance and geophysics. The treatment on wavelet methods in fractals focuses on stochastic fractals with a short section on multifractals. The treatment on finance touches on the use of wavelets by other authors in studying stock prices, commodity behaviour, market dynamics and foreign exchange rates. The treatment on geophysics covers what was omitted from the fourth chapter, namely, seismology, well logging, topographic feature analysis and the analysis of climatic data. The text concludes with an assortment of other application areas which could only be mentioned in passing. Unlike most other publications in the subject, this book does not treat wavelet transforms in a mathematically rigorous manner but rather aims to explain the mechanics of the wavelet transform in a way that is easy to understand. Consequently, it serves as an excellent overview of the subject rather than as a reference text. Keeping the mathematics to a minimum and omitting cumbersome and detailed proofs from the text, the book is best-suited to those who are new to wavelets or who want an intuitive understanding of the subject. Such an audience may include graduate students in engineering and professionals and researchers in engineering and the applied sciences.

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.

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions.

PubMed

Hu, Yue; Tu, Xiaotong; Li, Fucai; Meng, Guang

2018-01-07

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults.

Joint High-Order Synchrosqueezing Transform and Multi-Taper Empirical Wavelet Transform for Fault Diagnosis of Wind Turbine Planetary Gearbox under Nonstationary Conditions

PubMed Central

Li, Fucai; Meng, Guang

2018-01-01

Wind turbines usually operate under nonstationary conditions, such as wide-range speed fluctuation and time-varying load. Its critical component, the planetary gearbox, is prone to malfunction or failure, which leads to downtime and repair costs. Therefore, fault diagnosis and condition monitoring for the planetary gearbox in wind turbines is a vital research topic. Meanwhile, the signals measured by the vibration sensors mounted in the gearbox exhibit time-varying and nonstationary features. In this study, a novel time-frequency method based on high-order synchrosqueezing transform (SST) and multi-taper empirical wavelet transform (MTEWT) is proposed for the wind turbine planetary gearbox under nonstationary conditions. The high-order SST uses accurate instantaneous frequency approximations to obtain a sharper time-frequency representation (TFR). As the acquired signal consists of many components, like the meshing and rotating components of the gear and bearing, the fault component may be masked by other unrelated components. The MTEWT is used to separate the fault feature from the masking components. A variety of experimental signals of the wind turbine planetary gearbox under nonstationary conditions have been analyzed to demonstrate the effectiveness and robustness of the proposed method. Results show that the proposed method is effective in diagnosing both gear and bearing faults. PMID:29316668

Parallel object-oriented, denoising system using wavelet multiresolution analysis

DOEpatents

Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.; Tang, Nu A.

2005-04-12

The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.

Image-adaptive and robust digital wavelet-domain watermarking for images

NASA Astrophysics Data System (ADS)

Zhao, Yi; Zhang, Liping

2018-03-01

We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.

Development of 3D electromagnetic modeling tools for airborne vehicles

NASA Technical Reports Server (NTRS)

Volakis, John L.

1992-01-01

The main goal of this report is to advance the development of methodologies for scattering by airborne composite vehicles. Although the primary focus continues to be the development of a general purpose computer code for analyzing the entire structure as a single unit, a number of other tasks are also being pursued in parallel with this effort. One of these tasks discussed within is on new finite element formulations and mesh termination schemes. The goal here is to decrease computation time while retaining accuracy and geometric adaptability.The second task focuses on the application of wavelets to electromagnetics. Wavelet transformations are shown to be able to reduce a full matrix to a band matrix, thereby reducing the solutions memory requirements. Included within this document are two separate papers on finite element formulations and wavelets.

Image fusion based on millimeter-wave for concealed weapon detection

NASA Astrophysics Data System (ADS)

Zhu, Weiwen; Zhao, Yuejin; Deng, Chao; Zhang, Cunlin; Zhang, Yalin; Zhang, Jingshui

2010-11-01

This paper describes a novel multi sensors image fusion technology which is presented for concealed weapon detection (CWD). It is known to all, because of the good transparency of the clothes at millimeter wave band, a millimeter wave radiometer can be used to image and distinguish concealed contraband beneath clothes, for example guns, knives, detonator and so on. As a result, we adopt the passive millimeter wave (PMMW) imaging technology for airport security. However, in consideration of the wavelength of millimeter wave and the single channel mechanical scanning, the millimeter wave image has law optical resolution, which can't meet the need of practical application. Therefore, visible image (VI), which has higher resolution, is proposed for the image fusion with the millimeter wave image to enhance the readability. Before the image fusion, a novel image pre-processing which specifics to the fusion of millimeter wave imaging and visible image is adopted. And in the process of image fusion, multi resolution analysis (MRA) based on Wavelet Transform (WT) is adopted. In this way, the experiment result shows that this method has advantages in concealed weapon detection and has practical significance.

Investigation of multidimensional control systems in the state space and wavelet medium

NASA Astrophysics Data System (ADS)

Fedosenkov, D. B.; Simikova, A. A.; Fedosenkov, B. A.

2018-05-01

The notions are introduced of “one-dimensional-point” and “multidimensional-point” automatic control systems. To demonstrate the joint use of approaches based on the concepts of state space and wavelet transforms, a method for optimal control in a state space medium represented in the form of time-frequency representations (maps), is considered. The computer-aided control system is formed on the basis of the similarity transformation method, which makes it possible to exclude the use of reduced state variable observers. 1D-material flow signals formed by primary transducers are converted by means of wavelet transformations into multidimensional concentrated-at-a point variables in the form of time-frequency distributions of Cohen’s class. The algorithm for synthesizing a stationary controller for feeding processes is given here. The conclusion is made that the formation of an optimal control law with time-frequency distributions available contributes to the improvement of transient processes quality in feeding subsystems and the mixing unit. Confirming the efficiency of the method presented is illustrated by an example of the current registration of material flows in the multi-feeding unit. The first section in your paper.

Multidimensional, mapping-based complex wavelet transforms.

PubMed

Fernandes, Felix C A; van Spaendonck, Rutger L C; Burrus, C Sidney

2005-01-01

Although the discrete wavelet transform (DWT) is a powerful tool for signal and image processing, it has three serious disadvantages: shift sensitivity, poor directionality, and lack of phase information. To overcome these disadvantages, we introduce multidimensional, mapping-based, complex wavelet transforms that consist of a mapping onto a complex function space followed by a DWT of the complex mapping. Unlike other popular transforms that also mitigate DWT shortcomings, the decoupled implementation of our transforms has two important advantages. First, the controllable redundancy of the mapping stage offers a balance between degree of shift sensitivity and transform redundancy. This allows us to create a directional, nonredundant, complex wavelet transform with potential benefits for image coding systems. To the best of our knowledge, no other complex wavelet transform is simultaneously directional and nonredundant. The second advantage of our approach is the flexibility to use any DWT in the transform implementation. As an example, we exploit this flexibility to create the complex double-density DWT: a shift-insensitive, directional, complex wavelet transform with a low redundancy of (3M - 1)/(2M - 1) in M dimensions. No other transform achieves all these properties at a lower redundancy, to the best of our knowledge. By exploiting the advantages of our multidimensional, mapping-based complex wavelet transforms in seismic signal-processing applications, we have demonstrated state-of-the-art results.

Application of wavelet analysis to estimation of parameters of the gravitational-wave signal from a coalescing binary

NASA Astrophysics Data System (ADS)

Królak, Andrzej; Trzaskoma, Pawel

1996-05-01

Application of wavelet analysis to the estimation of parameters of the broad-band gravitational-wave signal emitted by a binary system is investigated. A method of instantaneous frequency extraction first proposed in this context by Innocent and Vinet is used. The gravitational-wave signal from a binary is investigated from the point of view of signal analysis theory and it is shown that such a signal is characterized by a large time - bandwidth product. This property enables the extraction of frequency modulation from the wavelet transform of the signal. The wavelet transform of the chirp signal from a binary is calculated analytically. Numerical simulations with the noisy chirp signal are performed. The gravitational-wave signal from a binary is taken in the quadrupole approximation and it is buried in noise corresponding to three different values of the signal-to-noise ratio and the wavelet method to extract the frequency modulation of the signal is applied. Then, from the frequency modulation, the chirp mass parameter of the binary is estimated. It is found that the chirp mass can be estimated to a good accuracy, typically of the order of (20/0264-9381/13/5/006/img5% where 0264-9381/13/5/006/img6 is the optimal signal-to-noise ratio. It is also shown that the post-Newtonian effects in the gravitational wave signal from a binary can be discriminated to a satisfactory accuracy.

A new time-frequency method for identification and classification of ball bearing faults

NASA Astrophysics Data System (ADS)

Attoui, Issam; Fergani, Nadir; Boutasseta, Nadir; Oudjani, Brahim; Deliou, Adel

2017-06-01

In order to fault diagnosis of ball bearing that is one of the most critical components of rotating machinery, this paper presents a time-frequency procedure incorporating a new feature extraction step that combines the classical wavelet packet decomposition energy distribution technique and a new feature extraction technique based on the selection of the most impulsive frequency bands. In the proposed procedure, firstly, as a pre-processing step, the most impulsive frequency bands are selected at different bearing conditions using a combination between Fast-Fourier-Transform FFT and Short-Frequency Energy SFE algorithms. Secondly, once the most impulsive frequency bands are selected, the measured machinery vibration signals are decomposed into different frequency sub-bands by using discrete Wavelet Packet Decomposition WPD technique to maximize the detection of their frequency contents and subsequently the most useful sub-bands are represented in the time-frequency domain by using Short Time Fourier transform STFT algorithm for knowing exactly what the frequency components presented in those frequency sub-bands are. Once the proposed feature vector is obtained, three feature dimensionality reduction techniques are employed using Linear Discriminant Analysis LDA, a feedback wrapper method and Locality Sensitive Discriminant Analysis LSDA. Lastly, the Adaptive Neuro-Fuzzy Inference System ANFIS algorithm is used for instantaneous identification and classification of bearing faults. In order to evaluate the performances of the proposed method, different testing data set to the trained ANFIS model by using different conditions of healthy and faulty bearings under various load levels, fault severities and rotating speed. The conclusion resulting from this paper is highlighted by experimental results which prove that the proposed method can serve as an intelligent bearing fault diagnosis system.

A Novel Analysis Of The Connection Between Indian Monsoon Rainfall And Solar Activity

NASA Astrophysics Data System (ADS)

Bhattacharyya, S.; Narasimha, R.

2005-12-01

The existence of possible correlations between the solar cycle period as extracted from the yearly means of sunspot numbers and any periodicities that may be present in the Indian monsoon rainfall has been addressed using wavelet analysis. The wavelet transform coefficient maps of sunspot-number time series and those of the homogeneous Indian monsoon rainfall annual time series data reveal striking similarities, especially around the 11-year period. A novel method to analyse and quantify this similarity devising statistical schemes is suggested in this paper. The wavelet transform coefficient maxima at the 11-year period for the sunspot numbers and the monsoon rainfall have each been modelled as a point process in time and a statistical scheme for identifying a trend or dependence between the two processes has been devised. A regression analysis of parameters in these processes reveals a nearly linear trend with small but systematic deviations from the regressed line. Suitable function models for these deviations have been obtained through an unconstrained error minimisation scheme. These models provide an excellent fit to the time series of the given wavelet transform coefficient maxima obtained from actual data. Statistical significance tests on these deviations suggest with 99% confidence that the deviations are sample fluctuations obtained from normal distributions. In fact our earlier studies (see, Bhattacharyya and Narasimha, 2005, Geophys. Res. Lett., Vol. 32, No. 5) revealed that average rainfall is higher during periods of greater solar activity for all cases, at confidence levels varying from 75% to 99%, being 95% or greater in 3 out of 7 of them. Analysis using standard wavelet techniques reveals higher power in the 8--16 y band during the higher solar activity period, in 6 of the 7 rainfall time series, at confidence levels exceeding 99.99%. Furthermore, a comparison between the wavelet cross spectra of solar activity with rainfall and noise (including those simulating the rainfall spectrum and probability distribution) revealed that over the two test-periods respectively of high and low solar activity, the average cross power of the solar activity index with rainfall exceeds that with the noise at z-test confidence levels exceeding 99.99% over period-bands covering the 11.6 y sunspot cycle (see, Bhattacharyya and Narasimha, SORCE 2005 14-16th September, at Durango, Colorado USA). These results provide strong evidence for connections between Indian rainfall and solar activity. The present study reveals in addition the presence of subharmonics of the solar cycle period in the monsoon rainfall time series together with information on their phase relationships.

Comparisons between real and complex Gauss wavelet transform methods of three-dimensional shape reconstruction

NASA Astrophysics Data System (ADS)

Xu, Luopeng; Dan, Youquan; Wang, Qingyuan

2015-10-01

The continuous wavelet transform (CWT) introduces an expandable spatial and frequency window which can overcome the inferiority of localization characteristic in Fourier transform and windowed Fourier transform. The CWT method is widely applied in the non-stationary signal analysis field including optical 3D shape reconstruction with remarkable performance. In optical 3D surface measurement, the performance of CWT for optical fringe pattern phase reconstruction usually depends on the choice of wavelet function. A large kind of wavelet functions of CWT, such as Mexican Hat wavelet, Morlet wavelet, DOG wavelet, Gabor wavelet and so on, can be generated from Gauss wavelet function. However, so far, application of the Gauss wavelet transform (GWT) method (i.e. CWT with Gauss wavelet function) in optical profilometry is few reported. In this paper, the method using GWT for optical fringe pattern phase reconstruction is presented first and the comparisons between real and complex GWT methods are discussed in detail. The examples of numerical simulations are also given and analyzed. The results show that both the real GWT method along with a Hilbert transform and the complex GWT method can realize three-dimensional surface reconstruction; and the performance of reconstruction generally depends on the frequency domain appearance of Gauss wavelet functions. For the case of optical fringe pattern of large phase variation with position, the performance of real GWT is better than that of complex one due to complex Gauss series wavelets existing frequency sidelobes. Finally, the experiments are carried out and the experimental results agree well with our theoretical analysis.

The wavelet transform as an analysis tool for structure identification in molecular clouds

NASA Astrophysics Data System (ADS)

Gill, Arnold Gerald

1993-01-01

Of the many methods used to attempt to understand the complex structure of giant molecular clouds, perhaps the most commonly used are the autocorrelation functions (ACF), the structure function, and the power spectrum. However, these do not give unique interpretations of structure, as is shown by explicit examples compared to the Taurus Molecular Complex. Thus, another, independent method of analysis is indicated. Here, the wavelet transform is presented, a relatively new technique less than 10 years old. It can be thought of as a band-pass filter that identifies structures of specific sizes. In addition, its mathematical properties allow it to be used to identify fractal structures and accurately identify the scaling exponent. This is shown by the wavelet transform identifying the fractal dimension of a hierarchical rain cloud model first proposed by Frisch et al. (1978). A wavelet analysis is then carried out for a range of astronomical CO data, including the clouds Orion A and B and NGC 7538 (in (12)CO) and Orion A and B, Mon R2, and L1551 (in (13)CO). The data analyzed consists of the velocities of the fitted Gaussians to the individual spectra, the halfwidths and amplitude of these Gaussians, and the total area of the spectral line. For most of the clouds investigated, each of these data types showed a very high degree of scaling coherence over a wide range of scales, from down at the beam spacing up to the full size of the cloud. The analysis carried out uses both the scaling and structure identification strengths of the wavelet transform The fragmentation parameters used by Scalo (1985) and the parameters of the geometric molecular cloud description introduced by Henriksen (1986) are calculated for each cloud. These results are all consistent with previous observations of these and other molecular clouds, though they are obtained individually for each cloud investigated. It is found that the uncertainties are of a magnitude that the differentiation of various theories of molecular cloud structure is not possible. It is noted that the effects of projection and superposition strongly affect the values of some of these parameters, thus hampering a thorough understanding of the underlying physics. The strengths and weaknesses of the wavelet transform in the analysis of molecular cloud data are presented, as well as directions for future work.

Efficacy Evaluation of Different Wavelet Feature Extraction Methods on Brain MRI Tumor Detection

NASA Astrophysics Data System (ADS)

Nabizadeh, Nooshin; John, Nigel; Kubat, Miroslav

2014-03-01

Automated Magnetic Resonance Imaging brain tumor detection and segmentation is a challenging task. Among different available methods, feature-based methods are very dominant. While many feature extraction techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present the results of a study in which we evaluate the efficiency of using different wavelet transform features extraction methods in brain MRI abnormality detection. Applying T1-weighted brain image, Discrete Wavelet Transform (DWT), Discrete Wavelet Packet Transform (DWPT), Dual Tree Complex Wavelet Transform (DTCWT), and Complex Morlet Wavelet Transform (CMWT) methods are applied to construct the feature pool. Three various classifiers as Support Vector Machine, K Nearest Neighborhood, and Sparse Representation-Based Classifier are applied and compared for classifying the selected features. The results show that DTCWT and CMWT features classified with SVM, result in the highest classification accuracy, proving of capability of wavelet transform features to be informative in this application.

Coherent-subspace array processing based on wavelet covariance: an application to broad-band, seismo-volcanic signals

NASA Astrophysics Data System (ADS)

Saccorotti, G.; Nisii, V.; Del Pezzo, E.

2008-07-01

Long-Period (LP) and Very-Long-Period (VLP) signals are the most characteristic seismic signature of volcano dynamics, and provide important information about the physical processes occurring in magmatic and hydrothermal systems. These events are usually characterized by sharp spectral peaks, which may span several frequency decades, by emergent onsets, and by a lack of clear S-wave arrivals. These two latter features make both signal detection and location a challenging task. In this paper, we propose a processing procedure based on Continuous Wavelet Transform of multichannel, broad-band data to simultaneously solve the signal detection and location problems. Our method consists of two steps. First, we apply a frequency-dependent threshold to the estimates of the array-averaged WCO in order to locate the time-frequency regions spanned by coherent arrivals. For these data, we then use the time-series of the complex wavelet coefficients for deriving the elements of the spatial Cross-Spectral Matrix. From the eigenstructure of this matrix, we eventually estimate the kinematic signals' parameters using the MUltiple SIgnal Characterization (MUSIC) algorithm. The whole procedure greatly facilitates the detection and location of weak, broad-band signals, in turn avoiding the time-frequency resolution trade-off and frequency leakage effects which affect conventional covariance estimates based upon Windowed Fourier Transform. The method is applied to explosion signals recorded at Stromboli volcano by either a short-period, small aperture antenna, or a large-aperture, broad-band network. The LP (0.2 < T < 2s) components of the explosive signals are analysed using data from the small-aperture array and under the plane-wave assumption. In this manner, we obtain a precise time- and frequency-localization of the directional properties for waves impinging at the array. We then extend the wavefield decomposition method using a spherical wave front model, and analyse the VLP components (T > 2s) of the explosion recordings from the broad-band network. Source locations obtained this way are fully compatible with those retrieved from application of more traditional (and computationally expensive) time-domain techniques, such as the Radial Semblance method.

Watermarking on 3D mesh based on spherical wavelet transform.

PubMed

Jin, Jian-Qiu; Dai, Min-Ya; Bao, Hu-Jun; Peng, Qun-Sheng

2004-03-01

In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.

Implementing wavelet inverse-transform processor with surface acoustic wave device.

PubMed

Lu, Wenke; Zhu, Changchun; Liu, Qinghong; Zhang, Jingduan

2013-02-01

The objective of this research was to investigate the implementation schemes of the wavelet inverse-transform processor using surface acoustic wave (SAW) device, the length function of defining the electrodes, and the possibility of solving the load resistance and the internal resistance for the wavelet inverse-transform processor using SAW device. In this paper, we investigate the implementation schemes of the wavelet inverse-transform processor using SAW device. In the implementation scheme that the input interdigital transducer (IDT) and output IDT stand in a line, because the electrode-overlap envelope of the input IDT is identical with the one of the output IDT (i.e. the two transducers are identical), the product of the input IDT's frequency response and the output IDT's frequency response can be implemented, so that the wavelet inverse-transform processor can be fabricated. X-112(0)Y LiTaO(3) is used as a substrate material to fabricate the wavelet inverse-transform processor. The size of the wavelet inverse-transform processor using this implementation scheme is small, so its cost is low. First, according to the envelope function of the wavelet function, the length function of the electrodes is defined, then, the lengths of the electrodes can be calculated from the length function of the electrodes, finally, the input IDT and output IDT can be designed according to the lengths and widths for the electrodes. In this paper, we also present the load resistance and the internal resistance as the two problems of the wavelet inverse-transform processor using SAW devices. The solutions to these problems are achieved in this study. When the amplifiers are subjected to the input end and output end for the wavelet inverse-transform processor, they can eliminate the influence of the load resistance and the internal resistance on the output voltage of the wavelet inverse-transform processor using SAW device. Copyright © 2012 Elsevier B.V. All rights reserved.

Analysis of the Biceps Brachii Muscle by Varying the Arm Movement Level and Load Resistance Band

PubMed Central

Abdullah, Shahrum Shah; Jali, Mohd Hafiz

2017-01-01

Biceps brachii muscle illness is one of the common physical disabilities that requires rehabilitation exercises in order to build up the strength of the muscle after surgery. It is also important to monitor the condition of the muscle during the rehabilitation exercise through electromyography (EMG) signals. The purpose of this study was to analyse and investigate the selection of the best mother wavelet (MWT) function and depth of the decomposition level in the wavelet denoising EMG signals through the discrete wavelet transform (DWT) method at each decomposition level. In this experimental work, six healthy subjects comprised of males and females (26 ± 3.0 years and BMI of 22 ± 2.0) were selected as a reference for persons with the illness. The experiment was conducted for three sets of resistance band loads, namely, 5 kg, 9 kg, and 16 kg, as a force during the biceps brachii muscle contraction. Each subject was required to perform three levels of the arm angle positions (30°, 90°, and 150°) for each set of resistance band load. The experimental results showed that the Daubechies5 (db5) was the most appropriate DWT method together with a 6-level decomposition with a soft heursure threshold for the biceps brachii EMG signal analysis. PMID:29138687

Analysis of the Biceps Brachii Muscle by Varying the Arm Movement Level and Load Resistance Band.

PubMed

Burhan, Nuradebah; Kasno, Mohammad 'Afif; Ghazali, Rozaimi; Said, Md Radzai; Abdullah, Shahrum Shah; Jali, Mohd Hafiz

2017-01-01

Biceps brachii muscle illness is one of the common physical disabilities that requires rehabilitation exercises in order to build up the strength of the muscle after surgery. It is also important to monitor the condition of the muscle during the rehabilitation exercise through electromyography (EMG) signals. The purpose of this study was to analyse and investigate the selection of the best mother wavelet (MWT) function and depth of the decomposition level in the wavelet denoising EMG signals through the discrete wavelet transform (DWT) method at each decomposition level. In this experimental work, six healthy subjects comprised of males and females (26 ± 3.0 years and BMI of 22 ± 2.0) were selected as a reference for persons with the illness. The experiment was conducted for three sets of resistance band loads, namely, 5 kg, 9 kg, and 16 kg, as a force during the biceps brachii muscle contraction. Each subject was required to perform three levels of the arm angle positions (30°, 90°, and 150°) for each set of resistance band load. The experimental results showed that the Daubechies5 (db5) was the most appropriate DWT method together with a 6-level decomposition with a soft heursure threshold for the biceps brachii EMG signal analysis.

Spectral decomposition of seismic data with reassigned smoothed pseudo Wigner-Ville distribution

NASA Astrophysics Data System (ADS)

Wu, Xiaoyang; Liu, Tianyou

2009-07-01

Seismic signals are nonstationary mainly due to absorption and attenuation of seismic energy in strata. Referring to spectral decomposition of seismic data, the conventional method using short-time Fourier transform (STFT) limits temporal and spectral resolution by a predefined window length. Continuous-wavelet transform (CWT) uses dilation and translation of a wavelet to produce a time-scale map. However, the wavelets utilized should be orthogonal in order to obtain a satisfactory resolution. The less applied, Wigner-Ville distribution (WVD) being superior in energy distribution concentration, is confronted with cross-terms interference (CTI) when signals are multi-component. In order to reduce the impact of CTI, Cohen class uses kernel function as low-pass filter. Nevertheless it also weakens energy concentration of auto-terms. In this paper, we employ smoothed pseudo Wigner-Ville distribution (SPWVD) with Gauss kernel function to reduce CTI in time and frequency domain, then reassign values of SPWVD (called reassigned SPWVD) according to the center of gravity of the considering energy region so that distribution concentration is maintained simultaneously. We conduct the method above on a multi-component synthetic seismic record and compare with STFT and CWT spectra. Two field examples reveal that RSPWVD potentially can be applied to detect low-frequency shadows caused by hydrocarbons and to delineate the space distribution of abnormal geological body more precisely.

Multi-scale streamflow variability responses to precipitation over the headwater catchments in southern China

NASA Astrophysics Data System (ADS)

Niu, Jun; Chen, Ji; Wang, Keyi; Sivakumar, Bellie

2017-08-01

This paper examines the multi-scale streamflow variability responses to precipitation over 16 headwater catchments in the Pearl River basin, South China. The long-term daily streamflow data (1952-2000), obtained using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme, are studied. Temporal features of streamflow variability at 10 different timescales, ranging from 6 days to 8.4 years, are revealed with the Haar wavelet transform. The principal component analysis (PCA) is performed to categorize the headwater catchments with the coherent modes of multi-scale wavelet spectra. The results indicate that three distinct modes, with different variability distributions at small timescales and seasonal scales, can explain 95% of the streamflow variability. A large majority of the catchments (i.e. 12 out of 16) exhibit consistent mode feature on multi-scale variability throughout three sub-periods (1952-1968, 1969-1984, and 1985-2000). The multi-scale streamflow variability responses to precipitation are identified to be associated with the regional flood and drought tendency over the headwater catchments in southern China.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features.

PubMed

Tripathy, Rajesh Kumar; Dandapat, Samarendra

2017-04-01

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques.

A stethoscope with wavelet separation of cardiac and respiratory sounds for real time telemedicine implemented on field-programmable gate array

NASA Astrophysics Data System (ADS)

Castro, Víctor M.; Muñoz, Nestor A.; Salazar, Antonio J.

2015-01-01

Auscultation is one of the most utilized physical examination procedures for listening to lung, heart and intestinal sounds during routine consults and emergencies. Heart and lung sounds overlap in the thorax. An algorithm was used to separate them based on the discrete wavelet transform with multi-resolution analysis, which decomposes the signal into approximations and details. The algorithm was implemented in software and in hardware to achieve real-time signal separation. The heart signal was found in detail eight and the lung signal in approximation six. The hardware was used to separate the signals with a delay of 256 ms. Sending wavelet decomposition data - instead of the separated full signa - allows telemedicine applications to function in real time over low-bandwidth communication channels.

Necessary and sufficient condition for the realization of the complex wavelet

NASA Astrophysics Data System (ADS)

Keita, Alpha; Qing, Qianqin; Wang, Nengchao

1997-04-01

Wavelet theory is a whole new signal analysis theory in recent years, and the appearance of which is attracting lots of experts in many different fields giving it a deepen study. Wavelet transformation is a new kind of time. Frequency domain analysis method of localization in can-be- realized time domain or frequency domain. It has many perfect characteristics that many other kinds of time frequency domain analysis, such as Gabor transformation or Viginier. For example, it has orthogonality, direction selectivity, variable time-frequency domain resolution ratio, adjustable local support, parsing data in little amount, and so on. All those above make wavelet transformation a very important new tool and method in signal analysis field. Because the calculation of complex wavelet is very difficult, in application, real wavelet function is used. In this paper, we present a necessary and sufficient condition that the real wavelet function can be obtained by the complex wavelet function. This theorem has some significant values in theory. The paper prepares its technique from Hartley transformation, then, it gives the complex wavelet was a signal engineering expert. His Hartley transformation, which also mentioned by Hartley, had been overlooked for about 40 years, for the social production conditions at that time cannot help to show its superiority. Only when it came to the end of 70s and the early 80s, after the development of the fast algorithm of Fourier transformation and the hardware implement to some degree, the completely some positive-negative transforming method was coming to take seriously. W transformation, which mentioned by Zhongde Wang, pushed the studying work of Hartley transformation and its fast algorithm forward. The kernel function of Hartley transformation.

Comparison between wavelet and wavelet packet transform features for classification of faults in distribution system

NASA Astrophysics Data System (ADS)

Arvind, Pratul

2012-11-01

The ability to identify and classify all ten types of faults in a distribution system is an important task for protection engineers. Unlike transmission system, distribution systems have a complex configuration and are subjected to frequent faults. In the present work, an algorithm has been developed for identifying all ten types of faults in a distribution system by collecting current samples at the substation end. The samples are subjected to wavelet packet transform and artificial neural network in order to yield better classification results. A comparison of results between wavelet transform and wavelet packet transform is also presented thereby justifying the feature extracted from wavelet packet transform yields promising results. It should also be noted that current samples are collected after simulating a 25kv distribution system in PSCAD software.

Design of tree structured matched wavelet for HRV signals of menstrual cycle.

PubMed

Rawal, Kirti; Saini, B S; Saini, Indu

2016-07-01

An algorithm is presented for designing a new class of wavelets matched to the Heart Rate Variability (HRV) signals of the menstrual cycle. The proposed wavelets are used to find HRV variations between phases of menstrual cycle. The method finds the signal matching characteristics by minimising the shape feature error using Least Mean Square method. The proposed filter banks are used for the decomposition of the HRV signal. For reconstructing the original signal, the tree structure method is used. In this approach, decomposed sub-bands are selected based upon their energy in each sub-band. Thus, instead of using all sub-bands for reconstruction, sub-bands having high energy content are used for the reconstruction of signal. Thus, a lower number of sub-bands are required for reconstruction of the original signal which shows the effectiveness of newly created filter coefficients. Results show that proposed wavelets are able to differentiate HRV variations between phases of the menstrual cycle accurately than standard wavelets.

Spatially adaptive bases in wavelet-based coding of semi-regular meshes

NASA Astrophysics Data System (ADS)

Denis, Leon; Florea, Ruxandra; Munteanu, Adrian; Schelkens, Peter

2010-05-01

In this paper we present a wavelet-based coding approach for semi-regular meshes, which spatially adapts the employed wavelet basis in the wavelet transformation of the mesh. The spatially-adaptive nature of the transform requires additional information to be stored in the bit-stream in order to allow the reconstruction of the transformed mesh at the decoder side. In order to limit this overhead, the mesh is first segmented into regions of approximately equal size. For each spatial region, a predictor is selected in a rate-distortion optimal manner by using a Lagrangian rate-distortion optimization technique. When compared against the classical wavelet transform employing the butterfly subdivision filter, experiments reveal that the proposed spatially-adaptive wavelet transform significantly decreases the energy of the wavelet coefficients for all subbands. Preliminary results show also that employing the proposed transform for the lowest-resolution subband systematically yields improved compression performance at low-to-medium bit-rates. For the Venus and Rabbit test models the compression improvements add up to 1.47 dB and 0.95 dB, respectively.

Spherical 3D isotropic wavelets

NASA Astrophysics Data System (ADS)

Lanusse, F.; Rassat, A.; Starck, J.-L.

2012-04-01

Context. Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D spherical Fourier-Bessel (SFB) analysis in spherical coordinates is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. Aims: The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. Methods: We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. (2006). We also present a new fast discrete spherical Fourier-Bessel transform (DSFBT) based on both a discrete Bessel transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and find we can successfully remove noise without much loss to the large scale structure. Results: We have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel DSFBT. We illustrate its potential use for denoising using a toy model. All the algorithms presented in this paper are available for download as a public code called MRS3D at http://jstarck.free.fr/mrs3d.html

Single Trial EEG Patterns for the Prediction of Individual Differences in Fluid Intelligence.

PubMed

Qazi, Emad-Ul-Haq; Hussain, Muhammad; Aboalsamh, Hatim; Malik, Aamir Saeed; Amin, Hafeez Ullah; Bamatraf, Saeed

2016-01-01

Assessing a person's intelligence level is required in many situations, such as career counseling and clinical applications. EEG evoked potentials in oddball task and fluid intelligence score are correlated because both reflect the cognitive processing and attention. A system for prediction of an individual's fluid intelligence level using single trial Electroencephalography (EEG) signals has been proposed. For this purpose, we employed 2D and 3D contents and 34 subjects each for 2D and 3D, which were divided into low-ability (LA) and high-ability (HA) groups using Raven's Advanced Progressive Matrices (RAPM) test. Using visual oddball cognitive task, neural activity of each group was measured and analyzed over three midline electrodes (Fz, Cz, and Pz). To predict whether an individual belongs to LA or HA group, features were extracted using wavelet decomposition of EEG signals recorded in visual oddball task and support vector machine (SVM) was used as a classifier. Two different types of Haar wavelet transform based features have been extracted from the band (0.3 to 30 Hz) of EEG signals. Statistical wavelet features and wavelet coefficient features from the frequency bands 0.0-1.875 Hz (delta low) and 1.875-3.75 Hz (delta high), resulted in the 100 and 98% prediction accuracies, respectively, both for 2D and 3D contents. The analysis of these frequency bands showed clear difference between LA and HA groups. Further, discriminative values of the features have been validated using statistical significance tests and inter-class and intra-class variation analysis. Also, statistical test showed that there was no effect of 2D and 3D content on the assessment of fluid intelligence level. Comparisons with state-of-the-art techniques showed the superiority of the proposed system.

Wavelet filter analysis of local atmospheric pressure effects in the long-period tidal bands

NASA Astrophysics Data System (ADS)

Hu, X.-G.; Liu, L. T.; Ducarme, B.; Hsu, H. T.; Sun, H.-P.

2006-11-01

It is well known that local atmospheric pressure variations obviously affect the observation of short-period Earth tides, such as diurnal tides, semi-diurnal tides and ter-diurnal tides, but local atmospheric pressure effects on the long-period Earth tides have not been studied in detail. This is because the local atmospheric pressure is believed not to be sufficient for an effective pressure correction in long-period tidal bands, and there are no efficient methods to investigate local atmospheric effects in these bands. The usual tidal analysis software package, such as ETERNA, Baytap-G and VAV, cannot provide detailed pressure admittances for long-period tidal bands. We propose a wavelet method to investigate local atmospheric effects on gravity variations in long-period tidal bands. This method constructs efficient orthogonal filter bank with Daubechies wavelets of high vanishing moments. The main advantage of the wavelet filter bank is that it has excellent low frequency response and efficiently suppresses instrumental drift of superconducting gravimeters (SGs) without using any mathematical model. Applying the wavelet method to the 13-year continuous gravity observations from SG T003 in Brussels, Belgium, we filtered 12 long-period tidal groups into eight narrow frequency bands. Wavelet method demonstrates that local atmospheric pressure fluctuations are highly correlated with the noise of SG measurements in the period band 4-40 days with correlation coefficients higher than 0.95 and local atmospheric pressure variations are the main error source for the determination of the tidal parameters in these bands. We show the significant improvement of long-period tidal parameters provided by wavelet method in term of precision.

Multidimensional signaling via wavelet packets

NASA Astrophysics Data System (ADS)

Lindsey, Alan R.

1995-04-01

This work presents a generalized signaling strategy for orthogonally multiplexed communication. Wavelet packet modulation (WPM) employs the basis functions from an arbitrary pruning of a full dyadic tree structured filter bank as orthogonal pulse shapes for conventional QAM symbols. The multi-scale modulation (MSM) and M-band wavelet modulation (MWM) schemes which have been recently introduced are handled as special cases, with the added benefit of an entire library of potentially superior sets of basis functions. The figures of merit are derived and it is shown that the power spectral density is equivalent to that for QAM (in fact, QAM is another special case) and hence directly applicable in existing systems employing this standard modulation. Two key advantages of this method are increased flexibility in time-frequency partitioning and an efficient all-digital filter bank implementation, making the WPM scheme more robust to a larger set of interferences (both temporal and sinusoidal) and computationally attractive as well.

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

Dual-tree complex wavelet transform and image block residual-based multi-focus image fusion in visual sensor networks.

PubMed

Yang, Yong; Tong, Song; Huang, Shuying; Lin, Pan

2014-11-26

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.

Evaluation of retrieval methods of daytime convective boundary layer height based on lidar data

NASA Astrophysics Data System (ADS)

Li, Hong; Yang, Yi; Hu, Xiao-Ming; Huang, Zhongwei; Wang, Guoyin; Zhang, Beidou; Zhang, Tiejun

2017-04-01

The atmospheric boundary layer height is a basic parameter in describing the structure of the lower atmosphere. Because of their high temporal resolution, ground-based lidar data are widely used to determine the daytime convective boundary layer height (CBLH), but the currently available retrieval methods have their advantages and drawbacks. In this paper, four methods of retrieving the CBLH (i.e., the gradient method, the idealized backscatter method, and two forms of the wavelet covariance transform method) from lidar normalized relative backscatter are evaluated, using two artificial cases (an idealized profile and a case similar to real profile), to test their stability and accuracy. The results show that the gradient method is suitable for high signal-to-noise ratio conditions. The idealized backscatter method is less sensitive to the first estimate of the CBLH; however, it is computationally expensive. The results obtained from the two forms of the wavelet covariance transform method are influenced by the selection of the initial input value of the wavelet amplitude. Further sensitivity analysis using real profiles under different orders of magnitude of background counts show that when different initial input values are set, the idealized backscatter method always obtains consistent CBLH. For two wavelet methods, the different CBLH are always obtained with the increase in the wavelet amplitude when noise is significant. Finally, the CBLHs as measured by three lidar-based methods are evaluated by as measured from L-band soundings. The boundary layer heights from two instruments coincide with ±200 m in most situations.

A Comparative Analysis for Selection of Appropriate Mother Wavelet for Detection of Stationary Disturbances

NASA Astrophysics Data System (ADS)

Kamble, Saurabh Prakash; Thawkar, Shashank; Gaikwad, Vinayak G.; Kothari, D. P.

2017-12-01

Detection of disturbances is the first step of mitigation. Power electronics plays a crucial role in modern power system which makes system operation efficient but it also bring stationary disturbances in the power system and added impurities to the supply. It happens because of the non-linear loads used in modern day power system which inject disturbances like harmonic disturbances, flickers, sag etc. in power grid. These impurities can damage equipments so it is necessary to mitigate these impurities present in the supply very quickly. So, digital signal processing techniques are incorporated for detection purpose. Signal processing techniques like fast Fourier transform, short-time Fourier transform, Wavelet transform etc. are widely used for the detection of disturbances. Among all, wavelet transform is widely used because of its better detection capabilities. But, which mother wavelet has to use for detection is still a mystery. Depending upon the periodicity, the disturbances are classified as stationary and non-stationary disturbances. This paper presents the importance of selection of mother wavelet for analyzing stationary disturbances using discrete wavelet transform. Signals with stationary disturbances of various frequencies are generated using MATLAB. The analysis of these signals is done using various mother wavelets like Daubechies and bi-orthogonal wavelets and the measured root mean square value of stationary disturbance is obtained. The measured value obtained by discrete wavelet transform is compared with the exact RMS value of the frequency component and the percentage differences are presented which helps to select optimum mother wavelet.

Adaptive wavelet collocation methods for initial value boundary problems of nonlinear PDE's

NASA Technical Reports Server (NTRS)

Cai, Wei; Wang, Jian-Zhong

1993-01-01

We have designed a cubic spline wavelet decomposition for the Sobolev space H(sup 2)(sub 0)(I) where I is a bounded interval. Based on a special 'point-wise orthogonality' of the wavelet basis functions, a fast Discrete Wavelet Transform (DWT) is constructed. This DWT transform will map discrete samples of a function to its wavelet expansion coefficients in O(N log N) operations. Using this transform, we propose a collocation method for the initial value boundary problem of nonlinear PDE's. Then, we test the efficiency of the DWT transform and apply the collocation method to solve linear and nonlinear PDE's.

Chemometrics-assisted spectrophotometric method for simultaneous determination of Pb²⁺ and Cu²⁺ ions in different foodstuffs, soil and water samples using 2-benzylspiro [isoindoline-1,5'-oxazolidine]-2',3,4'-trione using continuous wavelet transformation and partial least squares - calculation of pKf of complexes with rank annihilation factor analysis.

PubMed

Abbasi Tarighat, Maryam; Nabavi, Masoume; Mohammadizadeh, Mohammad Reza

2015-06-15

A new multi-component analysis method based on zero-crossing point-continuous wavelet transformation (CWT) was developed for simultaneous spectrophotometric determination of Cu(2+) and Pb(2+) ions based on the complex formation with 2-benzyl espiro[isoindoline-1,5 oxasolidine]-2,3,4 trione (BSIIOT). The absorption spectra were evaluated with respect to synthetic ligand concentration, time of complexation and pH. Therefore according the absorbance values, 0.015 mmol L(-1) BSIIOT, 10 min after mixing and pH 8.0 were used as optimum values. The complex formation between BSIIOT ligand and the cations Cu(2+) and Pb(2+) by application of rank annihilation factor analysis (RAFA) were investigated. Daubechies-4 (db4), discrete Meyer (dmey), Morlet (morl) and Symlet-8 (sym8) continuous wavelet transforms for signal treatments were found to be suitable among the wavelet families. The applicability of new synthetic ligand and selected mother wavelets were used for the simultaneous determination of strongly overlapped spectra of species without using any pre-chemical treatment. Therefore, CWT signals together with zero crossing technique were directly applied to the overlapping absorption spectra of Cu(2+) and Pb(2+). The calibration graphs for estimation of Pb(2+) and Cu (2+)were obtained by measuring the CWT amplitudes at zero crossing points for Cu(2+) and Pb(2+) at the wavelet domain, respectively. The proposed method was validated by simultaneous determination of Cu(2+) and Pb(2+) ions in red beans, walnut, rice, tea and soil samples. The obtained results of samples with proposed method have been compared with those predicted by partial least squares (PLS) and flame atomic absorption spectrophotometry (FAAS). Copyright © 2015 Elsevier B.V. All rights reserved.

Speckle noise reduction in quantitative optical metrology techniques by application of the discrete wavelet transformation

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Pryputniewicz, Ryszard J.

2002-06-01

Effective suppression of speckle noise content in interferometric data images can help in improving accuracy and resolution of the results obtained with interferometric optical metrology techniques. In this paper, novel speckle noise reduction algorithms based on the discrete wavelet transformation are presented. The algorithms proceed by: (a) estimating the noise level contained in the interferograms of interest, (b) selecting wavelet families, (c) applying the wavelet transformation using the selected families, (d) wavelet thresholding, and (e) applying the inverse wavelet transformation, producing denoised interferograms. The algorithms are applied to the different stages of the processing procedures utilized for generation of quantitative speckle correlation interferometry data of fiber-optic based opto-electronic holography (FOBOEH) techniques, allowing identification of optimal processing conditions. It is shown that wavelet algorithms are effective for speckle noise reduction while preserving image features otherwise faded with other algorithms.

Poisson denoising on the sphere: application to the Fermi gamma ray space telescope

NASA Astrophysics Data System (ADS)

Schmitt, J.; Starck, J. L.; Casandjian, J. M.; Fadili, J.; Grenier, I.

2010-07-01

The Large Area Telescope (LAT), the main instrument of the Fermi gamma-ray Space telescope, detects high energy gamma rays with energies from 20 MeV to more than 300 GeV. The two main scientific objectives, the study of the Milky Way diffuse background and the detection of point sources, are complicated by the lack of photons. That is why we need a powerful Poisson noise removal method on the sphere which is efficient on low count Poisson data. This paper presents a new multiscale decomposition on the sphere for data with Poisson noise, called multi-scale variance stabilizing transform on the sphere (MS-VSTS). This method is based on a variance stabilizing transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has a quasi constant variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. MS-VSTS consists of decomposing the data into a sparse multi-scale dictionary like wavelets or curvelets, and then applying a VST on the coefficients in order to get almost Gaussian stabilized coefficients. In this work, we use the isotropic undecimated wavelet transform (IUWT) and the curvelet transform as spherical multi-scale transforms. Then, binary hypothesis testing is carried out to detect significant coefficients, and the denoised image is reconstructed with an iterative algorithm based on hybrid steepest descent (HSD). To detect point sources, we have to extract the Galactic diffuse background: an extension of the method to background separation is then proposed. In contrary, to study the Milky Way diffuse background, we remove point sources with a binary mask. The gaps have to be interpolated: an extension to inpainting is then proposed. The method, applied on simulated Fermi LAT data, proves to be adaptive, fast and easy to implement.

Detecting of transient vibration signatures using an improved fast spatial-spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery

NASA Astrophysics Data System (ADS)

Chen, BinQiang; Zhang, ZhouSuo; Zi, YanYang; He, ZhengJia; Sun, Chuang

2013-10-01

Detecting transient vibration signatures is of vital importance for vibration-based condition monitoring and fault detection of the rotating machinery. However, raw mechanical signals collected by vibration sensors are generally mixtures of physical vibrations of the multiple mechanical components installed in the examined machinery. Fault-generated incipient vibration signatures masked by interfering contents are difficult to be identified. The fast kurtogram (FK) is a concise and smart gadget for characterizing these vibration features. The multi-rate filter-bank (MRFB) and the spectral kurtosis (SK) indicator of the FK are less powerful when strong interfering vibration contents exist, especially when the FK are applied to vibration signals of short duration. It is encountered that the impulsive interfering contents not authentically induced by mechanical faults complicate the optimal analyzing process and lead to incorrect choosing of the optimal analysis subband, therefore the original FK may leave out the essential fault signatures. To enhance the analyzing performance of FK for industrial applications, an improved version of fast kurtogram, named as "fast spatial-spectral ensemble kurtosis kurtogram", is presented. In the proposed technique, discrete quasi-analytic wavelet tight frame (QAWTF) expansion methods are incorporated as the detection filters. The QAWTF, constructed based on dual tree complex wavelet transform, possesses better vibration transient signature extracting ability and enhanced time-frequency localizability compared with conventional wavelet packet transforms (WPTs). Moreover, in the constructed QAWTF, a non-dyadic ensemble wavelet subband generating strategy is put forward to produce extra wavelet subbands that are capable of identifying fault features located in transition-band of WPT. On the other hand, an enhanced signal impulsiveness evaluating indicator, named "spatial-spectral ensemble kurtosis" (SSEK), is put forward and utilized as the quantitative measure to select optimal analyzing parameters. The SSEK indicator is robuster in evaluating the impulsiveness intensity of vibration signals due to its better suppressing ability of Gaussian noise, harmonics and sporadic impulsive shocks. Numerical validations, an experimental test and two engineering applications were used to verify the effectiveness of the proposed technique. The analyzing results of the numerical validations, experimental tests and engineering applications demonstrate that the proposed technique possesses robuster transient vibration content detecting performance in comparison with the original FK and the WPT-based FK method, especially when they are applied to the processing of vibration signals of relative limited duration.

Experimental study on the crack detection with optimized spatial wavelet analysis and windowing

NASA Astrophysics Data System (ADS)

Ghanbari Mardasi, Amir; Wu, Nan; Wu, Christine

2018-05-01

In this paper, a high sensitive crack detection is experimentally realized and presented on a beam under certain deflection by optimizing spatial wavelet analysis. Due to the crack existence in the beam structure, a perturbation/slop singularity is induced in the deflection profile. Spatial wavelet transformation works as a magnifier to amplify the small perturbation signal at the crack location to detect and localize the damage. The profile of a deflected aluminum cantilever beam is obtained for both intact and cracked beams by a high resolution laser profile sensor. Gabor wavelet transformation is applied on the subtraction of intact and cracked data sets. To improve detection sensitivity, scale factor in spatial wavelet transformation and the transformation repeat times are optimized. Furthermore, to detect the possible crack close to the measurement boundaries, wavelet transformation edge effect, which induces large values of wavelet coefficient around the measurement boundaries, is efficiently reduced by introducing different windowing functions. The result shows that a small crack with depth of less than 10% of the beam height can be localized with a clear perturbation. Moreover, the perturbation caused by a crack at 0.85 mm away from one end of the measurement range, which is covered by wavelet transform edge effect, emerges by applying proper window functions.

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.

MRS3D: 3D Spherical Wavelet Transform on the Sphere

NASA Astrophysics Data System (ADS)

Lanusse, F.; Rassat, A.; Starck, J.-L.

2011-12-01

Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. We present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We tested the 3D wavelet transform and as a toy-application, applied a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and found we can successfully remove noise without much loss to the large scale structure. The new spherical 3D isotropic wavelet transform, called MRS3D, is ideally suited to analysing and denoising future 3D spherical cosmological surveys; it uses a novel discrete spherical Fourier-Bessel Transform. MRS3D is based on two packages, IDL and Healpix and can be used only if these two packages have been installed.

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.

Characterizing multi-scale self-similar behavior and non-statistical properties of fluctuations in financial time series

NASA Astrophysics Data System (ADS)

Ghosh, Sayantan; Manimaran, P.; Panigrahi, Prasanta K.

2011-11-01

We make use of wavelet transform to study the multi-scale, self-similar behavior and deviations thereof, in the stock prices of large companies, belonging to different economic sectors. The stock market returns exhibit multi-fractal characteristics, with some of the companies showing deviations at small and large scales. The fact that, the wavelets belonging to the Daubechies’ (Db) basis enables one to isolate local polynomial trends of different degrees, plays the key role in isolating fluctuations at different scales. One of the primary motivations of this work is to study the emergence of the k-3 behavior [X. Gabaix, P. Gopikrishnan, V. Plerou, H. Stanley, A theory of power law distributions in financial market fluctuations, Nature 423 (2003) 267-270] of the fluctuations starting with high frequency fluctuations. We make use of Db4 and Db6 basis sets to respectively isolate local linear and quadratic trends at different scales in order to study the statistical characteristics of these financial time series. The fluctuations reveal fat tail non-Gaussian behavior, unstable periodic modulations, at finer scales, from which the characteristic k-3 power law behavior emerges at sufficiently large scales. We further identify stable periodic behavior through the continuous Morlet wavelet.

Wavelet-based multicomponent denoising on GPU to improve the classification of hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco; Mouriño, J. C.

2017-10-01

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

Automatic Image Registration of Multi-Modal Remotely Sensed Data with Global Shearlet Features

PubMed Central

Murphy, James M.; Le Moigne, Jacqueline; Harding, David J.

2017-01-01

Automatic image registration is the process of aligning two or more images of approximately the same scene with minimal human assistance. Wavelet-based automatic registration methods are standard, but sometimes are not robust to the choice of initial conditions. That is, if the images to be registered are too far apart relative to the initial guess of the algorithm, the registration algorithm does not converge or has poor accuracy, and is thus not robust. These problems occur because wavelet techniques primarily identify isotropic textural features and are less effective at identifying linear and curvilinear edge features. We integrate the recently developed mathematical construction of shearlets, which is more effective at identifying sparse anisotropic edges, with an existing automatic wavelet-based registration algorithm. Our shearlet features algorithm produces more distinct features than wavelet features algorithms; the separation of edges from textures is even stronger than with wavelets. Our algorithm computes shearlet and wavelet features for the images to be registered, then performs least squares minimization on these features to compute a registration transformation. Our algorithm is two-staged and multiresolution in nature. First, a cascade of shearlet features is used to provide a robust, though approximate, registration. This is then refined by registering with a cascade of wavelet features. Experiments across a variety of image classes show an improved robustness to initial conditions, when compared to wavelet features alone. PMID:29123329

Adaptive Filtering in the Wavelet Transform Domain Via Genetic Algorithms

DTIC Science & Technology

2004-08-01

inverse transform process. 2. BACKGROUND The image processing research conducted at the AFRL/IFTA Reconfigurable Computing Laboratory has been...coefficients from the wavelet domain back into the original signal domain. In other words, the inverse transform produces the original signal x(t) from the...coefficients for an inverse wavelet transform, such that the MSE of images reconstructed by this inverse transform is significantly less than the mean squared

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.

A Wavelet Model for Vocalic Speech Coarticulation

DTIC Science & Technology

1994-10-01

control vowel’s signal as the mother wavelet. A practical experiment is conducted to evaluate the coarticulation channel using samples 01 real speech...transformation from a control speech state (input) to an effected speech state (output). Specifically, a vowel produced in isolation is transformed into an...the wavelet transform of the effected vowel’s signal, using the control vowel’s signal as the mother wavelet. A practical experiment is conducted to

Use of the wavelet transform to investigate differences in brain PET images between patient groups

NASA Astrophysics Data System (ADS)

Ruttimann, Urs E.; Unser, Michael A.; Rio, Daniel E.; Rawlings, Robert R.

1993-06-01

Suitability of the wavelet transform was studied for the analysis of glucose utilization differences between subject groups as displayed in PET images. To strengthen statistical inference, it was of particular interest investigating the tradeoff between signal localization and image decomposition into uncorrelated components. This tradeoff is shown to be controlled by wavelet regularity, with the optimal compromise attained by third-order orthogonal spline wavelets. Testing of the ensuing wavelet coefficients identified only about 1.5% as statistically different (p < .05) from noise, which then served to resynthesize the difference images by the inverse wavelet transform. The resulting images displayed relatively uniform, noise-free regions of significant differences with, due to the good localization maintained by the wavelets, very little reconstruction artifacts.

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

Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings

NASA Astrophysics Data System (ADS)

Wang, Dong; Zhao, Yang; Yi, Cai; Tsui, Kwok-Leung; Lin, Jianhui

2018-02-01

Rolling element bearings are widely used in various industrial machines, such as electric motors, generators, pumps, gearboxes, railway axles, turbines, and helicopter transmissions. Fault diagnosis of rolling element bearings is beneficial to preventing any unexpected accident and reducing economic loss. In the past years, many bearing fault detection methods have been developed. Recently, a new adaptive signal processing method called empirical wavelet transform attracts much attention from readers and engineers and its applications to bearing fault diagnosis have been reported. The main problem of empirical wavelet transform is that Fourier segments required in empirical wavelet transform are strongly dependent on the local maxima of the amplitudes of the Fourier spectrum of a signal, which connotes that Fourier segments are not always reliable and effective if the Fourier spectrum of the signal is complicated and overwhelmed by heavy noises and other strong vibration components. In this paper, sparsity guided empirical wavelet transform is proposed to automatically establish Fourier segments required in empirical wavelet transform for fault diagnosis of rolling element bearings. Industrial bearing fault signals caused by single and multiple railway axle bearing defects are used to verify the effectiveness of the proposed sparsity guided empirical wavelet transform. Results show that the proposed method can automatically discover Fourier segments required in empirical wavelet transform and reveal single and multiple railway axle bearing defects. Besides, some comparisons with three popular signal processing methods including ensemble empirical mode decomposition, the fast kurtogram and the fast spectral correlation are conducted to highlight the superiority of the proposed method.

Characteristics of Lake Chad Level Variability and Links to ENSO, Precipitation, and River Discharge

PubMed Central

Demoz, Belay; Gebremariam, Sium

2014-01-01

This study used trend, correlation, and wavelet analysis to characterize Lake Chad (LC) level fluctuations, river discharge, El Niño Southern Oscillation (ENSO), and precipitation regimes and their interrelationships. Linear correlation results indicate a negative association between ENSO and LC level, river discharge and precipitation. Trend analysis shows increasing precipitation in the Lake Chad Basin (LCB) but decreasing LC level. The mode of interannual variability in LC level, rainfall, and ENSO analyzed using wavelet analysis is dominated by 3-4-year periods. Results show that variability in ENSO could explain only 31% and 13% of variations in LC level at Kindjeria and precipitation in the northern LCB, respectively. The wavelet transform coherency (WTC) between LC level of the southern pool at Kalom and ENSO is statistically significant at the 95% confidence level and phase-locked, implying a cause-and-effect association. These strong coherencies coincide with the La Niña years with the exception of 1997-1998 El Niño events. The WTC shows strong covariance between increasing precipitation and LC level in the northern pool at a 2- to 4-year band and 3- to 4-year band localized from 1996 to 2010. Implications for water resource planning and management are discussed. PMID:25538946

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

Fault Analysis of Space Station DC Power Systems-Using Neural Network Adaptive Wavelets to Detect Faults

NASA Technical Reports Server (NTRS)

Momoh, James A.; Wang, Yanchun; Dolce, James L.

1997-01-01

This paper describes the application of neural network adaptive wavelets for fault diagnosis of space station power system. The method combines wavelet transform with neural network by incorporating daughter wavelets into weights. Therefore, the wavelet transform and neural network training procedure become one stage, which avoids the complex computation of wavelet parameters and makes the procedure more straightforward. The simulation results show that the proposed method is very efficient for the identification of fault locations.

Embedded wavelet packet transform technique for texture compression

NASA Astrophysics Data System (ADS)

Li, Jin; Cheng, Po-Yuen; Kuo, C.-C. Jay

1995-09-01

A highly efficient texture compression scheme is proposed in this research. With this scheme, energy compaction of texture images is first achieved by the wavelet packet transform, and an embedding approach is then adopted for the coding of the wavelet packet transform coefficients. By comparing the proposed algorithm with the JPEG standard, FBI wavelet/scalar quantization standard and the EZW scheme with extensive experimental results, we observe a significant improvement in the rate-distortion performance and visual quality.

Wavelet entropy characterization of elevated intracranial pressure.

PubMed

Xu, Peng; Scalzo, Fabien; Bergsneider, Marvin; Vespa, Paul; Chad, Miller; Hu, Xiao

2008-01-01

Intracranial Hypertension (ICH) often occurs for those patients with traumatic brain injury (TBI), stroke, tumor, etc. Pathology of ICH is still controversial. In this work, we used wavelet entropy and relative wavelet entropy to study the difference existed between normal and hypertension states of ICP for the first time. The wavelet entropy revealed the similar findings as the approximation entropy that entropy during ICH state is smaller than that in normal state. Moreover, with wavelet entropy, we can see that ICH state has the more focused energy in the low wavelet frequency band (0-3.1 Hz) than the normal state. The relative wavelet entropy shows that the energy distribution in the wavelet bands between these two states is actually different. Based on these results, we suggest that ICH may be formed by the re-allocation of oscillation energy within brain.

Localization of self-potential sources in volcano-electric effect with complex continuous wavelet transform and electrical tomography methods for an active volcano

NASA Astrophysics Data System (ADS)

Saracco, Ginette; Labazuy, Philippe; Moreau, Frédérique

2004-06-01

This study concerns the fluid flow circulation associated with magmatic intrusion during volcanic eruptions from electrical tomography studies. The objective is to localize and characterize the sources responsible for electrical disturbances during a time evolution survey between 1993 and 1999 of an active volcano, the Piton de la Fournaise. We have applied a dipolar probability tomography and a multi-scale analysis on synthetic and experimental SP data. We show the advantage of the complex continuous wavelet transform which allows to obtain directional information from the phase without a priori information on sources. In both cases, we point out a translation of potential sources through the upper depths during periods preceding a volcanic eruption around specific faults or structural features. The set of parameters obtained (vertical and horizontal localization, multipolar degree and inclination) could be taken into account as criteria to define volcanic precursors.

A Novel Image Compression Algorithm for High Resolution 3D Reconstruction

NASA Astrophysics Data System (ADS)

Siddeq, M. M.; Rodrigues, M. A.

2014-06-01

This research presents a novel algorithm to compress high-resolution images for accurate structured light 3D reconstruction. Structured light images contain a pattern of light and shadows projected on the surface of the object, which are captured by the sensor at very high resolutions. Our algorithm is concerned with compressing such images to a high degree with minimum loss without adversely affecting 3D reconstruction. The Compression Algorithm starts with a single level discrete wavelet transform (DWT) for decomposing an image into four sub-bands. The sub-band LL is transformed by DCT yielding a DC-matrix and an AC-matrix. The Minimize-Matrix-Size Algorithm is used to compress the AC-matrix while a DWT is applied again to the DC-matrix resulting in LL2, HL2, LH2 and HH2 sub-bands. The LL2 sub-band is transformed by DCT, while the Minimize-Matrix-Size Algorithm is applied to the other sub-bands. The proposed algorithm has been tested with images of different sizes within a 3D reconstruction scenario. The algorithm is demonstrated to be more effective than JPEG2000 and JPEG concerning higher compression rates with equivalent perceived quality and the ability to more accurately reconstruct the 3D models.

Wavelet transform analysis of transient signals: the seismogram and the electrocardiogram

DOE Office of Scientific and Technical Information (OSTI.GOV)

Anant, K.S.

1997-06-01

In this dissertation I quantitatively demonstrate how the wavelet transform can be an effective mathematical tool for the analysis of transient signals. The two key signal processing applications of the wavelet transform, namely feature identification and representation (i.e., compression), are shown by solving important problems involving the seismogram and the electrocardiogram. The seismic feature identification problem involved locating in time the P and S phase arrivals. Locating these arrivals accurately (particularly the S phase) has been a constant issue in seismic signal processing. In Chapter 3, I show that the wavelet transform can be used to locate both the Pmore » as well as the S phase using only information from single station three-component seismograms. This is accomplished by using the basis function (wave-let) of the wavelet transform as a matching filter and by processing information across scales of the wavelet domain decomposition. The `pick` time results are quite promising as compared to analyst picks. The representation application involved the compression of the electrocardiogram which is a recording of the electrical activity of the heart. Compression of the electrocardiogram is an important problem in biomedical signal processing due to transmission and storage limitations. In Chapter 4, I develop an electrocardiogram compression method that applies vector quantization to the wavelet transform coefficients. The best compression results were obtained by using orthogonal wavelets, due to their ability to represent a signal efficiently. Throughout this thesis the importance of choosing wavelets based on the problem at hand is stressed. In Chapter 5, I introduce a wavelet design method that uses linear prediction in order to design wavelets that are geared to the signal or feature being analyzed. The use of these designed wavelets in a test feature identification application led to positive results. The methods developed in this thesis; the feature identification methods of Chapter 3, the compression methods of Chapter 4, as well as the wavelet design methods of Chapter 5, are general enough to be easily applied to other transient signals.« less

Local wavelet transform: a cost-efficient custom processor for space image compression

NASA Astrophysics Data System (ADS)

Masschelein, Bart; Bormans, Jan G.; Lafruit, Gauthier

2002-11-01

Thanks to its intrinsic scalability features, the wavelet transform has become increasingly popular as decorrelator in image compression applications. Throuhgput, memory requirements and complexity are important parameters when developing hardware image compression modules. An implementation of the classical, global wavelet transform requires large memory sizes and implies a large latency between the availability of the input image and the production of minimal data entities for entropy coding. Image tiling methods, as proposed by JPEG2000, reduce the memory sizes and the latency, but inevitably introduce image artefacts. The Local Wavelet Transform (LWT), presented in this paper, is a low-complexity wavelet transform architecture using a block-based processing that results in the same transformed images as those obtained by the global wavelet transform. The architecture minimizes the processing latency with a limited amount of memory. Moreover, as the LWT is an instruction-based custom processor, it can be programmed for specific tasks, such as push-broom processing of infinite-length satelite images. The features of the LWT makes it appropriate for use in space image compression, where high throughput, low memory sizes, low complexity, low power and push-broom processing are important requirements.

Exploring the impact of wavelet-based denoising in the classification of remote sensing hyperspectral images

NASA Astrophysics Data System (ADS)

Quesada-Barriuso, Pablo; Heras, Dora B.; Argüello, Francisco

2016-10-01

The classification of remote sensing hyperspectral images for land cover applications is a very intensive topic. In the case of supervised classification, Support Vector Machines (SVMs) play a dominant role. Recently, the Extreme Learning Machine algorithm (ELM) has been extensively used. The classification scheme previously published by the authors, and called WT-EMP, introduces spatial information in the classification process by means of an Extended Morphological Profile (EMP) that is created from features extracted by wavelets. In addition, the hyperspectral image is denoised in the 2-D spatial domain, also using wavelets and it is joined to the EMP via a stacked vector. In this paper, the scheme is improved achieving two goals. The first one is to reduce the classification time while preserving the accuracy of the classification by using ELM instead of SVM. The second one is to improve the accuracy results by performing not only a 2-D denoising for every spectral band, but also a previous additional 1-D spectral signature denoising applied to each pixel vector of the image. For each denoising the image is transformed by applying a 1-D or 2-D wavelet transform, and then a NeighShrink thresholding is applied. Improvements in terms of classification accuracy are obtained, especially for images with close regions in the classification reference map, because in these cases the accuracy of the classification in the edges between classes is more relevant.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Skin image retrieval using Gabor wavelet texture feature.

PubMed

Ou, X; Pan, W; Zhang, X; Xiao, P

2016-12-01

Skin imaging plays a key role in many clinical studies. We have used many skin imaging techniques, including the recently developed capacitive contact skin imaging based on fingerprint sensors. The aim of this study was to develop an effective skin image retrieval technique using Gabor wavelet transform, which can be used on different types of skin images, but with a special focus on skin capacitive contact images. Content-based image retrieval (CBIR) is a useful technology to retrieve stored images from database by supplying query images. In a typical CBIR, images are retrieved based on colour, shape, texture, etc. In this study, texture feature is used for retrieving skin images, and Gabor wavelet transform is used for texture feature description and extraction. The results show that the Gabor wavelet texture features can work efficiently on different types of skin images. Although Gabor wavelet transform is slower compared with other image retrieval techniques, such as principal component analysis (PCA) and grey-level co-occurrence matrix (GLCM), Gabor wavelet transform is the best for retrieving skin capacitive contact images and facial images with different orientations. Gabor wavelet transform can also work well on facial images with different expressions and skin cancer/disease images. We have developed an effective skin image retrieval method based on Gabor wavelet transform, that it is useful for retrieving different types of images, namely digital colour face images, digital colour skin cancer and skin disease images, and particularly greyscale skin capacitive contact images. Gabor wavelet transform can also be potentially useful for face recognition (with different orientation and expressions) and skin cancer/disease diagnosis. © 2016 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Multi-scale Imaging of Cellular and Sub-cellular Structures using Scanning Probe Recognition Microscopy.

NASA Astrophysics Data System (ADS)

Chen, Q.; Rice, A. F.

2005-03-01

Scanning Probe Recognition Microscopy is a new scanning probe capability under development within our group to reliably return to and directly interact with a specific nanobiological feature of interest. In previous work, we have successfully recognized and classified tubular versus globular biological objects from experimental atomic force microscope images using a method based on normalized central moments [ref. 1]. In this paper we extend this work to include recognition schemes appropriate for cellular and sub-cellular structures. Globular cells containing tubular actin filaments are under investigation. Thus there are differences in external/internal shapes and scales. Continuous Wavelet Transform with a differential Gaussian mother wavelet is employed for multi- scale analysis. [ref. 1] Q. Chen, V. Ayres and L. Udpa, ``Biological Investigation Using Scanning Probe Recognition Microscopy,'' Proceedings 3rd IEEE Conference on Nanotechnology, vol. 2, p 863-865 (2003).

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Seismic reflection response from cross-correlations of ambient vibrations on non-conventional hidrocarbon reservoir

NASA Astrophysics Data System (ADS)

Huerta, F. V.; Granados, I.; Aguirre, J.; Carrera, R. Á.

2017-12-01

Nowadays, in hydrocarbon industry, there is a need to optimize and reduce exploration costs in the different types of reservoirs, motivating the community specialized in the search and development of alternative exploration geophysical methods. This study show the reflection response obtained from a shale gas / oil deposit through the method of seismic interferometry of ambient vibrations in combination with Wavelet analysis and conventional seismic reflection techniques (CMP & NMO). The method is to generate seismic responses from virtual sources through the process of cross-correlation of records of Ambient Seismic Vibrations (ASV), collected in different receivers. The seismic response obtained is interpreted as the response that would be measured in one of the receivers considering a virtual source in the other. The acquisition of ASV records was performed in northern of Mexico through semi-rectangular arrays of multi-component geophones with instrumental response of 10 Hz. The in-line distance between geophones was 40 m while in cross-line was 280 m, the sampling used during the data collection was 2 ms and the total duration of the records was 6 hours. The results show the reflection response of two lines in the in-line direction and two in the cross-line direction for which the continuity of coherent events have been identified and interpreted as reflectors. There is certainty that the events identified correspond to reflections because the time-frequency analysis performed with the Wavelet Transform has allowed to identify the frequency band in which there are body waves. On the other hand, the CMP and NMO techniques have allowed to emphasize and correct the reflection response obtained during the correlation processes in the frequency band of interest. The results of the processing and analysis of ASV records through the seismic interferometry method have allowed us to see interesting results in light of the cross-correlation process in combination with the Wavelet analysis and conventional seismic reflection techniques. Therefore it was possible to recover the seismic response on each analyzed source-receiver pair, allowing us to obtain the reflection response of each analyzed seismic line.

Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features

PubMed Central

Dandapat, Samarendra

2017-01-01

The complex wavelet sub-band bi-spectrum (CWSB) features are proposed for detection and classification of myocardial infarction (MI), heart muscle disease (HMD) and bundle branch block (BBB) from 12-lead ECG. The dual tree CW transform of 12-lead ECG produces CW coefficients at different sub-bands. The higher-order CW analysis is used for evaluation of CWSB. The mean of the absolute value of CWSB, and the number of negative phase angle and the number of positive phase angle features from the phase of CWSB of 12-lead ECG are evaluated. Extreme learning machine and support vector machine (SVM) classifiers are used to evaluate the performance of CWSB features. Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies. The individual accuracy values for MI, HMD and BBB classes are obtained as 98.37, 97.39 and 96.40%, respectively, using SVM classifier and radial basis function kernel function. A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques. PMID:28894589

A Rigid Image Registration Based on the Nonsubsampled Contourlet Transform and Genetic Algorithms

PubMed Central

Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

2010-01-01

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise. PMID:22163672

A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms.

PubMed

Meskine, Fatiha; Chikr El Mezouar, Miloud; Taleb, Nasreddine

2010-01-01

Image registration is a fundamental task used in image processing to match two or more images taken at different times, from different sensors or from different viewpoints. The objective is to find in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images. Exhaustive search is computationally expensive and the computational cost increases exponentially with the number of transformation parameters and the size of the data set. In this work, we present an efficient image registration algorithm that uses genetic algorithms within a multi-resolution framework based on the Non-Subsampled Contourlet Transform (NSCT). An adaptable genetic algorithm for registration is adopted in order to minimize the search space. This approach is used within a hybrid scheme applying the two techniques fitness sharing and elitism. Two NSCT based methods are proposed for registration. A comparative study is established between these methods and a wavelet based one. Because the NSCT is a shift-invariant multidirectional transform, the second method is adopted for its search speeding up property. Simulation results clearly show that both proposed techniques are really promising methods for image registration compared to the wavelet approach, while the second technique has led to the best performance results of all. Moreover, to demonstrate the effectiveness of these methods, these registration techniques have been successfully applied to register SPOT, IKONOS and Synthetic Aperture Radar (SAR) images. The algorithm has been shown to work perfectly well for multi-temporal satellite images as well, even in the presence of noise.

Wavelet-linear genetic programming: A new approach for modeling monthly streamflow

NASA Astrophysics Data System (ADS)

Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur

2017-06-01

The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.

Investigation on thermo-acoustic instability dynamic characteristics of hydrocarbon fuel flowing in scramjet cooling channel based on wavelet entropy method

NASA Astrophysics Data System (ADS)

Zan, Hao; Li, Haowei; Jiang, Yuguang; Wu, Meng; Zhou, Weixing; Bao, Wen

2018-06-01

As part of our efforts to find ways and means to further improve the regenerative cooling technology in scramjet, the experiments of thermo-acoustic instability dynamic characteristics of hydrocarbon fuel flowing have been conducted in horizontal circular tubes at different conditions. The experimental results indicate that there is a developing process from thermo-acoustic stability to instability. In order to have a deep understanding on the developing process of thermo-acoustic instability, the method of Multi-scale Shannon Wavelet Entropy (MSWE) based on Wavelet Transform Correlation Filter (WTCF) and Multi-Scale Shannon Entropy (MSE) is adopted in this paper. The results demonstrate that the developing process of thermo-acoustic instability from noise and weak signals is well detected by MSWE method and the differences among the stability, the developing process and the instability can be identified. These properties render the method particularly powerful for warning thermo-acoustic instability of hydrocarbon fuel flowing in scramjet cooling channels. The mass flow rate and the inlet pressure will make an influence on the developing process of the thermo-acoustic instability. The investigation on thermo-acoustic instability dynamic characteristics at supercritical pressure based on wavelet entropy method offers guidance on the control of scramjet fuel supply, which can secure stable fuel flowing in regenerative cooling system.

The Wavelet ToolKat: A set of tools for the analysis of series through wavelet transforms. Application to the channel curvature and the slope control of three free meandering rivers in the Amazon basin.

NASA Astrophysics Data System (ADS)

Vaudor, Lise; Piegay, Herve; Wawrzyniak, Vincent; Spitoni, Marie

2016-04-01

The form and functioning of a geomorphic system result from processes operating at various spatial and temporal scales. Longitudinal channel characteristics thus exhibit complex patterns which vary according to the scale of study, might be periodic or segmented, and are generally blurred by noise. Describing the intricate, multiscale structure of such signals, and identifying at which scales the patterns are dominant and over which sub-reach, could help determine at which scales they should be investigated, and provide insights into the main controlling factors. Wavelet transforms aim at describing data at multiple scales (either in time or space), and are now exploited in geophysics for the analysis of nonstationary series of data. They provide a consistent, non-arbitrary, and multiscale description of a signal's variations and help explore potential causalities. Nevertheless, their use in fluvial geomorphology, notably to study longitudinal patterns, is hindered by a lack of user-friendly tools to help understand, implement, and interpret them. We have developed a free application, The Wavelet ToolKat, designed to facilitate the use of wavelet transforms on temporal or spatial series. We illustrate its usefulness describing longitudinal channel curvature and slope of three freely meandering rivers in the Amazon basin (the Purus, Juruá and Madre de Dios rivers), using topographic data generated from NASA's Shuttle Radar Topography Mission (SRTM) in 2000. Three types of wavelet transforms are used, with different purposes. Continuous Wavelet Transforms are used to identify in a non-arbitrary way the dominant scales and locations at which channel curvature and slope vary. Cross-wavelet transforms, and wavelet coherence and phase are used to identify scales and locations exhibiting significant channel curvature and slope co-variations. Maximal Overlap Discrete Wavelet Transforms decompose data into their variations at a series of scales and are used to provide smoothed descriptions of the series at the scales deemed relevant.

A new fractional wavelet transform

NASA Astrophysics Data System (ADS)

Dai, Hongzhe; Zheng, Zhibao; Wang, Wei

2017-03-01

The fractional Fourier transform (FRFT) is a potent tool to analyze the time-varying signal. However, it fails in locating the fractional Fourier domain (FRFD)-frequency contents which is required in some applications. A novel fractional wavelet transform (FRWT) is proposed to solve this problem. It displays the time and FRFD-frequency information jointly in the time-FRFD-frequency plane. The definition, basic properties, inverse transform and reproducing kernel of the proposed FRWT are considered. It has been shown that an FRWT with proper order corresponds to the classical wavelet transform (WT). The multiresolution analysis (MRA) associated with the developed FRWT, together with the construction of the orthogonal fractional wavelets are also presented. Three applications are discussed: the analysis of signal with time-varying frequency content, the FRFD spectrum estimation of signals that involving noise, and the construction of fractional Harr wavelet. Simulations verify the validity of the proposed FRWT.

A study on multiresolution lossless video coding using inter/intra frame adaptive prediction

NASA Astrophysics Data System (ADS)

Nakachi, Takayuki; Sawabe, Tomoko; Fujii, Tetsuro

2003-06-01

Lossless video coding is required in the fields of archiving and editing digital cinema or digital broadcasting contents. This paper combines a discrete wavelet transform and adaptive inter/intra-frame prediction in the wavelet transform domain to create multiresolution lossless video coding. The multiresolution structure offered by the wavelet transform facilitates interchange among several video source formats such as Super High Definition (SHD) images, HDTV, SDTV, and mobile applications. Adaptive inter/intra-frame prediction is an extension of JPEG-LS, a state-of-the-art lossless still image compression standard. Based on the image statistics of the wavelet transform domains in successive frames, inter/intra frame adaptive prediction is applied to the appropriate wavelet transform domain. This adaptation offers superior compression performance. This is achieved with low computational cost and no increase in additional information. Experiments on digital cinema test sequences confirm the effectiveness of the proposed algorithm.

A support vector machine approach for classification of welding defects from ultrasonic signals

NASA Astrophysics Data System (ADS)

Chen, Yuan; Ma, Hong-Wei; Zhang, Guang-Ming

2014-07-01

Defect classification is an important issue in ultrasonic non-destructive evaluation. A layered multi-class support vector machine (LMSVM) classification system, which combines multiple SVM classifiers through a layered architecture, is proposed in this paper. The proposed LMSVM classification system is applied to the classification of welding defects from ultrasonic test signals. The measured ultrasonic defect echo signals are first decomposed into wavelet coefficients by the wavelet packet transform. The energy of the wavelet coefficients at different frequency channels are used to construct the feature vectors. The bees algorithm (BA) is then used for feature selection and SVM parameter optimisation for the LMSVM classification system. The BA-based feature selection optimises the energy feature vectors. The optimised feature vectors are input to the LMSVM classification system for training and testing. Experimental results of classifying welding defects demonstrate that the proposed technique is highly robust, precise and reliable for ultrasonic defect classification.

A novel neural-wavelet approach for process diagnostics and complex system modeling

NASA Astrophysics Data System (ADS)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

Interactions between Uterine EMG at Different Sites Investigated Using Wavelet Analysis: Comparison of Pregnancy and Labor Contractions

NASA Astrophysics Data System (ADS)

Hassan, Mahmoud; Terrien, Jérémy; Karlsson, Brynjar; Marque, Catherine

2010-12-01

This paper describes the use of the Morlet wavelet transform to investigate the difference in the time-frequency plane between uterine EMG signals recorded simultaneously on two different sites on women's abdomen, both during pregnancy and in labor. The methods used are wavelet transform, cross wavelet transform, phase/amplitude correlation, and phase synchronization. We computed the linear relationship and phase synchronization between uterine signals measured during the same contractions at two different sites on data obtained from women during pregnancy and labor. The results show that the Morlet wavelet transform can successfully analyze and quantify the relationship between uterine electrical activities at different sites and could be employed to investigate the evolution of uterine contraction from pregnancy to labor.

Application of Time-Frequency Domain Transform to Three-Dimensional Interpolation of Medical Images.

PubMed

Lv, Shengqing; Chen, Yimin; Li, Zeyu; Lu, Jiahui; Gao, Mingke; Lu, Rongrong

2017-11-01

Medical image three-dimensional (3D) interpolation is an important means to improve the image effect in 3D reconstruction. In image processing, the time-frequency domain transform is an efficient method. In this article, several time-frequency domain transform methods are applied and compared in 3D interpolation. And a Sobel edge detection and 3D matching interpolation method based on wavelet transform is proposed. We combine wavelet transform, traditional matching interpolation methods, and Sobel edge detection together in our algorithm. What is more, the characteristics of wavelet transform and Sobel operator are used. They deal with the sub-images of wavelet decomposition separately. Sobel edge detection 3D matching interpolation method is used in low-frequency sub-images under the circumstances of ensuring high frequency undistorted. Through wavelet reconstruction, it can get the target interpolation image. In this article, we make 3D interpolation of the real computed tomography (CT) images. Compared with other interpolation methods, our proposed method is verified to be effective and superior.

The norms and variances of the Gabor, Morlet and general harmonic wavelet functions

NASA Astrophysics Data System (ADS)

Simonovski, I.; Boltežar, M.

2003-07-01

This paper deals with certain properties of the continuous wavelet transform and wavelet functions. The norms and the spreads in time and frequency of the common Gabor and Morlet wavelet functions are presented. It is shown that the norm of the Morlet wavelet function does not satisfy the normalization condition and that the normalized Morlet wavelet function is identical to the Gabor wavelet function with the parameter σ=1. The general harmonic wavelet function is developed using frequency modulation of the Hanning and Hamming window functions. Several properties of the general harmonic wavelet function are also presented and compared to the Gabor wavelet function. The time and frequency spreads of the general harmonic wavelet function are only slightly higher than the time and frequency spreads of the Gabor wavelet function. However, the general harmonic wavelet function is simpler to use than the Gabor wavelet function. In addition, the general harmonic wavelet function can be constructed in such a way that the zero average condition is truly satisfied. The average value of the Gabor wavelet function can approach a value of zero but it cannot reach it. When calculating the continuous wavelet transform, errors occur at the start- and the end-time indexes. This is called the edge effect and is caused by the fact that the wavelet transform is calculated from a signal of finite length. In this paper, we propose a method that uses signal mirroring to reduce the errors caused by the edge effect. The success of the proposed method is demonstrated by using a simulated signal.

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

NASA Astrophysics Data System (ADS)

Sayadi, Omid; Shamsollahi, Mohammad B.

2007-12-01

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the[InlineEquation not available: see fulltext.]-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

An Algorithm Based Wavelet Entropy for Shadowing Effect of Human Detection Using Ultra-Wideband Bio-Radar

PubMed Central

Liu, Miao; Zhang, Yang; Liang, Fulai; Qi, Fugui; Lv, Hao; Wang, Jianqi; Zhang, Yang

2017-01-01

Ultra-wide band (UWB) radar for short-range human target detection is widely used to find and locate survivors in some rescue missions after a disaster. The results of the application of bistatic UWB radar for detecting multi-stationary human targets have shown that human targets close to the radar antennas are very often visible, while those farther from radar antennas are detected with less reliability. In this paper, on account of the significant difference of frequency content between the echo signal of the human target and that of noise in the shadowing region, an algorithm based on wavelet entropy is proposed to detect multiple targets. Our findings indicate that the entropy value of human targets was much lower than that of noise. Compared with the method of adaptive filtering and the energy spectrum, wavelet entropy can accurately detect the person farther from the radar antennas, and it can be employed as a useful tool in detecting multiple targets by bistatic UWB radar. PMID:28973988

An Algorithm Based Wavelet Entropy for Shadowing Effect of Human Detection Using Ultra-Wideband Bio-Radar.

PubMed

Xue, Huijun; Liu, Miao; Zhang, Yang; Liang, Fulai; Qi, Fugui; Chen, Fuming; Lv, Hao; Wang, Jianqi; Zhang, Yang

2017-09-30

Ultra-wide band (UWB) radar for short-range human target detection is widely used to find and locate survivors in some rescue missions after a disaster. The results of the application of bistatic UWB radar for detecting multi-stationary human targets have shown that human targets close to the radar antennas are very often visible, while those farther from radar antennas are detected with less reliability. In this paper, on account of the significant difference of frequency content between the echo signal of the human target and that of noise in the shadowing region, an algorithm based on wavelet entropy is proposed to detect multiple targets. Our findings indicate that the entropy value of human targets was much lower than that of noise. Compared with the method of adaptive filtering and the energy spectrum, wavelet entropy can accurately detect the person farther from the radar antennas, and it can be employed as a useful tool in detecting multiple targets by bistatic UWB radar.

Evaluation of the Use of Second Generation Wavelets in the Coherent Vortex Simulation Approach

NASA Technical Reports Server (NTRS)

Goldstein, D. E.; Vasilyev, O. V.; Wray, A. A.; Rogallo, R. S.

2000-01-01

The objective of this study is to investigate the use of the second generation bi-orthogonal wavelet transform for the field decomposition in the Coherent Vortex Simulation of turbulent flows. The performances of the bi-orthogonal second generation wavelet transform and the orthogonal wavelet transform using Daubechies wavelets with the same number of vanishing moments are compared in a priori tests using a spectral direct numerical simulation (DNS) database of isotropic turbulence fields: 256(exp 3) and 512(exp 3) DNS of forced homogeneous turbulence (Re(sub lambda) = 168) and 256(exp 3) and 512(exp 3) DNS of decaying homogeneous turbulence (Re(sub lambda) = 55). It is found that bi-orthogonal second generation wavelets can be used for coherent vortex extraction. The results of a priori tests indicate that second generation wavelets have better compression and the residual field is closer to Gaussian. However, it was found that the use of second generation wavelets results in an integral length scale for the incoherent part that is larger than that derived from orthogonal wavelets. A way of dealing with this difficulty is suggested.

A joint resonance frequency estimation and in-band noise reduction method for enhancing the detectability of bearing fault signals

NASA Astrophysics Data System (ADS)

Bozchalooi, I. Soltani; Liang, Ming

2008-05-01

The vibration signal measured from a bearing contains vital information for the prognostic and health assessment purposes. However, when bearings are installed as part of a complex mechanical system, the measured signal is often heavily clouded by various noises due to the compounded effect of interferences of other machine elements and background noises present in the measuring device. As such, reliable condition monitoring would not be possible without proper de-noising. This is particularly true for incipient bearing faults with very weak signature signals. A new de-noising scheme is proposed in this paper to enhance the vibration signals acquired from faulty bearings. This de-noising scheme features a spectral subtraction to trim down the in-band noise prior to wavelet filtering. The Gabor wavelet is used in the wavelet transform and its parameters, i.e., scale and shape factor are selected in separate steps. The proper scale is found based on a novel resonance estimation algorithm. This algorithm makes use of the information derived from the variable shaft rotational speed though such variation is highly undesirable in fault detection since it complicates the process substantially. The shape factor value is then selected by minimizing a smoothness index. This index is defined as the ratio of the geometric mean to the arithmetic mean of the wavelet coefficient moduli. De-noising results are presented for simulated signals and experimental data acquired from both normal and faulty bearings with defective outer race, inner race, and rolling element.

Application of Lipschitz Regularity and Multiscale Techniques for the Automatic Detection of Oil Spills in Synthetic Aperture Radar Imagery

NASA Astrophysics Data System (ADS)

Ajadi, O. A.; Meyer, F. J.; Tello, M.

2015-12-01

This research presents a promising new method for the detection and tracking of oil spills from Synthetic Aperture Radar (SAR) data. The method presented here combines a number of advanced image processing techniques in order to overcome some common performance limitations of SAR-based oil spill detection. Principal among these limitations are: (1) the radar cross section of the ocean surface strongly depends on wind and wave activities and is therefore highly variable; (2) the radar cross section of oil covered waters is often indistinguishable from other dark ocean features such as low wind areas or oil lookalikes, leading to ambiguities in oil spill detection. In this paper, we introduce two novel image analysis techniques to largely mitigate the aforementioned performance limitations, namely Lipschitz regularity (LR) and Wavelet transforms. We used LR, an image texture parameter akin to the slope of the local power spectrum, in our approach to mitigate these limitations. We show that the LR parameter is much less sensitive to variations of wind and waves than the original image amplitude, lending itself well for normalizing image content. Beyond its benefit for image normalization, we also show that the LR transform enhances the contrast between oil-covered and oil-free ocean surfaces and therefore improves overall spill detection performance. To calculate LR, the SAR images are decomposed using two-dimensional continuous wavelet transform (2D-CWT), which are furthermore transformed into Holder space to measure LR. Finally, we demonstrate that the implementation of wavelet transforms provide additional benefits related to the adaptive reduction of speckle noise. We show how LR and CWT are integrated into our image analysis workflow for application to oil spill detection. To describe the performance of this approach under controlled conditions, we applied our method to simulated SAR data of wind driven oceans containing oil spills of various properties. We also show applications to several real life oil spill scenarios using a series of L-band ALOS PALSAR images and X-band TerraSAR-X images acquired during the Deep Water Horizon spill in the Gulf of Mexico in 2010. From our analysis, we concluded that the LR and CWT have distinct advantages in oil spill detection and lead to high performance spill mapping results.

Robust and adaptive band-to-band image transform of UAS miniature multi-lens multispectral camera

NASA Astrophysics Data System (ADS)

Jhan, Jyun-Ping; Rau, Jiann-Yeou; Haala, Norbert

2018-03-01

Utilizing miniature multispectral (MS) or hyperspectral (HS) cameras by mounting them on an Unmanned Aerial System (UAS) has the benefits of convenience and flexibility to collect remote sensing imagery for precision agriculture, vegetation monitoring, and environment investigation applications. Most miniature MS cameras adopt a multi-lens structure to record discrete MS bands of visible and invisible information. The differences in lens distortion, mounting positions, and viewing angles among lenses mean that the acquired original MS images have significant band misregistration errors. We have developed a Robust and Adaptive Band-to-Band Image Transform (RABBIT) method for dealing with the band co-registration of various types of miniature multi-lens multispectral cameras (Mini-MSCs) to obtain band co-registered MS imagery for remote sensing applications. The RABBIT utilizes modified projective transformation (MPT) to transfer the multiple image geometry of a multi-lens imaging system to one sensor geometry, and combines this with a robust and adaptive correction (RAC) procedure to correct several systematic errors and to obtain sub-pixel accuracy. This study applies three state-of-the-art Mini-MSCs to evaluate the RABBIT method's performance, specifically the Tetracam Miniature Multiple Camera Array (MiniMCA), Micasense RedEdge, and Parrot Sequoia. Six MS datasets acquired at different target distances and dates, and locations are also applied to prove its reliability and applicability. Results prove that RABBIT is feasible for different types of Mini-MSCs with accurate, robust, and rapid image processing efficiency.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Three-dimensional object recognitions from two-dimensional images using wavelet transforms and neural networks

NASA Astrophysics Data System (ADS)

Deschenes, Sylvain; Sheng, Yunlong; Chevrette, Paul C.

1998-03-01

3D object classification from 2D IR images is shown. The wavelet transform is used for edge detection. Edge tracking is used for removing noise effectively int he wavelet transform. The invariant Fourier descriptor is used to describe the contour curves. Invariance under out-of-plane rotation is achieved by the feature space trajectory neural network working as a classifier.

Analyze the dynamic features of rat EEG using wavelet entropy.

PubMed

Feng, Zhouyan; Chen, Hang

2005-01-01

Wavelet entropy (WE), a new method of complexity measure for non-stationary signals, was used to investigate the dynamic features of rat EEGs under three vigilance states. The EEGs of the freely moving rats were recorded with implanted electrodes and were decomposed into four components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the wavelet entropy curves were calculated as a function of time. The results showed that there were significant differences among the average WEs of EEGs recorded under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement (REM) sleep. The changes of WE had different relationships with the four power components under different states. Moreover, there was evident rhythm in EEG WEs of SWS sleep for most experimental rats, which indicated a reciprocal relationship between slow waves and sleep spindles in the micro-states of SWS sleep. Therefore, WE can be used not only to distinguish the long-term changes in EEG complexity, but also to reveal the short-term changes in EEG micro-state.

MRI Volume Fusion Based on 3D Shearlet Decompositions.

PubMed

Duan, Chang; Wang, Shuai; Wang, Xue Gang; Huang, Qi Hong

2014-01-01

Nowadays many MRI scans can give 3D volume data with different contrasts, but the observers may want to view various contrasts in the same 3D volume. The conventional 2D medical fusion methods can only fuse the 3D volume data layer by layer, which may lead to the loss of interframe correlative information. In this paper, a novel 3D medical volume fusion method based on 3D band limited shearlet transform (3D BLST) is proposed. And this method is evaluated upon MRI T2* and quantitative susceptibility mapping data of 4 human brains. Both the perspective impression and the quality indices indicate that the proposed method has a better performance than conventional 2D wavelet, DT CWT, and 3D wavelet, DT CWT based fusion methods.

Wavelet-domain de-noising of OCT images of human brain malignant glioma

NASA Astrophysics Data System (ADS)

Dolganova, I. N.; Aleksandrova, P. V.; Beshplav, S.-I. T.; Chernomyrdin, N. V.; Dubyanskaya, E. N.; Goryaynov, S. A.; Kurlov, V. N.; Reshetov, I. V.; Potapov, A. A.; Tuchin, V. V.; Zaytsev, K. I.

2018-04-01

We have proposed a wavelet-domain de-noising technique for imaging of human brain malignant glioma by optical coherence tomography (OCT). It implies OCT image decomposition using the direct fast wavelet transform, thresholding of the obtained wavelet spectrum and further inverse fast wavelet transform for image reconstruction. By selecting both wavelet basis and thresholding procedure, we have found an optimal wavelet filter, which application improves differentiation of the considered brain tissue classes - i.e. malignant glioma and normal/intact tissue. Namely, it allows reducing the scattering noise in the OCT images and retaining signal decrement for each tissue class. Therefore, the observed results reveals the wavelet-domain de-noising as a prospective tool for improved characterization of biological tissue using the OCT.

Comparison of air pollution in Shanghai and Lanzhou based on wavelet transform.

PubMed

Su, Yana; Sha, Yongzhong; Zhai, Guangyu; Zong, Shengliang; Jia, Jiehua

2017-04-21

For a long-period comparative analysis of air pollution in coastal and inland cities, we analyzed the continuous Morlet wavelet transform on the time series of a 5274-day air pollution index in Shanghai and Lanzhou during 15 years and studied the multi-scale variation characteristic, main cycle, and impact factor of the air pollution time series. The analysis showed that (1) air pollution in the two cities was non-stationary and nonlinear, had multiple timescales, and exhibited the characteristics of high in winter and spring and low in summer and autumn. (2) The monthly variation in air pollution in Shanghai was not significant, whereas the seasonal variation of air pollution in Lanzhou was obvious. (3) Air pollution in Shanghai showed an ascending tendency, whereas that in Lanzhou presented a descending tendency. Overall, air pollution in Lanzhou was higher than that in Shanghai, but the situation has reversed since 2015. (4) The primary cycles of air pollution in these two cities were close, but the secondary cycles were significantly different. The aforementioned differences were mainly due to the impact of topographical and meteorological factors in Lanzhou, the weather process and the surrounding environment in Shanghai. These conclusions have reference significance for Shanghai and Lanzhou to control air pollution. The multi-timescale variation and local features of the wavelet analysis method used in this study can be applied to varied aspects of air pollution analysis. The identification of cycle characteristics and the monitoring, forecasting, and controlling of air pollution can yield valuable reference.

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.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Hui, E-mail: corinna@seu.edu.cn; Key Laboratory of Computer Network and Information Integration; Centre de Recherche en Information Biomédicale sino-français, Laboratoire International Associé, Inserm, Université de Rennes 1, Rennes 35000

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 subimagesmore » 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.« less

Multispectral code excited linear prediction coding and its application in magnetic resonance images.

PubMed

Hu, J H; Wang, Y; Cahill, P T

1997-01-01

This paper reports a multispectral code excited linear prediction (MCELP) method for the compression of multispectral images. Different linear prediction models and adaptation schemes have been compared. The method that uses a forward adaptive autoregressive (AR) model has been proven to achieve a good compromise between performance, complexity, and robustness. This approach is referred to as the MFCELP method. Given a set of multispectral images, the linear predictive coefficients are updated over nonoverlapping three-dimensional (3-D) macroblocks. Each macroblock is further divided into several 3-D micro-blocks, and the best excitation signal for each microblock is determined through an analysis-by-synthesis procedure. The MFCELP method has been applied to multispectral magnetic resonance (MR) images. To satisfy the high quality requirement for medical images, the error between the original image set and the synthesized one is further specified using a vector quantizer. This method has been applied to images from 26 clinical MR neuro studies (20 slices/study, three spectral bands/slice, 256x256 pixels/band, 12 b/pixel). The MFCELP method provides a significant visual improvement over the discrete cosine transform (DCT) based Joint Photographers Expert Group (JPEG) method, the wavelet transform based embedded zero-tree wavelet (EZW) coding method, and the vector tree (VT) coding method, as well as the multispectral segmented autoregressive moving average (MSARMA) method we developed previously.

Wavelet-based multiscale adjoint waveform-difference tomography using body and surface waves

NASA Astrophysics Data System (ADS)

Yuan, Y. O.; Simons, F. J.; Bozdag, E.

2014-12-01

We present a multi-scale scheme for full elastic waveform-difference inversion. Using a wavelet transform proves to be a key factor to mitigate cycle-skipping effects. We start with coarse representations of the seismogram to correct a large-scale background model, and subsequently explain the residuals in the fine scales of the seismogram to map the heterogeneities with great complexity. We have previously applied the multi-scale approach successfully to body waves generated in a standard model from the exploration industry: a modified two-dimensional elastic Marmousi model. With this model we explored the optimal choice of wavelet family, number of vanishing moments and decomposition depth. For this presentation we explore the sensitivity of surface waves in waveform-difference tomography. The incorporation of surface waves is rife with cycle-skipping problems compared to the inversions considering body waves only. We implemented an envelope-based objective function probed via a multi-scale wavelet analysis to measure the distance between predicted and target surface-wave waveforms in a synthetic model of heterogeneous near-surface structure. Our proposed method successfully purges the local minima present in the waveform-difference misfit surface. An elastic shallow model with 100~m in depth is used to test the surface-wave inversion scheme. We also analyzed the sensitivities of surface waves and body waves in full waveform inversions, as well as the effects of incorrect density information on elastic parameter inversions. Based on those numerical experiments, we ultimately formalized a flexible scheme to consider both body and surface waves in adjoint tomography. While our early examples are constructed from exploration-style settings, our procedure will be very valuable for the study of global network data.

A discrete polar Stockwell transform for enhanced characterization of tissue structure using MRI.

PubMed

Pridham, Glen; Steenwijk, Martijn D; Geurts, Jeroen J G; Zhang, Yunyan

2018-05-02

The purpose of this study was to present an effective algorithm for computing the discrete polar Stockwell transform (PST), investigate its unique multiscale and multi-orientation features, and explore potentially new applications including denoising and tissue segmentation. We investigated PST responses using both synthetic and MR images. Moreover, we compared the features of PST with both Gabor and Morlet wavelet transforms, and compared the PST with two wavelet approaches for denoising using MRI. Using a synthetic image, we also tested the edge effect of PST through signal-padding. Then, we constructed a partially supervised classifier using radial, marginal PST spectra of T2-weighted MRI, acquired from postmortem brains with multiple sclerosis. The classification involved three histology-verified tissue types: normal appearing white matter (NAWM), lesion, or other, along with 5-fold cross-validation. The PST generated a series of images with varying orientations or rotation-invariant scales. Radial frequencies highlighted image structures of different size, and angular frequencies enhanced structures by orientation. Signal-padding helped suppress boundary artifacts but required attention to incidental artifacts. In comparison, the Gabor transform produced more redundant images and the wavelet spectra appeared less spatially smooth than the PST. In addition, the PST demonstrated lower root-mean-square errors than other transforms in denoising and achieved a 93% accuracy for NAWM pixels (296/317), and 88% accuracy for lesion pixels (165/188) in MRI segmentation. The PST is a unique local spectral density-assessing tool which is sensitive to both structure orientations and scales. This may facilitate multiple new applications including advanced characterization of tissue structure in standard MRI. © 2018 International Society for Magnetic Resonance in Medicine.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

PubMed

Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.

Research of generalized wavelet transformations of Haar correctness in remote sensing of the Earth

NASA Astrophysics Data System (ADS)

Kazaryan, Maretta; Shakhramanyan, Mihail; Nedkov, Roumen; Richter, Andrey; Borisova, Denitsa; Stankova, Nataliya; Ivanova, Iva; Zaharinova, Mariana

2017-10-01

In this paper, Haar's generalized wavelet functions are applied to the problem of ecological monitoring by the method of remote sensing of the Earth. We study generalized Haar wavelet series and suggest the use of Tikhonov's regularization method for investigating them for correctness. In the solution of this problem, an important role is played by classes of functions that were introduced and described in detail by I.M. Sobol for studying multidimensional quadrature formulas and it contains functions with rapidly convergent series of wavelet Haar. A theorem on the stability and uniform convergence of the regularized summation function of the generalized wavelet-Haar series of a function from this class with approximate coefficients is proved. The article also examines the problem of using orthogonal transformations in Earth remote sensing technologies for environmental monitoring. Remote sensing of the Earth allows to receive from spacecrafts information of medium, high spatial resolution and to conduct hyperspectral measurements. Spacecrafts have tens or hundreds of spectral channels. To process the images, the device of discrete orthogonal transforms, and namely, wavelet transforms, was used. The aim of the work is to apply the regularization method in one of the problems associated with remote sensing of the Earth and subsequently to process the satellite images through discrete orthogonal transformations, in particular, generalized Haar wavelet transforms. General methods of research. In this paper, Tikhonov's regularization method, the elements of mathematical analysis, the theory of discrete orthogonal transformations, and methods for decoding of satellite images are used. Scientific novelty. The task of processing of archival satellite snapshots (images), in particular, signal filtering, was investigated from the point of view of an incorrectly posed problem. The regularization parameters for discrete orthogonal transformations were determined.

An introduction to wavelet analysis in oceanography and meteorology - With application to the dispersion of Yanai waves

NASA Technical Reports Server (NTRS)

Meyers, Steven D.; Kelly, B. G.; O'Brien, J. J.

1993-01-01

Wavelet analysis is a relatively new technique that is an important addition to standard signal analysis methods. Unlike Fourier analysis that yields an average amplitude and phase for each harmonic in a dataset, the wavelet transform produces an instantaneous estimate or local value for the amplitude and phase of each harmonic. This allows detailed study of nonstationary spatial or time-dependent signal characteristics. The wavelet transform is discussed, examples are given, and some methods for preprocessing data for wavelet analysis are compared. By studying the dispersion of Yanai waves in a reduced gravity equatorial model, the usefulness of the transform is demonstrated. The group velocity is measured directly over a finite range of wavenumbers by examining the time evolution of the transform. The results agree well with linear theory at higher wavenumber but the measured group velocity is reduced at lower wavenumbers, possibly due to interaction with the basin boundaries.

Multispectral Image Compression Based on DSC Combined with CCSDS-IDC

PubMed Central

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches. PMID:25110741

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Multispectral image compression based on DSC combined with CCSDS-IDC.

PubMed

Li, Jin; Xing, Fei; Sun, Ting; You, Zheng

2014-01-01

Remote sensing multispectral image compression encoder requires low complexity, high robust, and high performance because it usually works on the satellite where the resources, such as power, memory, and processing capacity, are limited. For multispectral images, the compression algorithms based on 3D transform (like 3D DWT, 3D DCT) are too complex to be implemented in space mission. In this paper, we proposed a compression algorithm based on distributed source coding (DSC) combined with image data compression (IDC) approach recommended by CCSDS for multispectral images, which has low complexity, high robust, and high performance. First, each band is sparsely represented by DWT to obtain wavelet coefficients. Then, the wavelet coefficients are encoded by bit plane encoder (BPE). Finally, the BPE is merged to the DSC strategy of Slepian-Wolf (SW) based on QC-LDPC by deep coupling way to remove the residual redundancy between the adjacent bands. A series of multispectral images is used to test our algorithm. Experimental results show that the proposed DSC combined with the CCSDS-IDC (DSC-CCSDS)-based algorithm has better compression performance than the traditional compression approaches.

Differential neural responses to acupuncture revealed by MEG using wavelet-based time-frequency analysis: a pilot study.

PubMed

You, Youbo; Bai, Lijun; Dai, Ruwei; Xue, Ting; Zhong, Chongguang; Feng, Yuanyuan; Wang, Hu; Liu, Zhenyu; Tian, Jie

2011-01-01

Acupoint specificity, lying at the core of the Traditional Chinese Medicine, still faces many controversies. As previous neuroimaging studies on acupuncture mainly adopted relatively low time-resolution functional magnetic resonance imaging (fMRI) technology and inappropriate block-designed experimental paradigm due to sustained effect, in the current study, we employed a single block-designed paradigm together with high temporal-resolution magnetoencephalography (MEG) technology. We applied time-frequency analysis based upon Morlet wavelet transforming approach to detect differential oscillatory brain dynamics induced by acupuncture at Stomach Meridian 36 (ST36) using a nearby nonacupoint (NAP) as control condition. We observed that frequency power changes were mainly restricted to delta band for both ST36 group and NAP group. Consistently increased delta band power in contralateral temporal regions and decreased power in the counterparts of ipsilateral hemisphere were detected following stimulation at ST36 on the right leg. Compared with ST36, no significant delta ranges were found in temporal regions in NAP group, illustrating different oscillatory brain patterns. Our results may provide additional evidence to support the specificity of acupuncture modulation effects.

Fast frequency domain method to detect skew in a document image

NASA Astrophysics Data System (ADS)

Mehta, Sunita; Walia, Ekta; Dutta, Maitreyee

2015-12-01

In this paper, a new fast frequency domain method based on Discrete Wavelet Transform and Fast Fourier Transform has been implemented for the determination of the skew angle in a document image. Firstly, image size reduction is done by using two-dimensional Discrete Wavelet Transform and then skew angle is computed using Fast Fourier Transform. Skew angle error is almost negligible. The proposed method is experimented using a large number of documents having skew between -90° and +90° and results are compared with Moments with Discrete Wavelet Transform method and other commonly used existing methods. It has been determined that this method works more efficiently than the existing methods. Also, it works with typed, picture documents having different fonts and resolutions. It overcomes the drawback of the recently proposed method of Moments with Discrete Wavelet Transform that does not work with picture documents.

Anisotropic analysis of trabecular architecture in human femur bone radiographs using quaternion wavelet transforms.

PubMed

Sangeetha, S; Sujatha, C M; Manamalli, D

2014-01-01

In this work, anisotropy of compressive and tensile strength regions of femur trabecular bone are analysed using quaternion wavelet transforms. The normal and abnormal femur trabecular bone radiographic images are considered for this study. The sub-anatomic regions, which include compressive and tensile regions, are delineated using pre-processing procedures. These delineated regions are subjected to quaternion wavelet transforms and statistical parameters are derived from the transformed images. These parameters are correlated with apparent porosity, which is derived from the strength regions. Further, anisotropy is also calculated from the transformed images and is analyzed. Results show that the anisotropy values derived from second and third phase components of quaternion wavelet transform are found to be distinct for normal and abnormal samples with high statistical significance for both compressive and tensile regions. These investigations demonstrate that architectural anisotropy derived from QWT analysis is able to differentiate normal and abnormal samples.

The demodulated band transform

PubMed Central

Kovach, Christopher K.; Gander, Phillip E.

2016-01-01

Background Windowed Fourier decompositions (WFD) are widely used in measuring stationary and non-stationary spectral phenomena and in describing pairwise relationships among multiple signals. Although a variety of WFDs see frequent application in electrophysiological research, including the short-time Fourier transform, continuous wavelets, band-pass filtering and multitaper-based approaches, each carries certain drawbacks related to computational efficiency and spectral leakage. This work surveys the advantages of a WFD not previously applied in electrophysiological settings. New Methods A computationally efficient form of complex demodulation, the demodulated band transform (DBT), is described. Results DBT is shown to provide an efficient approach to spectral estimation with minimal susceptibility to spectral leakage. In addition, it lends itself well to adaptive filtering of non-stationary narrowband noise. Comparison with existing methods A detailed comparison with alternative WFDs is offered, with an emphasis on the relationship between DBT and Thomson's multitaper. DBT is shown to perform favorably in combining computational efficiency with minimal introduction of spectral leakage. Conclusion DBT is ideally suited to efficient estimation of both stationary and non-stationary spectral and cross-spectral statistics with minimal susceptibility to spectral leakage. These qualities are broadly desirable in many settings. PMID:26711370

Edge detection based on adaptive threshold b-spline wavelet for optical sub-aperture measuring

NASA Astrophysics Data System (ADS)

Zhang, Shiqi; Hui, Mei; Liu, Ming; Zhao, Zhu; Dong, Liquan; Liu, Xiaohua; Zhao, Yuejin

2015-08-01

In the research of optical synthetic aperture imaging system, phase congruency is the main problem and it is necessary to detect sub-aperture phase. The edge of the sub-aperture system is more complex than that in the traditional optical imaging system. And with the existence of steep slope for large-aperture optical component, interference fringe may be quite dense when interference imaging. Deep phase gradient may cause a loss of phase information. Therefore, it's urgent to search for an efficient edge detection method. Wavelet analysis as a powerful tool is widely used in the fields of image processing. Based on its properties of multi-scale transform, edge region is detected with high precision in small scale. Longing with the increase of scale, noise is reduced in contrary. So it has a certain suppression effect on noise. Otherwise, adaptive threshold method which sets different thresholds in various regions can detect edge points from noise. Firstly, fringe pattern is obtained and cubic b-spline wavelet is adopted as the smoothing function. After the multi-scale wavelet decomposition of the whole image, we figure out the local modulus maxima in gradient directions. However, it also contains noise, and thus adaptive threshold method is used to select the modulus maxima. The point which greater than threshold value is boundary point. Finally, we use corrosion and expansion deal with the resulting image to get the consecutive boundary of image.

Comparison of LSS-IV and LISS-III+LISS-IV merged data for classification of crops

NASA Astrophysics Data System (ADS)

Hebbar, R.; Sesha Sai, M. V. R.

2014-11-01

Resourcesat-1 satellite with its unique capability of simultaneous acquisition of multispectral images at different spatial resolutions (AWiFS, LISS-III and LISS-IV MX / Mono) has immense potential for crop inventory. The present study was carried for selection of suitable LISS-IV MX band for data fusion and its evaluation for delineation different crops in a multi-cropped area. Image fusion techniques namely intensity hue saturation (IHS), principal component analysis (PCA), brovey, high pass filter (HPF) and wavelet methods were used for merging LISS-III and LISS-IV Mono data. The merged products were evaluated visually and through universal image quality index, ERGAS and classification accuracy. The study revealed that red band of LISS-IV MX data was found to be optimal band for merging with LISS-III data in terms of maintaining both spectral and spatial information and thus, closely matching with multispectral LISS-IVMX data. Among the five data fusion techniques, wavelet method was found to be superior in retaining image quality and higher classification accuracy compared to commonly used methods of IHS, PCA and Brovey. The study indicated that LISS-IV data in mono mode with wider swath of 70 km could be exploited in place of 24km LISS-IVMX data by selection of appropriate fusion techniques by acquiring monochromatic data in the red band.

Speckle noise reduction in SAR images ship detection

NASA Astrophysics Data System (ADS)

Yuan, Ji; Wu, Bin; Yuan, Yuan; Huang, Qingqing; Chen, Jingbo; Ren, Lin

2012-09-01

At present, there are two types of method to detect ships in SAR images. One is a direct detection type, detecting ships directly. The other is an indirect detection type. That is, it firstly detects ship wakes, and then seeks ships around wakes. The two types all effect by speckle noise. In order to improve the accuracy of ship detection and get accurate ship and ship wakes parameters, such as ship length, ship width, ship area, the angle of ship wakes and ship outline from SAR images, it is extremely necessary to remove speckle noise in SAR images before data used in various SAR images ship detection. The use of speckle noise reduction filter depends on the specification for a particular application. Some common filters are widely used in speckle noise reduction, such as the mean filter, the median filter, the lee filter, the enhanced lee filter, the Kuan filter, the frost filter, the enhanced frost filter and gamma filter, but these filters represent some disadvantages in SAR image ship detection because of the various types of ship. Therefore, a mathematical function known as the wavelet transform and multi-resolution analysis were used to localize an SAR ocean image into different frequency components or useful subbands, and effectively reduce the speckle in the subbands according to the local statistics within the bands. Finally, the analysis of the statistical results are presented, which demonstrates the advantages and disadvantages of using wavelet shrinkage techniques over standard speckle filters.

Robust image watermarking using DWT and SVD for copyright protection

NASA Astrophysics Data System (ADS)

Harjito, Bambang; Suryani, Esti

2017-02-01

The Objective of this paper is proposed a robust combined Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The RGB image is called a cover medium, and watermark image is converted into gray scale. Then, they are transformed using DWT so that they can be split into several subbands, namely sub-band LL2, LH2, HL2. The watermark image embeds into the cover medium on sub-band LL2. This scheme aims to obtain the higher robustness level than the previous method which performs of SVD matrix factorization image for copyright protection. The experiment results show that the proposed method has robustness against several image processing attacks such as Gaussian, Poisson and Salt and Pepper Noise. In these attacks, noise has average Normalized Correlation (NC) values of 0.574863 0.889784, 0.889782 respectively. The watermark image can be detected and extracted.

"NONLINEAR DYNAMIC SYSTEMS RESPONSE TO NON-STATIONARY EXCITATION USING THE WAVELET TRANSFORM"

DOE Office of Scientific and Technical Information (OSTI.GOV)

SPANOS, POL D.

2006-01-15

The objective of this research project has been the development of techniques for estimating the power spectra of stochastic processes using wavelet transform, and the development of related techniques for determining the response of linear/nonlinear systems to excitations which are described via the wavelet transform. Both of the objectives have been achieved, and the research findings have been disseminated in papers in archival journals and technical conferences.

A novel compensation method of insertion losses for wavelet inverse-transform processors using surface acoustic wave devices.

PubMed

Lu, Wenke; Zhu, Changchun

2011-11-01

The objective of this research was to investigate the possibility of compensating for the insertion losses of the wavelet inverse-transform processors using SAW devices. The motivation for this work was prompted by the processors which are of large insertion losses. In this paper, the insertion losses are the key problem of the wavelet inverse-transform processors using SAW devices. A novel compensation method of the insertion losses is achieved in this study. When the output ends of the wavelet inverse-transform processors are respectively connected to the amplifiers, their insertion losses can be compensated for. The bandwidths of the amplifiers and their adjustment method are also given in this paper. © 2011 American Institute of Physics

ECG Signal Analysis and Arrhythmia Detection using Wavelet Transform

NASA Astrophysics Data System (ADS)

Kaur, Inderbir; Rajni, Rajni; Marwaha, Anupma

2016-12-01

Electrocardiogram (ECG) is used to record the electrical activity of the heart. The ECG signal being non-stationary in nature, makes the analysis and interpretation of the signal very difficult. Hence accurate analysis of ECG signal with a powerful tool like discrete wavelet transform (DWT) becomes imperative. In this paper, ECG signal is denoised to remove the artifacts and analyzed using Wavelet Transform to detect the QRS complex and arrhythmia. This work is implemented in MATLAB software for MIT/BIH Arrhythmia database and yields the sensitivity of 99.85 %, positive predictivity of 99.92 % and detection error rate of 0.221 % with wavelet transform. It is also inferred that DWT outperforms principle component analysis technique in detection of ECG signal.

Estimation of effect of hydrogen on the parameters of magnetoacoustic emission signals

NASA Astrophysics Data System (ADS)

Skalskyi, Valentyn; Stankevych, Olena; Dubytskyi, Olexandr

2018-05-01

The features of the magnetoacoustic emission (MAE) signals during magnetization of structural steels with the different degree of hydrogenating were investigated by the wavelet transform. The dominant frequency ranges of MAE signals for the different magnetic field strength were determined using Discrete Wavelet Transform (DWT), and the energy and spectral parameters of MAE signals were determined using Continuous Wavelet Transform (CWT). The characteristic differences of the local maximums of signals according to energy, bandwidth, duration and frequency were found. The methodology of estimation of state of local degradation of materials by parameters of wavelet transform of MAE signals was proposed. This methodology was approbated for investigate of state of long-time exploitations structural steels of oil and gas pipelines.

Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

NASA Astrophysics Data System (ADS)

Abhayaratne, Charith

2011-07-01

Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.

Remote sensing of soil organic matter of farmland with hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Wang, Lei; Yang, Guijun; Zhang, Liyan

2017-10-01

Monitoring soil organic matter (SOM) of cultivated land quantitively and mastering its spatial change are helpful for fertility adjustment and sustainable development of agriculture. The study aimed to analyze the response between SOM and reflectivity of hyperspectral image with different pixel size and develop the optimal model of estimating SOM with imaging spectral technology. The wavelet transform method was used to analyze the correlation between the hyperspectral reflectivity and SOM. Then the optimal pixel size and sensitive wavelet feature scale were screened to develop the inversion model of SOM. Result showed that wavelet transform of soil hyperspectrum was help to improve the correlation between the wavelet features and SOM. In the visible wavelength range, the susceptible wavelet features of SOM mainly concentrated 460 603 nm. As the wavelength increased, the wavelet scale corresponding correlation coefficient increased maximum and then gradually decreased. In the near infrared wavelength range, the susceptible wavelet features of SOM mainly concentrated 762 882 nm. As the wavelength increased, the wavelet scale gradually decreased. The study developed multivariate model of continuous wavelet transforms by the method of stepwise linear regression (SLR). The CWT-SLR models reached higher accuracies than those of univariate models. With the resampling scale increasing, the accuracies of CWT-SLR models gradually increased, while the determination coefficients (R2) fluctuated from 0.52 to 0.59. The R2 of 5*5 scale reached highest (0.5954), while the RMSE reached lowest (2.41 g/kg). It indicated that multivariate model based on continuous wavelet transform had better ability for estimating SOM than univariate model.

Fair comparison of complexity between a multi-band CAP and DMT for data center interconnects.

PubMed

Wei, J L; Sanchez, C; Giacoumidis, E

2017-10-01

We present, to the best of our knowledge, the first known detailed analysis and fair comparison of complexity of a 56 Gb/s multi-band carrierless amplitude and phase (CAP) and discrete multi-tone (DMT) over 80 km dispersion compensation fiber-free single-mode fiber links based on intensity modulation and direct detection for data center interconnects. We show that the matched finite impulse response filters and inverse fast Fourier transform (IFFT)/FFT take the majority of the complexity of the multi-band CAP and DMT, respectively. The choice of the multi-band CAP sub-band count and the DMT IFFT/FFT size makes significant impact on the system complexity or performance, and trade-off must be considered.

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 positives voxels. PMID:21034833

The study of evolution and depression of the alpha-rhythm in the human brain EEG by means of wavelet-based methods

NASA Astrophysics Data System (ADS)

Runnova, A. E.; Zhuravlev, M. O.; Khramova, M. V.; Pysarchik, A. N.

2017-04-01

We study the appearance, development and depression of the alpha-rhythm in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. The new method based on continuous wavelet transform allows to estimate the energy contribution of various components, including the alpha rhythm, in the general dynamics of the electrical activity of the projections of various areas of the brain. The decision-making process by observe ambiguous images is characterized by specific oscillatory alfa-rhytm patterns in the multi-channel EEG data. We have shown the repeatability of detected principles of the alpha-rhythm evolution in a data of group of 12 healthy male volunteers.

The signal extraction of fetal heart rate based on wavelet transform and BP neural network

NASA Astrophysics Data System (ADS)

Yang, Xiao Hong; Zhang, Bang-Cheng; Fu, Hu Dai

2005-04-01

This paper briefly introduces the collection and recognition of bio-medical signals, designs the method to collect FM signals. A detailed discussion on the system hardware, structure and functions is also given. Under LabWindows/CVI,the hardware and the driver do compatible, the hardware equipment work properly actively. The paper adopts multi threading technology for real-time analysis and makes use of latency time of CPU effectively, expedites program reflect speed, improves the program to perform efficiency. One threading is collecting data; the other threading is analyzing data. Using the method, it is broaden to analyze the signal in real-time. Wavelet transform to remove the main interference in the FM and by adding time-window to recognize with BP network; Finally the results of collecting signals and BP networks are discussed. 8 pregnant women's signals of FM were collected successfully by using the sensor. The correctness rate of BP network recognition is about 83.3% by using the above measure.

Automated corresponding point candidate selection for image registration using wavelet transformation neurla network with rotation invariant inputs and context information about neighboring candidates

NASA Astrophysics Data System (ADS)

Okumura, Hiroshi; Suezaki, Masashi; Sueyasu, Hideki; Arai, Kohei

2003-03-01

An automated method that can select corresponding point candidates is developed. This method has the following three features: 1) employment of the RIN-net for corresponding point candidate selection; 2) employment of multi resolution analysis with Haar wavelet transformation for improvement of selection accuracy and noise tolerance; 3) employment of context information about corresponding point candidates for screening of selected candidates. Here, the 'RIN-net' means the back-propagation trained feed-forward 3-layer artificial neural network that feeds rotation invariants as input data. In our system, pseudo Zernike moments are employed as the rotation invariants. The RIN-net has N x N pixels field of view (FOV). Some experiments are conducted to evaluate corresponding point candidate selection capability of the proposed method by using various kinds of remotely sensed images. The experimental results show the proposed method achieves fewer training patterns, less training time, and higher selection accuracy than conventional method.

VStar: Variable star data visualization and analysis tool

NASA Astrophysics Data System (ADS)

VStar Team

2014-07-01

VStar is a multi-platform, easy-to-use variable star data visualization and analysis tool. Data for a star can be read from the AAVSO (American Association of Variable Star Observers) database or from CSV and TSV files. VStar displays light curves and phase plots, can produce a mean curve, and analyzes time-frequency with Weighted Wavelet Z-Transform. It offers tools for period analysis, filtering, and other functions.

Investigation of Parallax Issues for Multi-Lens Multispectral Camera Band Co-Registration

NASA Astrophysics Data System (ADS)

Jhan, J. P.; Rau, J. Y.; Haala, N.; Cramer, M.

2017-08-01

The multi-lens multispectral cameras (MSCs), such as Micasense Rededge and Parrot Sequoia, can record multispectral information by each separated lenses. With their lightweight and small size, which making they are more suitable for mounting on an Unmanned Aerial System (UAS) to collect high spatial images for vegetation investigation. However, due to the multi-sensor geometry of multi-lens structure induces significant band misregistration effects in original image, performing band co-registration is necessary in order to obtain accurate spectral information. A robust and adaptive band-to-band image transform (RABBIT) is proposed to perform band co-registration of multi-lens MSCs. First is to obtain the camera rig information from camera system calibration, and utilizes the calibrated results for performing image transformation and lens distortion correction. Since the calibration uncertainty leads to different amount of systematic errors, the last step is to optimize the results in order to acquire a better co-registration accuracy. Due to the potential issues of parallax that will cause significant band misregistration effects when images are closer to the targets, four datasets thus acquired from Rededge and Sequoia were applied to evaluate the performance of RABBIT, including aerial and close-range imagery. From the results of aerial images, it shows that RABBIT can achieve sub-pixel accuracy level that is suitable for the band co-registration purpose of any multi-lens MSC. In addition, the results of close-range images also has same performance, if we focus on the band co-registration on specific target for 3D modelling, or when the target has equal distance to the camera.

Directional dual-tree rational-dilation complex wavelet transform.

PubMed

Serbes, Gorkem; Gulcur, Halil Ozcan; Aydin, Nizamettin

2014-01-01

Dyadic discrete wavelet transform (DWT) has been used successfully in processing signals having non-oscillatory transient behaviour. However, due to the low Q-factor property of their wavelet atoms, the dyadic DWT is less effective in processing oscillatory signals such as embolic signals (ESs). ESs are extracted from quadrature Doppler signals, which are the output of Doppler ultrasound systems. In order to process ESs, firstly, a pre-processing operation known as phase filtering for obtaining directional signals from quadrature Doppler signals must be employed. Only then, wavelet based methods can be applied to these directional signals for further analysis. In this study, a directional dual-tree rational-dilation complex wavelet transform, which can be applied directly to quadrature signals and has the ability of extracting directional information during analysis, is introduced.

Unified selective sorting approach to analyse multi-electrode extracellular data

NASA Astrophysics Data System (ADS)

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-06-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators.

Unified selective sorting approach to analyse multi-electrode extracellular data

PubMed Central

Veerabhadrappa, R.; Lim, C. P.; Nguyen, T. T.; Berk, M.; Tye, S. J.; Monaghan, P.; Nahavandi, S.; Bhatti, A.

2016-01-01

Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. PMID:27339770

Variations in motor unit recruitment patterns occur within and between muscles in the running rat (Rattus norvegicus).

PubMed

Hodson-Tole, E F; Wakeling, J M

2007-07-01

Motor units are generally considered to follow a set, orderly pattern of recruitment within each muscle with activation occurring in the slowest through to the fastest units. A growing body of evidence, however, suggests that recruitment patterns may not always follow such an orderly sequence. Here we investigate whether motor unit recruitment patterns vary within and between the ankle extensor muscles of the rat running at 40 cm s(-1) on a level treadmill. In the past it has been difficult to quantify motor unit recruitment patterns during locomotion; however, recent application of wavelet analysis techniques has made such detailed analysis of motor unit recruitment possible. Here we present methods for quantifying the interplay of fast and slow motor unit recruitment based on their myoelectric signals. Myoelectric data were collected from soleus, plantaris and medial gastrocnemius muscles representing populations of slow, mixed and fast fibres, respectively, and providing a good opportunity to relate myoelectric frequency content to motor unit recruitment patterns. Following wavelet transformation, principal component analysis quantified signal intensity and relative frequency content. Significant differences in signal frequency content occurred between different time points within a stride (P<0.001). We optimised high- and low-frequency wavelets to the major signals from the fast and slow motor units. The goodness-of-fit of the optimised wavelets to the signal intensity was high for all three muscles (r2>0.98). The low-frequency band had a significantly better fit to signals from the soleus muscle (P<0.001), while the high-frequency band had a significantly better fit to the medial gastrocnemius (P<0.001).

Using discrete wavelet transform features to discriminate between noise and phases in seismic waveforms

NASA Astrophysics Data System (ADS)

Forrest, R.; Ray, J.; Hansen, C. W.

2017-12-01

Currently, simple polarization metrics such as the horizontal-to-vertical ratio are used to discriminate between noise and various phases in three-component seismic waveform data collected at regional distances. Accurately establishing the identity and arrival of these waves in adverse signal-to-noise environments is helpful in detecting and locating the seismic events. In this work, we explore the use of multiresolution decompositions to discriminate between noise and event arrivals. A segment of the waveform lying inside a time-window that spans the coda of an arrival is subjected to a discrete wavelet decomposition. Multi-resolution classification features as well as statistical tests are derived from these wavelet decomposition quantities to quantify their discriminating power. Furthermore, we move to streaming data and address the problem of false positives by introducing ensembles of classifiers. We describe in detail results of these methods tuned from data obtained from Coronel Fontana, Argentina (CFAA), as well as Stephens Creek, Australia (STKA). Acknowledgement: Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA-0003525.

Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network

NASA Astrophysics Data System (ADS)

Yu, Yali; Wang, Mengxia; Lima, Dimas

2018-04-01

In order to develop a novel alcoholism detection method, we proposed a magnetic resonance imaging (MRI)-based computer vision approach. We first use contrast equalization to increase the contrast of brain slices. Then, we perform Haar wavelet transform and principal component analysis. Finally, we use back propagation neural network (BPNN) as the classification tool. Our method yields a sensitivity of 81.71±4.51%, a specificity of 81.43±4.52%, and an accuracy of 81.57±2.18%. The Haar wavelet gives better performance than db4 wavelet and sym3 wavelet.

Research on artificial neural network intrusion detection photochemistry based on the improved wavelet analysis and transformation

NASA Astrophysics Data System (ADS)

Li, Hong; Ding, Xue

2017-03-01

This paper combines wavelet analysis and wavelet transform theory with artificial neural network, through the pretreatment on point feature attributes before in intrusion detection, to make them suitable for improvement of wavelet neural network. The whole intrusion classification model gets the better adaptability, self-learning ability, greatly enhances the wavelet neural network for solving the problem of field detection invasion, reduces storage space, contributes to improve the performance of the constructed neural network, and reduces the training time. Finally the results of the KDDCup99 data set simulation experiment shows that, this method reduces the complexity of constructing wavelet neural network, but also ensures the accuracy of the intrusion classification.

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

Sparsity prediction and application to a new steganographic technique

NASA Astrophysics Data System (ADS)

Phillips, David; Noonan, Joseph

2004-10-01

Steganography is a technique of embedding information in innocuous data such that only the innocent data is visible. The wavelet transform lends itself to image steganography because it generates a large number of coefficients representing the information in the image. Altering a small set of these coefficients allows embedding of information (payload) into an image (cover) without noticeably altering the original image. We propose a novel, dual-wavelet steganographic technique, using transforms selected such that the transform of the cover image has low sparsity, while the payload transform has high sparsity. Maximizing the sparsity of the payload transform reduces the amount of information embedded in the cover, and minimizing the sparsity of the cover increases the locations that can be altered without significantly altering the image. Making this system effective on any given image pair requires a metric to indicate the best (maximum sparsity) and worst (minimum sparsity) wavelet transforms to use. This paper develops the first stage of this metric, which can predict, averaged across many wavelet families, which of two images will have a higher sparsity. A prototype implementation of the dual-wavelet system as a proof of concept is also developed.

Wavelet Transforms in Parallel Image Processing

DTIC Science & Technology

1994-01-27

NUMBER OF PAGES Object Segmentation, Texture Segmentation, Image Compression, Image 137 Halftoning , Neural Network, Parallel Algorithms, 2D and 3D...Vector Quantization of Wavelet Transform Coefficients ........ ............................. 57 B.1.f Adaptive Image Halftoning based on Wavelet...application has been directed to the adaptive image halftoning . The gray information at a pixel, including its gray value and gradient, is represented by

Application of wavelet transformation and adaptive neighborhood based modified backpropagation (ANMBP) for classification of brain cancer

NASA Astrophysics Data System (ADS)

Werdiningsih, Indah; Zaman, Badrus; Nuqoba, Barry

2017-08-01

This paper presents classification of brain cancer using wavelet transformation and Adaptive Neighborhood Based Modified Backpropagation (ANMBP). Three stages of the processes, namely features extraction, features reduction, and classification process. Wavelet transformation is used for feature extraction and ANMBP is used for classification process. The result of features extraction is feature vectors. Features reduction used 100 energy values per feature and 10 energy values per feature. Classifications of brain cancer are normal, alzheimer, glioma, and carcinoma. Based on simulation results, 10 energy values per feature can be used to classify brain cancer correctly. The correct classification rate of proposed system is 95 %. This research demonstrated that wavelet transformation can be used for features extraction and ANMBP can be used for classification of brain cancer.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu Shouxian; Wang Detian; Li Tao

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 bymore » a plate-impact experiment data. The results illustrate that the new method is automatic and adequate for analysis of PDV data.« less

ECG denoising with adaptive bionic wavelet transform.

PubMed

Sayadi, Omid; Shamsollahi, Mohammad Bagher

2006-01-01

In this paper a new ECG denoising scheme is proposed using a novel adaptive wavelet transform, named bionic wavelet transform (BWT), which had been first developed based on a model of the active auditory system. There has been some outstanding features with the BWT such as nonlinearity, high sensitivity and frequency selectivity, concentrated energy distribution and its ability to reconstruct signal via inverse transform but the most distinguishing characteristic of BWT is that its resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying a new threshold value, one can handle ECG denoising with results comparing to those of wavelet transform (WT). Preliminary tests of BWT application to ECG denoising were constructed on the signals of MIT-BIH database which showed high performance of noise reduction.

An Investigation on Micro-Raman Spectra and Wavelet Data Analysis for Pemphigus Vulgaris Follow-up Monitoring.

PubMed

Camerlingo, Carlo; Zenone, Flora; Perna, Giuseppe; Capozzi, Vito; Cirillo, Nicola; Gaeta, Giovanni Maria; Lepore, Maria

2008-06-01

A wavelet multi-component decomposition algorithm has been used for data analysis of micro-Raman spectra of blood serum samples from patients affected by pemphigus vulgaris at different stages. Pemphigus is a chronic, autoimmune, blistering disease of the skin and mucous membranes with a potentially fatal outcome. Spectra were measured by means of a Raman confocal microspectrometer apparatus using the 632.8 nm line of a He-Ne laser source. A discrete wavelet transform decomposition method has been applied to the recorded Raman spectra in order to overcome problems related to low-level signals and the presence of noise and background components due to light scattering and fluorescence. This numerical data treatment can automatically extract quantitative information from the Raman spectra and makes more reliable the data comparison. Even if an exhaustive investigation has not been done in this work, the feasibility of the follow-up monitoring of pemphigus vulgaris pathology has been clearly proved with useful implications for the clinical applications.

An Investigation on Micro-Raman Spectra and Wavelet Data Analysis for Pemphigus Vulgaris Follow-up Monitoring

PubMed Central

Camerlingo, Carlo; Zenone, Flora; Perna, Giuseppe; Capozzi, Vito; Cirillo, Nicola; Gaeta, Giovanni Maria; Lepore, Maria

2008-01-01

A wavelet multi-component decomposition algorithm has been used for data analysis of micro-Raman spectra of blood serum samples from patients affected by pemphigus vulgaris at different stages. Pemphigus is a chronic, autoimmune, blistering disease of the skin and mucous membranes with a potentially fatal outcome. Spectra were measured by means of a Raman confocal microspectrometer apparatus using the 632.8 nm line of a He-Ne laser source. A discrete wavelet transform decomposition method has been applied to the recorded Raman spectra in order to overcome problems related to low-level signals and the presence of noise and background components due to light scattering and fluorescence. This numerical data treatment can automatically extract quantitative information from the Raman spectra and makes more reliable the data comparison. Even if an exhaustive investigation has not been done in this work, the feasibility of the follow-up monitoring of pemphigus vulgaris pathology has been clearly proved with useful implications for the clinical applications. PMID:27879899

Determination of phase from the ridge of CWT using generalized Morse wavelet

NASA Astrophysics Data System (ADS)

Kocahan, Ozlem; Tiryaki, Erhan; Coskun, Emre; Ozder, Serhat

2018-03-01

The selection of wavelet is an important step in order to determine the phase from the fringe patterns. In the present work, a new wavelet for phase retrieval from the ridge of continuous wavelet transform (CWT) is presented. The phase distributions have been extracted from the optical fringe pattern by choosing the zero order generalized morse wavelet (GMW) as a mother wavelet. The aim of the study is to reveal the ways in which the two varying parameters of GMW affect the phase calculation. To show the validity of this method, an experimental study has been conducted by using the diffraction phase microscopy (DPM) setup; consequently, the profiles of red blood cells have been retrieved. The results for the CWT ridge technique with GMW have been compared with the results for the Morlet wavelet and the Paul wavelet; the results are almost identical for Paul and zero order GMW because of their degree of freedom. Also, for further discussion, the Fourier transform and the Stockwell transform have been applied comparatively. The outcome of the comparison reveals that GMWs are highly applicable to the research in various areas, predominantly biomedicine.

On-Line Loss of Control Detection Using Wavelets

NASA Technical Reports Server (NTRS)

Brenner, Martin J. (Technical Monitor); Thompson, Peter M.; Klyde, David H.; Bachelder, Edward N.; Rosenthal, Theodore J.

2005-01-01

Wavelet transforms are used for on-line detection of aircraft loss of control. Wavelet transforms are compared with Fourier transform methods and shown to more rapidly detect changes in the vehicle dynamics. This faster response is due to a time window that decreases in length as the frequency increases. New wavelets are defined that further decrease the detection time by skewing the shape of the envelope. The wavelets are used for power spectrum and transfer function estimation. Smoothing is used to tradeoff the variance of the estimate with detection time. Wavelets are also used as front-end to the eigensystem reconstruction algorithm. Stability metrics are estimated from the frequency response and models, and it is these metrics that are used for loss of control detection. A Matlab toolbox was developed for post-processing simulation and flight data using the wavelet analysis methods. A subset of these methods was implemented in real time and named the Loss of Control Analysis Tool Set or LOCATS. A manual control experiment was conducted using a hardware-in-the-loop simulator for a large transport aircraft, in which the real time performance of LOCATS was demonstrated. The next step is to use these wavelet analysis tools for flight test support.

Wavelet extractor: A Bayesian well-tie and wavelet extraction program

NASA Astrophysics Data System (ADS)

Gunning, James; Glinsky, Michael E.

2006-06-01

We introduce a new open-source toolkit for the well-tie or wavelet extraction problem of estimating seismic wavelets from seismic data, time-to-depth information, and well-log suites. The wavelet extraction model is formulated as a Bayesian inverse problem, and the software will simultaneously estimate wavelet coefficients, other parameters associated with uncertainty in the time-to-depth mapping, positioning errors in the seismic imaging, and useful amplitude-variation-with-offset (AVO) related parameters in multi-stack extractions. It is capable of multi-well, multi-stack extractions, and uses continuous seismic data-cube interpolation to cope with the problem of arbitrary well paths. Velocity constraints in the form of checkshot data, interpreted markers, and sonic logs are integrated in a natural way. The Bayesian formulation allows computation of full posterior uncertainties of the model parameters, and the important problem of the uncertain wavelet span is addressed uses a multi-model posterior developed from Bayesian model selection theory. The wavelet extraction tool is distributed as part of the Delivery seismic inversion toolkit. A simple log and seismic viewing tool is included in the distribution. The code is written in Java, and thus platform independent, but the Seismic Unix (SU) data model makes the inversion particularly suited to Unix/Linux environments. It is a natural companion piece of software to Delivery, having the capacity to produce maximum likelihood wavelet and noise estimates, but will also be of significant utility to practitioners wanting to produce wavelet estimates for other inversion codes or purposes. The generation of full parameter uncertainties is a crucial function for workers wishing to investigate questions of wavelet stability before proceeding to more advanced inversion studies.

Multifractal spectrum of physiological signals: a mechanism-related approach

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Pavlova, Olga N.; Abdurashitov, Arkady S.; Arinushkin, Pavel A.; Runnova, Anastasiya E.; Semyachkina-Glushkovskaya, Oxana V.

2017-04-01

In this paper we discuss an approach for mechanism-related analysis of physiological signals performed with the wavelet-based multifractal formalism. This approach assumes estimation of the singularity spectrum for the band-pass filtered processes at different physiological conditions in order to provide explanation of the occurred changes in the Hölder exponents and the multi-fractality degree. We illustrate the considered approach using two examples, namely, the dynamics of the cerebral blood flow (CBF) and the electrical activity of the brain.

[Application of wavelet transform and neural network in the near-infrared spectrum analysis of oil shale].

PubMed

Li, Su-Yi; Ji, Yan-Ju; Liu, Wei-Yu; Wang, Zhi-Hong

2013-04-01

In the present study, an innovative method is proposed, employing both wavelet transform and neural network, to analyze the near-infrared spectrum data in oil shale survey. The method entails using db8 wavelet at 3 levels decomposition to process raw data, using the transformed data as the input matrix, and creating the model through neural network. To verify the validity of the method, this study analyzes 30 synthesized oil shale samples, in which 20 samples are randomly selected for network training, the other 10 for model prediction, and uses the full spectrum and the wavelet transformed spectrum to carry out 10 network models, respectively. Results show that the mean speed of the full spectrum neural network modeling is 570.33 seconds, and the predicted residual sum of squares (PRESS) and correlation coefficient of prediction are 0.006 012 and 0.843 75, respectively. In contrast, the mean speed of the wavelet network modeling method is 3.15 seconds, and the mean PRESS and correlation coefficient of prediction are 0.002 048 and 0.953 19, respectively. These results demonstrate that the wavelet neural network modeling method is significantly superior to the full spectrum neural network modeling method. This study not only provides a new method for more efficient and accurate detection of the oil content of oil shale, but also indicates the potential for applying wavelet transform and neutral network in broad near-infrared spectrum analysis.

Spectral Data Reduction via Wavelet Decomposition

NASA Technical Reports Server (NTRS)

Kaewpijit, S.; LeMoigne, J.; El-Ghazawi, T.; Rood, Richard (Technical Monitor)

2002-01-01

The greatest advantage gained from hyperspectral imagery is that narrow spectral features can be used to give more information about materials than was previously possible with broad-band multispectral imagery. For many applications, the new larger data volumes from such hyperspectral sensors, however, present a challenge for traditional processing techniques. For example, the actual identification of each ground surface pixel by its corresponding reflecting spectral signature is still one of the most difficult challenges in the exploitation of this advanced technology, because of the immense volume of data collected. Therefore, conventional classification methods require a preprocessing step of dimension reduction to conquer the so-called "curse of dimensionality." Spectral data reduction using wavelet decomposition could be useful, as it does not only reduce the data volume, but also preserves the distinctions between spectral signatures. This characteristic is related to the intrinsic property of wavelet transforms that preserves high- and low-frequency features during the signal decomposition, therefore preserving peaks and valleys found in typical spectra. When comparing to the most widespread dimension reduction technique, the Principal Component Analysis (PCA), and looking at the same level of compression rate, we show that Wavelet Reduction yields better classification accuracy, for hyperspectral data processed with a conventional supervised classification such as a maximum likelihood method.

Analysis of wavelet-filtered tonic-clonic electroencephalogram recordings.

PubMed

Rosso, O A; Figliola, A; Creso, J; Serrano, E

2004-07-01

EEG signals obtained during tonic-clonic epileptic seizures can be severely contaminated by muscle and physiological noise. Heavily contaminated EEG signals are hard to analyse quantitatively and also are usually rejected for visual inspection by physicians, resulting in a considerable loss of collected information. The aim of this work was to develop a computer-based method of time series analysis for such EEGs. A method is presented for filtering those frequencies associated with muscle activity using a wavelet transform. One of the advantages of this method over traditional filtering is that wavelet filtering of some frequency bands does not modify the pattern of the remaining ones. In consequence, the dynamics associated with them do not change. After generation of a 'noise free' signal by removal of the muscle artifacts using wavelets, a dynamic analysis was performed using non-linear dynamics metric tools. The characteristic parameters evaluated (correlation dimension D2 and largest Lyapunov exponent lambda1) were compatible with those obtained in previous works. The average values obtained were: D2=4.25 and lambda1=3.27 for the pre-ictal stage; D2=4.03 and lambda1=2.68 for the tonic seizure stage; D2=4.11 and lambda1=2.46 for the clonic seizure stage.

Identification of large geomorphological anomalies based on 2D discrete wavelet transform

NASA Astrophysics Data System (ADS)

Doglioni, A.; Simeone, V.

2012-04-01

The identification and analysis based on quantitative evidences of large geomorphological anomalies is an important stage for the study of large landslides. Numerical geomorphic analyses represent an interesting approach to this kind of studies, allowing for a detailed and pretty accurate identification of hidden topographic anomalies that may be related to large landslides. Here a geomorphic numerical analyses of the Digital Terrain Model (DTM) is presented. The introduced approach is based on 2D discrete wavelet transform (Antoine et al., 2003; Bruun and Nilsen, 2003, Booth et al., 2009). The 2D wavelet decomposition of the DTM, and in particular the analysis of the detail coefficients of the wavelet transform can provide evidences of anomalies or singularities, i.e. discontinuities of the land surface. These discontinuities are not very evident from the DTM as it is, while 2D wavelet transform allows for grid-based analysis of DTM and for mapping the decomposition. In fact, the grid-based DTM can be assumed as a matrix, where a discrete wavelet transform (Daubechies, 1992) is performed columnwise and linewise, which basically represent horizontal and vertical directions. The outcomes of this analysis are low-frequency approximation coefficients and high-frequency detail coefficients. Detail coefficients are analyzed, since their variations are associated to discontinuities of the DTM. Detailed coefficients are estimated assuming to perform 2D wavelet transform both for the horizontal direction (east-west) and for the vertical direction (north-south). Detail coefficients are then mapped for both the cases, thus allowing to visualize and quantify potential anomalies of the land surface. Moreover, wavelet decomposition can be pushed to further levels, assuming a higher scale number of the transform. This may potentially return further interesting results, in terms of identification of the anomalies of land surface. In this kind of approach, the choice of a proper mother wavelet function is a tricky point, since it conditions the analysis and then their outcomes. Therefore multiple levels as well as multiple wavelet analyses are guessed. Here the introduced approach is applied to some interesting cases study of south Italy, in particular for the identification of large anomalies associated to large landslides at the transition between Apennine chain domain and the foredeep domain. In particular low Biferno valley and Fortore valley are here analyzed. Finally, the wavelet transforms are performed on multiple levels, thus trying to address the problem of which is the level extent for an accurate analysis fit to a specific problem. Antoine J.P., Carrette P., Murenzi R., and Piette B., (2003), Image analysis with two-dimensional continuous wavelet transform, Signal Processing, 31(3), pp. 241-272, doi:10.1016/0165-1684(93)90085-O. Booth A.M., Roering J.J., and Taylor Perron J., (2009), Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon, Geomorphology, 109(3-4), pp. 132-147, doi:10.1016/j.geomorph.2009.02.027. Bruun B.T., and Nilsen S., (2003), Wavelet representation of large digital terrain models, Computers and Geoscience, 29(6), pp. 695-703, doi:10.1016/S0098-3004(03)00015-3. Daubechies, I. (1992), Ten lectures on wavelets, SIAM.

Time-frequency analysis of electric motors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bentley, C.L.; Dunn, M.E.; Mattingly, J.K.

1995-12-31

Physical signals such as the current of an electric motor become nonstationary as a consequence of degraded operation and broken parts. In this instance, their power spectral densities become time dependent, and time-frequency analysis techniques become the appropriate tools for signal analysis. The first among these techniques, generally called the short-time Fourier transform (STFT) method, is the Gabor transform 2 (GT) of a signal S(t), which decomposes the signal into time-local frequency modes: where the window function, {Phi}(t-{tau}), is a normalized Gaussian. Alternatively, one can decompose the signal into its multi-resolution representation at different levels of magnification. This representation ismore » achieved by the continuous wavelet transform (CWT) where the function g(t) is a kernel of zero average belonging to a family of scaled and shifted wavelet kernels. The CWT can be interpreted as the action of a microscope that locates the signal by the shift parameter b and adjusts its magnification by changing the scale parameter a. The Fourier-transformed CWT, W,{sub g}(a, {omega}), acts as a filter that places the high-frequency content of a signal into the lower end of the scale spectrum and vice versa for the low frequencies. Signals from a motor in three different states were analyzed.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Maiolo, M., E-mail: massimo.maiolo@zhaw.ch; ZHAW, Institut für Angewandte Simulation, Grüental, CH-8820 Wädenswil; Vancheri, A., E-mail: alberto.vancheri@supsi.ch

In this paper, we apply Multiresolution Analysis (MRA) to develop sparse but accurate representations for the Multiscale Coarse-Graining (MSCG) approximation to the many-body potential of mean force. We rigorously framed the MSCG method into MRA so that all the instruments of this theory become available together with a multitude of new basis functions, namely the wavelets. The coarse-grained (CG) force field is hierarchically decomposed at different resolution levels enabling to choose the most appropriate wavelet family for each physical interaction without requiring an a priori knowledge of the details localization. The representation of the CG potential in this new efficientmore » orthonormal basis leads to a compression of the signal information in few large expansion coefficients. The multiresolution property of the wavelet transform allows to isolate and remove the noise from the CG force-field reconstruction by thresholding the basis function coefficients from each frequency band independently. We discuss the implementation of our wavelet-based MSCG approach and demonstrate its accuracy using two different condensed-phase systems, i.e. liquid water and methanol. Simulations of liquid argon have also been performed using a one-to-one mapping between atomistic and CG sites. The latter model allows to verify the accuracy of the method and to test different choices of wavelet families. Furthermore, the results of the computer simulations show that the efficiency and sparsity of the representation of the CG force field can be traced back to the mathematical properties of the chosen family of wavelets. This result is in agreement with what is known from the theory of multiresolution analysis of signals.« less

Phase synchronization based on a Dual-Tree Complex Wavelet Transform

NASA Astrophysics Data System (ADS)

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

2016-11-01

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

A general theory on frequency and time-frequency analysis of irregularly sampled time series based on projection methods - Part 2: Extension to time-frequency analysis

NASA Astrophysics Data System (ADS)

Lenoir, Guillaume; Crucifix, Michel

2018-03-01

Geophysical time series are sometimes sampled irregularly along the time axis. The situation is particularly frequent in palaeoclimatology. Yet, there is so far no general framework for handling the continuous wavelet transform when the time sampling is irregular. Here we provide such a framework. To this end, we define the scalogram as the continuous-wavelet-transform equivalent of the extended Lomb-Scargle periodogram defined in Part 1 of this study (Lenoir and Crucifix, 2018). The signal being analysed is modelled as the sum of a locally periodic component in the time-frequency plane, a polynomial trend, and a background noise. The mother wavelet adopted here is the Morlet wavelet classically used in geophysical applications. The background noise model is a stationary Gaussian continuous autoregressive-moving-average (CARMA) process, which is more general than the traditional Gaussian white and red noise processes. The scalogram is smoothed by averaging over neighbouring times in order to reduce its variance. The Shannon-Nyquist exclusion zone is however defined as the area corrupted by local aliasing issues. The local amplitude in the time-frequency plane is then estimated with least-squares methods. We also derive an approximate formula linking the squared amplitude and the scalogram. Based on this property, we define a new analysis tool: the weighted smoothed scalogram, which we recommend for most analyses. The estimated signal amplitude also gives access to band and ridge filtering. Finally, we design a test of significance for the weighted smoothed scalogram against the stationary Gaussian CARMA background noise, and provide algorithms for computing confidence levels, either analytically or with Monte Carlo Markov chain methods. All the analysis tools presented in this article are available to the reader in the Python package WAVEPAL.

A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

PubMed Central

Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad

2017-01-01

Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients. PMID:28152063

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 participating nodes. Therefore, the feature-extraction method based on the Haar DWT is presented that employs a maximum-entropy measure to determine significant wavelet coefficients. Features are formed by calculating the energy of coefficients grouped around the competing clusters. A DWT-based feature extraction algorithm used for vehicle classification in WSNs can be enhanced by an added rule for selecting the optimal number of resolution levels to improve the correct classification rate and reduce energy consumption expended in local algorithm computations. Published field trial data for vehicular ground targets, measured with multiple sensor types, are used to evaluate the wavelet-assisted algorithms. Extracted features are used in established target recognition routines, e.g., the Bayesian minimum-error-rate classifier, to compare the effects on the classification performance of the wavelet compression. Simulations of feature sets and recognition routines at different resolution levels in target scenarios indicate the impact on classification rates, while formulas are provided to estimate reduction in resource use due to distributed compression.

Image Retrieval using Integrated Features of Binary Wavelet Transform

NASA Astrophysics Data System (ADS)

Agarwal, Megha; Maheshwari, R. P.

2011-12-01

In this paper a new approach for image retrieval is proposed with the application of binary wavelet transform. This new approach facilitates the feature calculation with the integration of histogram and correlogram features extracted from binary wavelet subbands. Experiments are performed to evaluate and compare the performance of proposed method with the published literature. It is verified that average precision and average recall of proposed method (69.19%, 41.78%) is significantly improved compared to optimal quantized wavelet correlogram (OQWC) [6] (64.3%, 38.00%) and Gabor wavelet correlogram (GWC) [10] (64.1%, 40.6%). All the experiments are performed on Corel 1000 natural image database [20].

A lung sound classification system based on the rational dilation wavelet transform.

PubMed

Ulukaya, Sezer; Serbes, Gorkem; Sen, Ipek; Kahya, Yasemin P

2016-08-01

In this work, a wavelet based classification system that aims to discriminate crackle, normal and wheeze lung sounds is presented. While the previous works related with this problem use constant low Q-factor wavelets, which have limited frequency resolution and can not cope with oscillatory signals, in the proposed system, the Rational Dilation Wavelet Transform, whose Q-factors can be tuned, is employed. Proposed system yields an accuracy of 95 % for crackle, 97 % for wheeze, 93.50 % for normal and 95.17 % for total sound signal types using energy feature subset and proposed approach is superior to conventional low Q-factor wavelet analysis.

Cell edge detection in JPEG2000 wavelet domain - analysis on sigmoid function edge model.

PubMed

Punys, Vytenis; Maknickas, Ramunas

2011-01-01

Big virtual microscopy images (80K x 60K pixels and larger) are usually stored using the JPEG2000 image compression scheme. Diagnostic quantification, based on image analysis, might be faster if performed on compressed data (approx. 20 times less the original amount), representing the coefficients of the wavelet transform. The analysis of possible edge detection without reverse wavelet transform is presented in the paper. Two edge detection methods, suitable for JPEG2000 bi-orthogonal wavelets, are proposed. The methods are adjusted according calculated parameters of sigmoid edge model. The results of model analysis indicate more suitable method for given bi-orthogonal wavelet.

Artificial retina model for the retinally blind based on wavelet transform

NASA Astrophysics Data System (ADS)

Zeng, Yan-an; Song, Xin-qiang; Jiang, Fa-gang; Chang, Da-ding

2007-01-01

Artificial retina is aimed for the stimulation of remained retinal neurons in the patients with degenerated photoreceptors. Microelectrode arrays have been developed for this as a part of stimulator. Design such microelectrode arrays first requires a suitable mathematical method for human retinal information processing. In this paper, a flexible and adjustable human visual information extracting model is presented, which is based on the wavelet transform. With the flexible of wavelet transform to image information processing and the consistent to human visual information extracting, wavelet transform theory is applied to the artificial retina model for the retinally blind. The response of the model to synthetic image is shown. The simulated experiment demonstrates that the model behaves in a manner qualitatively similar to biological retinas and thus may serve as a basis for the development of an artificial retina.

Applications of squeezed states: Bogoliubov transformations and wavelets to the statistical mechanics of water and its bubbles

NASA Technical Reports Server (NTRS)

Defacio, Brian; Kim, S.-H.; Vannevel, A.

1994-01-01

The squeezed states or Bogoliubov transformations and wavelets are applied to two problems in nonrelativistic statistical mechanics: the dielectric response of liquid water, epsilon(q-vector,w), and the bubble formation in water during insonnification. The wavelets are special phase-space windows which cover the domain and range of L(exp 1) intersection of L(exp 2) of classical causal, finite energy solutions. The multiresolution of discrete wavelets in phase space gives a decomposition into regions of time and scales of frequency thereby allowing the renormalization group to be applied to new systems in addition to the tired 'usual suspects' of the Ising models and lattice gasses. The Bogoliubov transformation: squeeze transformation is applied to the dipolaron collective mode in water and to the gas produced by the explosive cavitation process in bubble formation.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms.

PubMed

Reena Benjamin, J; Jayasree, T

2018-02-01

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms. A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details. The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations. In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

NASA Astrophysics Data System (ADS)

Joseph, R.; Courbin, F.; Starck, J.-L.

2016-05-01

We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html

Comparison of the Spectral Properties of Pansharpened Images Generated from AVNIR-2 and Prism Onboard Alos

NASA Astrophysics Data System (ADS)

Matsuoka, M.

2012-07-01

A considerable number of methods for pansharpening remote-sensing images have been developed to generate higher spatial resolution multispectral images by the fusion of lower resolution multispectral images and higher resolution panchromatic images. Because pansharpening alters the spectral properties of multispectral images, method selection is one of the key factors influencing the accuracy of subsequent analyses such as land-cover classification or change detection. In this study, seven pixel-based pansharpening methods (additive wavelet intensity, additive wavelet principal component, generalized Laplacian pyramid with spectral distortion minimization, generalized intensity-hue-saturation (GIHS) transform, GIHS adaptive, Gram-Schmidt spectral sharpening, and block-based synthetic variable ratio) were compared using AVNIR-2 and PRISM onboard ALOS from the viewpoint of the preservation of spectral properties of AVNIR-2. A visual comparison was made between pansharpened images generated from spatially degraded AVNIR-2 and original images over urban, agricultural, and forest areas. The similarity of the images was evaluated in terms of the image contrast, the color distinction, and the brightness of the ground objects. In the quantitative assessment, three kinds of statistical indices, correlation coefficient, ERGAS, and Q index, were calculated by band and land-cover type. These scores were relatively superior in bands 2 and 3 compared with the other two bands, especially over urban and agricultural areas. Band 4 showed a strong dependency on the land-cover type. This was attributable to the differences in the observing spectral wavelengths of the sensors and local scene variances.

High-resolution time-frequency representation of EEG data using multi-scale wavelets

NASA Astrophysics Data System (ADS)

Li, Yang; Cui, Wei-Gang; Luo, Mei-Lin; Li, Ke; Wang, Lina

2017-09-01

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time-frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition

NASA Astrophysics Data System (ADS)

Meckley, John R.

1995-09-01

The information content in many signal processing applications can be reduced to a set of linear features in a 2D signal transform. Examples include the narrowband lines in a spectrogram, ship wakes in a synthetic aperture radar image, and blood vessels in a medical computer-aided tomography scan. The line integrals that generate the values of the projections of the Radon transform can be characterized as a bank of matched filters for linear features. This localization of energy in the Radon transform for linear features can be exploited to enhance these features and to reduce noise by filtering the Radon transform with a filter explicitly designed to pass only linear features, and then reconstructing a new 2D signal by inverting the new filtered Radon transform (i.e., via filtered backprojection). Previously used methods for filtering the Radon transform include Fourier based filtering (a 2D elliptical Gaussian linear filter) and a nonlinear filter ((Radon xfrm)**y with y >= 2.0). Both of these techniques suffer from the mismatch of the filter response to the true functional form of the Radon transform of a line. The Radon transform of a line is not a point but is a function of the Radon variables (rho, theta) and the total line energy. This mismatch leads to artifacts in the reconstructed image and a reduction in achievable processing gain. The Radon transform for a line is computed as a function of angle and offset (rho, theta) and the line length. The 2D wavelet coefficients are then compared for the Haar wavelets and the Daubechies wavelets. These filter responses are used as frequency filters for the Radon transform. The filtering is performed on the wavelet pyramid decomposition of the Radon transform by detecting the most likely positions of lines in the transform and then by convolving the local area with the appropriate response and zeroing the pyramid coefficients outside of the response area. The response area is defined to contain 95% of the total wavelet coefficient energy. The detection algorithm provides an estimate of the line offset, orientation, and length that is then used to index the appropriate filter shape. Additional wavelet pyramid decomposition is performed in areas of high energy to refine the line position estimate. After filtering, the new Radon transform is generated by inverting the wavelet pyramid. The Radon transform is then inverted by filtered backprojection to produce the final 2D signal estimate with the enhanced linear features. The wavelet-based method is compared to both the Fourier and the nonlinear filtering with examples of sparse and dense shapes in imaging, acoustics and medical tomography with test images of noisy concentric lines, a real spectrogram of a blow fish (a very nonstationary spectrum), and the Shepp Logan Computer Tomography phantom image. Both qualitative and derived quantitative measures demonstrate the improvement of wavelet-based filtering. Additional research is suggested based on these results. Open questions include what level(s) to use for detection and filtering because multiple-level representations exist. The lower levels are smoother at reduced spatial resolution, while the higher levels provide better response to edges. Several examples are discussed based on analytical and phenomenological arguments.

Embedding multiple watermarks in the DFT domain using low- and high-frequency bands

NASA Astrophysics Data System (ADS)

Ganic, Emir; Dexter, Scott D.; Eskicioglu, Ahmet M.

2005-03-01

Although semi-blind and blind watermarking schemes based on Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) are robust to a number of attacks, they fail in the presence of geometric attacks such as rotation, scaling, and translation. The Discrete Fourier Transform (DFT) of a real image is conjugate symmetric, resulting in a symmetric DFT spectrum. Because of this property, the popularity of DFT-based watermarking has increased in the last few years. In a recent paper, we generalized a circular watermarking idea to embed multiple watermarks in lower and higher frequencies. Nevertheless, a circular watermark is visible in the DFT domain, providing a potential hacker with valuable information about the location of the watermark. In this paper, our focus is on embedding multiple watermarks that are not visible in the DFT domain. Using several frequency bands increases the overall robustness of the proposed watermarking scheme. Specifically, our experiments show that the watermark embedded in lower frequencies is robust to one set of attacks, and the watermark embedded in higher frequencies is robust to a different set of attacks.

Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform.

PubMed

De Queiroz, Ricardo; Chou, Philip A

2016-06-01

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time and with the recent possibility of real time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably to the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this work represents the state-of-the-art in intra-frame compression of point clouds for real-time 3D video.

Modular continuous wavelet processing of biosignals: extracting heart rate and oxygen saturation from a video signal

PubMed Central

2016-01-01

A novel method of extracting heart rate and oxygen saturation from a video-based biosignal is described. The method comprises a novel modular continuous wavelet transform approach which includes: performing the transform, undertaking running wavelet archetyping to enhance the pulse information, extraction of the pulse ridge time–frequency information [and thus a heart rate (HRvid) signal], creation of a wavelet ratio surface, projection of the pulse ridge onto the ratio surface to determine the ratio of ratios from which a saturation trending signal is derived, and calibrating this signal to provide an absolute saturation signal (SvidO2). The method is illustrated through its application to a video photoplethysmogram acquired during a porcine model of acute desaturation. The modular continuous wavelet transform-based approach is advocated by the author as a powerful methodology to deal with noisy, non-stationary biosignals in general. PMID:27382479

Measurement of entanglement entropy in the two-dimensional Potts model using wavelet analysis.

PubMed

Tomita, Yusuke

2018-05-01

A method is introduced to measure the entanglement entropy using a wavelet analysis. Using this method, the two-dimensional Haar wavelet transform of a configuration of Fortuin-Kasteleyn (FK) clusters is performed. The configuration represents a direct snapshot of spin-spin correlations since spin degrees of freedom are traced out in FK representation. A snapshot of FK clusters loses image information at each coarse-graining process by the wavelet transform. It is shown that the loss of image information measures the entanglement entropy in the Potts model.

Texture Analysis of Recurrence Plots Based on Wavelets and PSO for Laryngeal Pathologies Detection.

PubMed

Souza, Taciana A; Vieira, Vinícius J D; Correia, Suzete E N; Costa, Silvana L N C; de A Costa, Washington C; Souza, Micael A

2015-01-01

This paper deals with the discrimination between healthy and pathological speech signals using recurrence plots and wavelet transform with texture features. Approximation and detail coefficients are obtained from the recurrence plots using Haar wavelet transform, considering one decomposition level. The considered laryngeal pathologies are: paralysis, Reinke's edema and nodules. Accuracy rates above 86% were obtained by means of the employed method.

A wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry

NASA Astrophysics Data System (ADS)

Wang, Jianhua; Yang, Yanxi

2018-05-01

We present a new wavelet ridge extraction method employing a novel cost function in two-dimensional wavelet transform profilometry (2-D WTP). First of all, the maximum value point is extracted from two-dimensional wavelet transform coefficient modulus, and the local extreme value points over 90% of maximum value are also obtained, they both constitute wavelet ridge candidates. Then, the gradient of rotate factor is introduced into the Abid's cost function, and the logarithmic Logistic model is used to adjust and improve the cost function weights so as to obtain more reasonable value estimation. At last, the dynamic programming method is used to accurately find the optimal wavelet ridge, and the wrapped phase can be obtained by extracting the phase at the ridge. Its advantage is that, the fringe pattern with low signal-to-noise ratio can be demodulated accurately, and its noise immunity will be better. Meanwhile, only one fringe pattern is needed to projected to measured object, so dynamic three-dimensional (3-D) measurement in harsh environment can be realized. Computer simulation and experimental results show that, for the fringe pattern with noise pollution, the 3-D surface recovery accuracy by the proposed algorithm is increased. In addition, the demodulation phase accuracy of Morlet, Fan and Cauchy mother wavelets are compared.

Harmonic analysis of electric locomotive and traction power system based on wavelet singular entropy

NASA Astrophysics Data System (ADS)

Dun, Xiaohong

2018-05-01

With the rapid development of high-speed railway and heavy-haul transport, the locomotive and traction power system has become the main harmonic source of China's power grid. In response to this phenomenon, the system's power quality issues need timely monitoring, assessment and governance. Wavelet singular entropy is an organic combination of wavelet transform, singular value decomposition and information entropy theory, which combines the unique advantages of the three in signal processing: the time-frequency local characteristics of wavelet transform, singular value decomposition explores the basic modal characteristics of data, and information entropy quantifies the feature data. Based on the theory of singular value decomposition, the wavelet coefficient matrix after wavelet transform is decomposed into a series of singular values that can reflect the basic characteristics of the original coefficient matrix. Then the statistical properties of information entropy are used to analyze the uncertainty of the singular value set, so as to give a definite measurement of the complexity of the original signal. It can be said that wavelet entropy has a good application prospect in fault detection, classification and protection. The mat lab simulation shows that the use of wavelet singular entropy on the locomotive and traction power system harmonic analysis is effective.

Analysis of the tennis racket vibrations during forehand drives: Selection of the mother wavelet.

PubMed

Blache, Y; Hautier, C; Lefebvre, F; Djordjevic, A; Creveaux, T; Rogowski, I

2017-08-16

The time-frequency analysis of the tennis racket and hand vibrations is of great interest for discomfort and pathology prevention. This study aimed to (i) to assess the stationarity of the vibratory signal of the racket and hand and (ii) to identify the best mother wavelet to perform future time-frequency analysis, (iii) to determine if the stroke spin, racket characteristics and impact zone can influence the selection of the best mother wavelet. A total of 2364 topspin and flat forehand drives were performed by fourteen male competitive tennis players with six different rackets. One tri-axial and one mono-axial accelerometer were taped on the racket throat and dominant hand respectively. The signal stationarity was tested through the wavelet spectrum test. Eighty-nine mother wavelet were tested to select the best mother wavelet based on continuous and discrete transforms. On average only 25±17%, 2±5%, 5±7% and 27±27% of the signal tested respected the hypothesis of stationarity for the three axes of the racket and the hand respectively. Regarding the two methods for the detection of the best mother wavelet, the Daubechy 45 wavelet presented the highest average ranking. No effect of the stroke spin, racket characteristics and impact zone was observed for the selection of the best mother wavelet. It was concluded that alternative approach to Fast Fourier Transform should be used to interpret tennis vibration signals. In the case where wavelet transform is chosen, the Daubechy 45 mother wavelet appeared to be the most suitable. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterizing the SEMG patterns with myofascial pain using a multi-scale wavelet model through machine learning approaches.

PubMed

Lin, Yu-Ching; Yu, Nan-Ying; Jiang, Ching-Fen; Chang, Shao-Hsia

2018-06-02

In this paper, we introduce a newly developed multi-scale wavelet model for the interpretation of surface electromyography (SEMG) signals and validate the model's capability to characterize changes in neuromuscular activation in cases with myofascial pain syndrome (MPS) via machine learning methods. The SEMG data collected from normal (N = 30; 27 women, 3 men) and MPS subjects (N = 26; 22 women, 4 men) were adopted for this retrospective analysis. SMEGs were measured from the taut-band loci on both sides of the trapezius muscle on the upper back while he/she conducted a cyclic bilateral backward shoulder extension movement within 1 min. Classification accuracy of the SEMG model to differentiate MPS patients from normal subjects was 77% using template matching and 60% using K-means clustering. Classification consistency between the two machine learning methods was 87% in the normal group and 93% in the MPS group. The 2D feature graphs derived from the proposed multi-scale model revealed distinct patterns between normal subjects and MPS patients. The classification consistency using template matching and K-means clustering suggests the potential of using the proposed model to characterize interference pattern changes induced by MPS. Copyright © 2018. Published by Elsevier Ltd.

Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

2016-12-01

The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family tools. KurWSD has three main advantages: firstly, all the characteristic information scattered in proximal sub-bands is gathered through synthesizing those impulsive dominant sub-band signals and thus eliminates the dilemma of the IFFBS phenomenon. Secondly, the noises in the focused sub-bands could be alleviated efficiently through shrinking or removing the dense wavelet coefficients of Gaussian noise. Lastly, wavelet coefficients with faulty information are reliably detected and preserved through manipulating wavelet coefficients discriminatively based on the contribution to the impulsive components. Moreover, the reliability and effectiveness of the KurWSD are demonstrated through simulated and experimental signals.

Time-frequency analysis of phonocardiogram signals using wavelet transform: a comparative study.

PubMed

Ergen, Burhan; Tatar, Yetkin; Gulcur, Halil Ozcan

2012-01-01

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Visual information processing; Proceedings of the Meeting, Orlando, FL, Apr. 20-22, 1992

NASA Technical Reports Server (NTRS)

Huck, Friedrich O. (Editor); Juday, Richard D. (Editor)

1992-01-01

Topics discussed in these proceedings include nonlinear processing and communications; feature extraction and recognition; image gathering, interpolation, and restoration; image coding; and wavelet transform. Papers are presented on noise reduction for signals from nonlinear systems; driving nonlinear systems with chaotic signals; edge detection and image segmentation of space scenes using fractal analyses; a vision system for telerobotic operation; a fidelity analysis of image gathering, interpolation, and restoration; restoration of images degraded by motion; and information, entropy, and fidelity in visual communication. Attention is also given to image coding methods and their assessment, hybrid JPEG/recursive block coding of images, modified wavelets that accommodate causality, modified wavelet transform for unbiased frequency representation, and continuous wavelet transform of one-dimensional signals by Fourier filtering.

Short-term data forecasting based on wavelet transformation and chaos theory

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Cunbin; Zhang, Liang

2017-09-01

A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions.

PubMed

Cowlagi, Raghvendra V; Tsiotras, Panagiotis

2012-10-01

We present a path- and motion-planning scheme that is "multiresolution" both in the sense of representing the environment with high accuracy only locally and in the sense of addressing the vehicle kinematic and dynamic constraints only locally. The proposed scheme uses rectangular multiresolution cell decompositions, efficiently generated using the wavelet transform. The wavelet transform is widely used in signal and image processing, with emerging applications in autonomous sensing and perception systems. The proposed motion planner enables the simultaneous use of the wavelet transform in both the perception and in the motion-planning layers of vehicle autonomy, thus potentially reducing online computations. We rigorously prove the completeness of the proposed path-planning scheme, and we provide numerical simulation results to illustrate its efficacy.

Segmentation-based wavelet transform for still-image compression

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.

1996-10-01

In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.

Multimode waveguide speckle patterns for compressive sensing.

PubMed

Valley, George C; Sefler, George A; Justin Shaw, T

2016-06-01

Compressive sensing (CS) of sparse gigahertz-band RF signals using microwave photonics may achieve better performances with smaller size, weight, and power than electronic CS or conventional Nyquist rate sampling. The critical element in a CS system is the device that produces the CS measurement matrix (MM). We show that passive speckle patterns in multimode waveguides potentially provide excellent MMs for CS. We measure and calculate the MM for a multimode fiber and perform simulations using this MM in a CS system. We show that the speckle MM exhibits the sharp phase transition and coherence properties needed for CS and that these properties are similar to those of a sub-Gaussian MM with the same mean and standard deviation. We calculate the MM for a multimode planar waveguide and find dimensions of the planar guide that give a speckle MM with a performance similar to that of the multimode fiber. The CS simulations show that all measured and calculated speckle MMs exhibit a robust performance with equal amplitude signals that are sparse in time, in frequency, and in wavelets (Haar wavelet transform). The planar waveguide results indicate a path to a microwave photonic integrated circuit for measuring sparse gigahertz-band RF signals using CS.

Method and system for progressive mesh storage and reconstruction using wavelet-encoded height fields

NASA Technical Reports Server (NTRS)

Baxes, Gregory A. (Inventor); Linger, Timothy C. (Inventor)

2011-01-01

Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.

Method and system for progressive mesh storage and reconstruction using wavelet-encoded height fields

NASA Technical Reports Server (NTRS)

Baxes, Gregory A. (Inventor)

2010-01-01

Systems and methods are provided for progressive mesh storage and reconstruction using wavelet-encoded height fields. A method for progressive mesh storage includes reading raster height field data, and processing the raster height field data with a discrete wavelet transform to generate wavelet-encoded height fields. In another embodiment, a method for progressive mesh storage includes reading texture map data, and processing the texture map data with a discrete wavelet transform to generate wavelet-encoded texture map fields. A method for reconstructing a progressive mesh from wavelet-encoded height field data includes determining terrain blocks, and a level of detail required for each terrain block, based upon a viewpoint. Triangle strip constructs are generated from vertices of the terrain blocks, and an image is rendered utilizing the triangle strip constructs. Software products that implement these methods are provided.

Two-dimensional wavelet transform for reliability-guided phase unwrapping in optical fringe pattern analysis.

PubMed

Li, Sikun; Wang, Xiangzhao; Su, Xianyu; Tang, Feng

2012-04-20

This paper theoretically discusses modulus of two-dimensional (2D) wavelet transform (WT) coefficients, calculated by using two frequently used 2D daughter wavelet definitions, in an optical fringe pattern analysis. The discussion shows that neither is good enough to represent the reliability of the phase data. The differences between the two frequently used 2D daughter wavelet definitions in the performance of 2D WT also are discussed. We propose a new 2D daughter wavelet definition for reliability-guided phase unwrapping of optical fringe pattern. The modulus of the advanced 2D WT coefficients, obtained by using a daughter wavelet under this new daughter wavelet definition, includes not only modulation information but also local frequency information of the deformed fringe pattern. Therefore, it can be treated as a good parameter that represents the reliability of the retrieved phase data. Computer simulation and experimentation show the validity of the proposed method.

Asymptotic Cramer-Rao bounds for Morlet wavelet filter bank transforms of FM signals

NASA Astrophysics Data System (ADS)

Scheper, Richard

2002-03-01

Wavelet filter banks are potentially useful tools for analyzing and extracting information from frequency modulated (FM) signals in noise. Chief among the advantages of such filter banks is the tendency of wavelet transforms to concentrate signal energy while simultaneously dispersing noise energy over the time-frequency plane, thus raising the effective signal to noise ratio of filtered signals. Over the past decade, much effort has gone into devising new algorithms to extract the relevant information from transformed signals while identifying and discarding the transformed noise. Therefore, estimates of the ultimate performance bounds on such algorithms would serve as valuable benchmarks in the process of choosing optimal algorithms for given signal classes. Discussed here is the specific case of FM signals analyzed by Morlet wavelet filter banks. By making use of the stationary phase approximation of the Morlet transform, and assuming that the measured signals are well resolved digitally, the asymptotic form of the Fisher Information Matrix is derived. From this, Cramer-Rao bounds are analytically derived for simple cases.

Motion compensation via redundant-wavelet multihypothesis.

PubMed

Fowler, James E; Cui, Suxia; Wang, Yonghui

2006-10-01

Multihypothesis motion compensation has been widely used in video coding with previous attention focused on techniques employing predictions that are diverse spatially or temporally. In this paper, the multihypothesis concept is extended into the transform domain by using a redundant wavelet transform to produce multiple predictions that are diverse in transform phase. The corresponding multiple-phase inverse transform implicitly combines the phase-diverse predictions into a single spatial-domain prediction for motion compensation. The performance advantage of this redundant-wavelet-multihypothesis approach is investigated analytically, invoking the fact that the multiple-phase inverse involves a projection that significantly reduces the power of a dense-motion residual modeled as additive noise. The analysis shows that redundant-wavelet multihypothesis is capable of up to a 7-dB reduction in prediction-residual variance over an equivalent single-phase, single-hypothesis approach. Experimental results substantiate the performance advantage for a block-based implementation.

Applications of wavelets in interferometry and artificial vision

NASA Astrophysics Data System (ADS)

Escalona Z., Rafael A.

2001-08-01

In this paper we present a different point of view of phase measurements performed in interferometry, image processing and intelligent vision using Wavelet Transform. In standard and white-light interferometry, the phase function is retrieved by using phase-shifting, Fourier-Transform, cosinus-inversion and other known algorithms. Our novel technique presented here is faster, robust and shows excellent accuracy in phase determinations. Finally, in our second application, fringes are no more generate by some light interaction but result from the observation of adapted strip set patterns directly printed on the target of interest. The moving target is simply observed by a conventional vision system and usual phase computation algorithms are adapted to an image processing by wavelet transform, in order to sense target position and displacements with a high accuracy. In general, we have determined that wavelet transform presents properties of robustness, relative speed of calculus and very high accuracy in phase computations.

Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform

PubMed Central

Mayer, Markus A.; Boretsky, Adam R.; van Kuijk, Frederik J.; Motamedi, Massoud

2012-01-01

Abstract. Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained. PMID:23117804

Retinal optical coherence tomography image enhancement via shrinkage denoising using double-density dual-tree complex wavelet transform.

PubMed

Chitchian, Shahab; Mayer, Markus A; Boretsky, Adam R; van Kuijk, Frederik J; Motamedi, Massoud

2012-11-01

ABSTRACT. Image enhancement of retinal structures, in optical coherence tomography (OCT) scans through denoising, has the potential to aid in the diagnosis of several eye diseases. In this paper, a locally adaptive denoising algorithm using double-density dual-tree complex wavelet transform, a combination of the double-density wavelet transform and the dual-tree complex wavelet transform, is applied to reduce speckle noise in OCT images of the retina. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in significantly less acquisition time equal to an order of magnitude less time compared to the averaging method. In addition, improvements of image quality metrics and 5 dB increase in the signal-to-noise ratio are attained.

The 4D hyperspherical diffusion wavelet: A new method for the detection of localized anatomical variation.

PubMed

Hosseinbor, Ameer Pasha; Kim, Won Hwa; Adluru, Nagesh; Acharya, Amit; Vorperian, Houri K; Chung, Moo K

2014-01-01

Recently, the HyperSPHARM algorithm was proposed to parameterize multiple disjoint objects in a holistic manner using the 4D hyperspherical harmonics. The HyperSPHARM coefficients are global; they cannot be used to directly infer localized variations in signal. In this paper, we present a unified wavelet framework that links Hyper-SPHARM to the diffusion wavelet transform. Specifically, we will show that the HyperSPHARM basis forms a subset of a wavelet-based multiscale representation of surface-based signals. This wavelet, termed the hyperspherical diffusion wavelet, is a consequence of the equivalence of isotropic heat diffusion smoothing and the diffusion wavelet transform on the hypersphere. Our framework allows for the statistical inference of highly localized anatomical changes, which we demonstrate in the first-ever developmental study on the hyoid bone investigating gender and age effects. We also show that the hyperspherical wavelet successfully picks up group-wise differences that are barely detectable using SPHARM.

The 4D Hyperspherical Diffusion Wavelet: A New Method for the Detection of Localized Anatomical Variation

PubMed Central

Hosseinbor, A. Pasha; Kim, Won Hwa; Adluru, Nagesh; Acharya, Amit; Vorperian, Houri K.; Chung, Moo K.

2014-01-01

Recently, the HyperSPHARM algorithm was proposed to parameterize multiple disjoint objects in a holistic manner using the 4D hyperspherical harmonics. The HyperSPHARM coefficients are global; they cannot be used to directly infer localized variations in signal. In this paper, we present a unified wavelet framework that links HyperSPHARM to the diffusion wavelet transform. Specifically, we will show that the HyperSPHARM basis forms a subset of a wavelet-based multiscale representation of surface-based signals. This wavelet, termed the hyperspherical diffusion wavelet, is a consequence of the equivalence of isotropic heat diffusion smoothing and the diffusion wavelet transform on the hypersphere. Our framework allows for the statistical inference of highly localized anatomical changes, which we demonstrate in the firstever developmental study on the hyoid bone investigating gender and age effects. We also show that the hyperspherical wavelet successfully picks up group-wise differences that are barely detectable using SPHARM. PMID:25320783

Analysis of two dimensional signals via curvelet transform

NASA Astrophysics Data System (ADS)

Lech, W.; Wójcik, W.; Kotyra, A.; Popiel, P.; Duk, M.

2007-04-01

This paper describes an application of curvelet transform analysis problem of interferometric images. Comparing to two-dimensional wavelet transform, curvelet transform has higher time-frequency resolution. This article includes numerical experiments, which were executed on random interferometric image. In the result of nonlinear approximations, curvelet transform obtains matrix with smaller number of coefficients than is guaranteed by wavelet transform. Additionally, denoising simulations show that curvelet could be a very good tool to remove noise from images.

Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia.

PubMed

Liang, Zhenhu; Li, Duan; Ouyang, Gaoxiang; Wang, Yinghua; Voss, Logan J; Sleigh, Jamie W; Li, Xiaoli

2012-04-01

The Hurst exponent (HE) is a nonlinear method measuring the smoothness of a fractal time series. In this study we applied the HE index, extracted from electroencephalographic (EEG) recordings, as a measure of anesthetic drug effects on brain activity. In 19 adult patients undergoing sevoflurane general anesthesia, we calculated the HE of the raw EEG; comparing the maximal overlap discrete wavelet transform (MODWT) with the traditional rescaled range (R/S) analysis techniques, and with a commercial index of depth of anesthesia - the response entropy (RE). We analyzed each wavelet-decomposed sub-band as well as the combined low frequency bands (HEOLFBs). The methods were compared in regard to pharmacokinetic/pharmacodynamic (PK/PD) modeling, and prediction probability. All the low frequency band HE indices decreased when anesthesia deepened. However the HEOLFB was the best index because: it was less sensitive to artifacts, most closely tracked the exact point of loss of consciousness, showed a better prediction probability in separating the awake and unconscious states, and tracked sevoflurane concentration better - as estimated by the PK/PD models. The HE is a useful measure for estimating the depth of anesthesia. It was noted that HEOLFB showed the best performance for tracking drug effect. The HEOLFB could be used as an index for accurately estimating the effect of anesthesia on brain activity. Copyright © 2011 International Federation of Clinical Neurophysiology. All rights reserved.

Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination

NASA Astrophysics Data System (ADS)

Hadi, Sinan Jasim; Tombul, Mustafa

2018-06-01

Streamflow is an essential component of the hydrologic cycle in the regional and global scale and the main source of fresh water supply. It is highly associated with natural disasters, such as droughts and floods. Therefore, accurate streamflow forecasting is essential. Forecasting streamflow in general and monthly streamflow in particular is a complex process that cannot be handled by data-driven models (DDMs) only and requires pre-processing. Wavelet transformation is a pre-processing technique; however, application of continuous wavelet transformation (CWT) produces many scales that cause deterioration in the performance of any DDM because of the high number of redundant variables. This study proposes multigene genetic programming (MGGP) as a selection tool. After the CWT analysis, it selects important scales to be imposed into the artificial neural network (ANN). A basin located in the southeast of Turkey is selected as case study to prove the forecasting ability of the proposed model. One month ahead downstream flow is used as output, and downstream flow, upstream, rainfall, temperature, and potential evapotranspiration with associated lags are used as inputs. Before modeling, wavelet coherence transformation (WCT) analysis was conducted to analyze the relationship between variables in the time-frequency domain. Several combinations were developed to investigate the effect of the variables on streamflow forecasting. The results indicated a high localized correlation between the streamflow and other variables, especially the upstream. In the models of the standalone layout where the data were entered to ANN and MGGP without CWT, the performance is found poor. In the best-scale layout, where the best scale of the CWT identified as the highest correlated scale is chosen and enters to ANN and MGGP, the performance increased slightly. Using the proposed model, the performance improved dramatically particularly in forecasting the peak values because of the inclusion of several scales in which seasonality and irregularity can be captured. Using hydrological and meteorological variables also improved the ability to forecast the streamflow.

Texture analysis of intermediate-advanced hepatocellular carcinoma: prognosis and patients' selection of transcatheter arterial chemoembolization and sorafenib

PubMed Central

Fu, Sirui; Chen, Shuting; Liang, Changhong; Liu, Zaiyi; Zhu, Yanjie; Li, Yong; Lu, Ligong

2017-01-01

Transcatheter arterial chemoembolization (TACE) and sorafenib combination treatment for unselected hepatocellular carcinoma (HCC) is controversial. We explored the potential of texture analysis for appropriate patient selection. There were 261 HCCs included (TACE group: n = 197; TACE plus sorafenib (TACE+Sorafenib) group n = 64). We applied a Gabor filter and wavelet transform with 3 band-width responses (filter 0, 1.0, and 1.5) to portal-phase computed tomography (CT) images of the TACE group. Twenty-one textural parameters per filter were extracted from the region of interests delineated around tumor outline. After testing survival correlations, the TACE group was subdivided according to parameter thresholds in receiver operating characteristic curves and compared to TACE+Sorafenib group survival. The Gabor-1-90 (filter 0) was most significantly correlated with TTP. The TACE group was accordingly divided into the TACE-1 (Gabor-1-90 ≤ 3.6190) and TACE-2 (Gabor-1-90 > 3.6190) subgroups; TTP was similar in the TACE-1 subgroup and TACE+Sorafenib group, but shorter in the TACE-2 subgroup. Only wavelet-3-D (filter 1.0) correlated with overall survival (OS), and was used for subgrouping. The TACE-5 (wavelet-3-D ≤ 12.2620) subgroup and the TACE+Sorafenib group showed similar OS, while the TACE-6 (wavelet-3-D > 12.2620) subgroup had shorter OS. Gabor-1-90 and wavelet-3-D were consistent. In dependent of tumor number or size, CT textural parameters are correlated with TTP and OS. Patients with lower Gabor-1-90 (filter 0) and wavelet-3-D (filter 1.0) should be treated with TACE and sorafenib. Texture analysis holds promise for appropriate selection of HCCs for this combination therapy. PMID:27911268

Variable mass pendulum behaviour processed by wavelet analysis

NASA Astrophysics Data System (ADS)

Caccamo, M. T.; Magazù, S.

2017-01-01

The present work highlights how, in order to characterize the motion of a variable mass pendulum, wavelet analysis can be an effective tool in furnishing information on the time evolution of the oscillation spectral content. In particular, the wavelet transform is applied to process the motion of a hung funnel that loses fine sand at an exponential rate; it is shown how, in contrast to the Fourier transform which furnishes only an average frequency value for the motion, the wavelet approach makes it possible to perform a joint time-frequency analysis. The work is addressed at undergraduate and graduate students.

Identification Method of Mud Shale Fractures Base on Wavelet Transform

NASA Astrophysics Data System (ADS)

Xia, Weixu; Lai, Fuqiang; Luo, Han

2018-01-01

In recent years, inspired by seismic analysis technology, a new method for analysing mud shale fractures oil and gas reservoirs by logging properties has emerged. By extracting the high frequency attribute of the wavelet transform in the logging attribute, the formation information hidden in the logging signal is extracted, identified the fractures that are not recognized by conventional logging and in the identified fracture segment to show the “cycle jump”, “high value”, “spike” and other response effect is more obvious. Finally formed a complete wavelet denoising method and wavelet high frequency identification fracture method.

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.

Implementation of the 2-D Wavelet Transform into FPGA for Image

NASA Astrophysics Data System (ADS)

León, M.; Barba, L.; Vargas, L.; Torres, C. O.

2011-01-01

This paper presents a hardware system implementation of the of discrete wavelet transform algoritm in two dimensions for FPGA, using the Daubechies filter family of order 2 (db2). The decomposition algorithm of this transform is designed and simulated with the Hardware Description Language VHDL and is implemented in a programmable logic device (FPGA) XC3S1200E reference, Spartan IIIE family, by Xilinx, take advantage the parallels properties of these gives us and speeds processing that can reach them. The architecture is evaluated using images input of different sizes. This implementation is done with the aim of developing a future images encryption hardware system using wavelet transform for security information.

Acoustical Emission Source Location in Thin Rods Through Wavelet Detail Crosscorrelation

DTIC Science & Technology

1998-03-01

NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS ACOUSTICAL EMISSION SOURCE LOCATION IN THIN RODS THROUGH WAVELET DETAIL CROSSCORRELATION...ACOUSTICAL EMISSION SOURCE LOCATION IN THIN RODS THROUGH WAVELET DETAIL CROSSCORRELATION 6. AUTHOR(S) Jerauld, Joseph G. 5. FUNDING NUMBERS Grant...frequency characteristics of Wavelet Analysis. Software implementation now enables the exploration of the Wavelet Transform to identify the time of

Analysis of spike-wave discharges in rats using discrete wavelet transform.

PubMed

Ubeyli, Elif Derya; Ilbay, Gül; Sahin, Deniz; Ateş, Nurbay

2009-03-01

A feature is a distinctive or characteristic measurement, transform, structural component extracted from a segment of a pattern. Features are used to represent patterns with the goal of minimizing the loss of important information. The discrete wavelet transform (DWT) as a feature extraction method was used in representing the spike-wave discharges (SWDs) records of Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. The SWD records of WAG/Rij rats were decomposed into time-frequency representations using the DWT and the statistical features were calculated to depict their distribution. The obtained wavelet coefficients were used to identify characteristics of the signal that were not apparent from the original time domain signal. The present study demonstrates that the wavelet coefficients are useful in determining the dynamics in the time-frequency domain of SWD records.

Stationary wavelet transform for under-sampled MRI reconstruction.

PubMed

Kayvanrad, Mohammad H; McLeod, A Jonathan; Baxter, John S H; McKenzie, Charles A; Peters, Terry M

2014-12-01

In addition to coil sensitivity data (parallel imaging), sparsity constraints are often used as an additional lp-penalty for under-sampled MRI reconstruction (compressed sensing). Penalizing the traditional decimated wavelet transform (DWT) coefficients, however, results in visual pseudo-Gibbs artifacts, some of which are attributed to the lack of translation invariance of the wavelet basis. We show that these artifacts can be greatly reduced by penalizing the translation-invariant stationary wavelet transform (SWT) coefficients. This holds with various additional reconstruction constraints, including coil sensitivity profiles and total variation. Additionally, SWT reconstructions result in lower error values and faster convergence compared to DWT. These concepts are illustrated with extensive experiments on in vivo MRI data with particular emphasis on multiple-channel acquisitions. Copyright © 2014 Elsevier Inc. All rights reserved.

Photometric Supernova Classification with Machine Learning

NASA Astrophysics Data System (ADS)

Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Hierarchical analysis of spatial pattern and processes of Douglas-fir forests. Ph.D. Thesis, 10 Sep. 1991 Abstract Only

NASA Technical Reports Server (NTRS)

Bradshaw, G. A.

1995-01-01

There has been an increased interest in the quantification of pattern in ecological systems over the past years. This interest is motivated by the desire to construct valid models which extend across many scales. Spatial methods must quantify pattern, discriminate types of pattern, and relate hierarchical phenomena across scales. Wavelet analysis is introduced as a method to identify spatial structure in ecological transect data. The main advantage of the wavelet transform over other methods is its ability to preserve and display hierarchical information while allowing for pattern decomposition. Two applications of wavelet analysis are illustrated, as a means to: (1) quantify known spatial patterns in Douglas-fir forests at several scales, and (2) construct spatially-explicit hypotheses regarding pattern generating mechanisms. Application of the wavelet variance, derived from the wavelet transform, is developed for forest ecosystem analysis to obtain additional insight into spatially-explicit data. Specifically, the resolution capabilities of the wavelet variance are compared to the semi-variogram and Fourier power spectra for the description of spatial data using a set of one-dimensional stationary and non-stationary processes. The wavelet cross-covariance function is derived from the wavelet transform and introduced as a alternative method for the analysis of multivariate spatial data of understory vegetation and canopy in Douglas-fir forests of the western Cascades of Oregon.

Continuous Wavelet Transform Analysis of Acceleration Signals Measured from a Wave Buoy

PubMed Central

Chuang, Laurence Zsu-Hsin; Wu, Li-Chung; Wang, Jong-Hao

2013-01-01

Accelerometers, which can be installed inside a floating platform on the sea, are among the most commonly used sensors for operational ocean wave measurements. To examine the non-stationary features of ocean waves, this study was conducted to derive a wavelet spectrum of ocean waves and to synthesize sea surface elevations from vertical acceleration signals of a wave buoy through the continuous wavelet transform theory. The short-time wave features can be revealed by simultaneously examining the wavelet spectrum and the synthetic sea surface elevations. The in situ wave signals were applied to verify the practicality of the wavelet-based algorithm. We confirm that the spectral leakage and the noise at very-low-frequency bins influenced the accuracies of the estimated wavelet spectrum and the synthetic sea surface elevations. The appropriate thresholds of these two factors were explored. To study the short-time wave features from the wave records, the acceleration signals recorded from an accelerometer inside a discus wave buoy are analysed. The results from the wavelet spectrum show the evidence of short-time nonlinear wave events. Our study also reveals that more surface profiles with higher vertical asymmetry can be found from short-time nonlinear wave with stronger harmonic spectral peak. Finally, we conclude that the algorithms of continuous wavelet transform are practical for revealing the short-time wave features of the buoy acceleration signals. PMID:23966188

Wavelet analysis applied to the IRAS cirrus

NASA Technical Reports Server (NTRS)

Langer, William D.; Wilson, Robert W.; Anderson, Charles H.

1994-01-01

The structure of infrared cirrus clouds is analyzed with Laplacian pyramid transforms, a form of non-orthogonal wavelets. Pyramid and wavelet transforms provide a means to decompose images into their spatial frequency components such that all spatial scales are treated in an equivalent manner. The multiscale transform analysis is applied to IRAS 100 micrometer maps of cirrus emission in the north Galactic pole region to extract features on different scales. In the maps we identify filaments, fragments and clumps by separating all connected regions. These structures are analyzed with respect to their Hausdorff dimension for evidence of the scaling relationships in the cirrus clouds.

Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform.

PubMed

Hang, X; Greenberg, N L; Shiota, T; Firstenberg, M S; Thomas, J D

2000-01-01

Real-time three-dimensional echocardiography has been introduced to provide improved quantification and description of cardiac function. Data compression is desired to allow efficient storage and improve data transmission. Previous work has suggested improved results utilizing wavelet transforms in the compression of medical data including 2D echocardiogram. Set partitioning in hierarchical trees (SPIHT) was extended to compress volumetric echocardiographic data by modifying the algorithm based on the three-dimensional wavelet packet transform. A compression ratio of at least 40:1 resulted in preserved image quality.

Wavelet tree structure based speckle noise removal for optical coherence tomography

NASA Astrophysics Data System (ADS)

Yuan, Xin; Liu, Xuan; Liu, Yang

2018-02-01

We report a new speckle noise removal algorithm in optical coherence tomography (OCT). Though wavelet domain thresholding algorithms have demonstrated superior advantages in suppressing noise magnitude and preserving image sharpness in OCT, the wavelet tree structure has not been investigated in previous applications. In this work, we propose an adaptive wavelet thresholding algorithm via exploiting the tree structure in wavelet coefficients to remove the speckle noise in OCT images. The threshold for each wavelet band is adaptively selected following a special rule to retain the structure of the image across different wavelet layers. Our results demonstrate that the proposed algorithm outperforms conventional wavelet thresholding, with significant advantages in preserving image features.

Damage Identification in Beam Structure using Spatial Continuous Wavelet Transform

NASA Astrophysics Data System (ADS)

Janeliukstis, R.; Rucevskis, S.; Wesolowski, M.; Kovalovs, A.; Chate, A.

2015-11-01

In this paper the applicability of spatial continuous wavelet transform (CWT) technique for damage identification in the beam structure is analyzed by application of different types of wavelet functions and scaling factors. The proposed method uses exclusively mode shape data from the damaged structure. To examine limitations of the method and to ascertain its sensitivity to noisy experimental data, several sets of simulated data are analyzed. Simulated test cases include numerical mode shapes corrupted by different levels of random noise as well as mode shapes with different number of measurement points used for wavelet transform. A broad comparison of ability of different wavelet functions to detect and locate damage in beam structure is given. Effectiveness and robustness of the proposed algorithms are demonstrated experimentally on two aluminum beams containing single mill-cut damage. The modal frequencies and the corresponding mode shapes are obtained via finite element models for numerical simulations and by using a scanning laser vibrometer with PZT actuator as vibration excitation source for the experimental study.

Wavelet-based compression of M-FISH images.

PubMed

Hua, Jianping; Xiong, Zixiang; Wu, Qiang; Castleman, Kenneth R

2005-05-01

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.

Wavelet data analysis of micro-Raman spectra for follow-up monitoring in oral pathologies

NASA Astrophysics Data System (ADS)

Camerlingo, C.; Zenone, F.; Perna, G.; Capozzi, V.; Cirillo, N.; Gaeta, G. M.; Lepore, M.

2008-02-01

A wavelet multi-component decomposition algorithm has been used for data analysis of micro-Raman spectra from human biological samples. In particular, measurements have been performed on some samples of oral tissue and blood serum from patients affected by pemphigus vulgaris at different stages. Pemphigus is a chronic, autoimmune, blistering disease of the skin and mucous membranes with a potentially fatal outcome. The disease is characterized histologically by intradermal blisters and immunopathologically by the finding of tissue bound and circulating immunoglobulin G (IgG) antibody directed against the cell surface of keratinocytes. More than 150 spectra were measured by means of a Raman confocal microspectrometer apparatus using the 632.8 nm line of a He-Ne laser source. A discrete wavelet transform decomposition method has been applied to the recorded Raman spectra in order to overcome related to low-level signals and the presence of noise and background components due to light scattering and fluorescence. The results indicate that appropriate data processing can contribute to enlarge the medical applications of micro-Raman spectroscopy.

Wavelet data processing of micro-Raman spectra of biological samples

NASA Astrophysics Data System (ADS)

Camerlingo, C.; Zenone, F.; Gaeta, G. M.; Riccio, R.; Lepore, M.

2006-02-01

A wavelet multi-component decomposition algorithm is proposed for processing data from micro-Raman spectroscopy (μ-RS) of biological tissue. The μ-RS has been recently recognized as a promising tool for the biopsy test and in vivo diagnosis of degenerative human tissue pathologies, due to the high chemical and structural information contents of this spectroscopic technique. However, measurements of biological tissues are usually hampered by typically low-level signals and by the presence of noise and background components caused by light diffusion or fluorescence processes. In order to overcome these problems, a numerical method based on discrete wavelet transform is used for the analysis of data from μ-RS measurements performed in vitro on animal (pig and chicken) tissue samples and, in a preliminary form, on human skin and oral tissue biopsy from normal subjects. Visible light μ-RS was performed using a He-Ne laser and a monochromator with a liquid nitrogen cooled charge coupled device equipped with a grating of 1800 grooves mm-1. The validity of the proposed data procedure has been tested on the well-characterized Raman spectra of reference acetylsalicylic acid samples.

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.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

Characterization and Simulation of Gunfire with Wavelets

DOE PAGES

Smallwood, David O.

1999-01-01

Gunfire is used as an example to show how the wavelet transform can be used to characterize and simulate nonstationary random events when an ensemble of events is available. The structural response to nearby firing of a high-firing rate gun has been characterized in several ways as a nonstationary random process. The current paper will explore a method to describe the nonstationary random process using a wavelet transform. The gunfire record is broken up into a sequence of transient waveforms each representing the response to the firing of a single round. A wavelet transform is performed on each of thesemore » records. The gunfire is simulated by generating realizations of records of a single-round firing by computing an inverse wavelet transform from Gaussian random coefficients with the same mean and standard deviation as those estimated from the previously analyzed gunfire record. The individual records are assembled into a realization of many rounds firing. A second-order correction of the probability density function is accomplished with a zero memory nonlinear function. The method is straightforward, easy to implement, and produces a simulated record much like the measured gunfire record.« less

Observation of ion acoustic multi-Peregrine solitons in multicomponent plasma with negative ions

NASA Astrophysics Data System (ADS)

Pathak, Pallabi; Sharma, Sumita K.; Nakamura, Y.; Bailung, H.

2017-12-01

The evolution of the multi-Peregrine soliton is investigated in a multicomponent plasma and found to be critically dependent on the initial bound state. Formation and splitting of Peregrine soliton, broadening of the frequency spectra provide clear evidence of nonlinear-dispersive focusing due to modulational instability, a generic mechanism for rogue wave formation in which amplitude and phase modulation grow as a result of interplay between nonlinearity and anomalous dispersion. We have shown that initial perturbation parameters (amplitude & temporal length) critically determine the number of solitons evolution. It is also found that a sufficiently long wavelength perturbation of high amplitude invoke strong nonlinearity to generate a supercontinuum state. Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT) analysis of the experimental time series data clearly indicate the spatio-temporal localization and spectral broadening. We consider a model based on the frame work of Nonlinear Schrodinger equation (NLSE) to explain the experimental observations.

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.

Signal processing method and system for noise removal and signal extraction

DOEpatents

Fu, Chi Yung; Petrich, Loren

2009-04-14

A signal processing method and system combining smooth level wavelet pre-processing together with artificial neural networks all in the wavelet domain for signal denoising and extraction. Upon receiving a signal corrupted with noise, an n-level decomposition of the signal is performed using a discrete wavelet transform to produce a smooth component and a rough component for each decomposition level. The n.sup.th level smooth component is then inputted into a corresponding neural network pre-trained to filter out noise in that component by pattern recognition in the wavelet domain. Additional rough components, beginning at the highest level, may also be retained and inputted into corresponding neural networks pre-trained to filter out noise in those components also by pattern recognition in the wavelet domain. In any case, an inverse discrete wavelet transform is performed on the combined output from all the neural networks to recover a clean signal back in the time domain.

Use of the Morlet mother wavelet in the frequency-scale domain decomposition technique for the modal identification of ambient vibration responses

NASA Astrophysics Data System (ADS)

Le, Thien-Phu

2017-10-01

The frequency-scale domain decomposition technique has recently been proposed for operational modal analysis. The technique is based on the Cauchy mother wavelet. In this paper, the approach is extended to the Morlet mother wavelet, which is very popular in signal processing due to its superior time-frequency localization. Based on the regressive form and an appropriate norm of the Morlet mother wavelet, the continuous wavelet transform of the power spectral density of ambient responses enables modes in the frequency-scale domain to be highlighted. Analytical developments first demonstrate the link between modal parameters and the local maxima of the continuous wavelet transform modulus. The link formula is then used as the foundation of the proposed modal identification method. Its practical procedure, combined with the singular value decomposition algorithm, is presented step by step. The proposition is finally verified using numerical examples and a laboratory test.

Gas demand forecasting by a new artificial intelligent algorithm

NASA Astrophysics Data System (ADS)

Khatibi. B, Vahid; Khatibi, Elham

2012-01-01

Energy demand forecasting is a key issue for consumers and generators in all energy markets in the world. This paper presents a new forecasting algorithm for daily gas demand prediction. This algorithm combines a wavelet transform and forecasting models such as multi-layer perceptron (MLP), linear regression or GARCH. The proposed method is applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the proposed method.

Invariant 2D object recognition using the wavelet transform and structured neural networks

NASA Astrophysics Data System (ADS)

Khalil, Mahmoud I.; Bayoumi, Mohamed M.

1999-03-01

This paper applies the dyadic wavelet transform and the structured neural networks approach to recognize 2D objects under translation, rotation, and scale transformation. Experimental results are presented and compared with traditional methods. The experimental results showed that this refined technique successfully classified the objects and outperformed some traditional methods especially in the presence of noise.

Use of switched capacitor filters to implement the discrete wavelet transform

NASA Technical Reports Server (NTRS)

Kaiser, Kraig E.; Peterson, James N.

1993-01-01

This paper analyzes the use of IIR switched capacitor filters to implement the discrete wavelet transform and the inverse transform, using quadrature mirror filters (QMF) which have the necessary symmetry for reconstruction of the data. This is done by examining the sensitivity of the QMF transforms to the manufacturing variance in the desired capacitances. The performance is evaluated at the outputs of the separate filter stages and the error in the reconstruction of the inverse transform is compared with the desired results.

Implementation in an FPGA circuit of Edge detection algorithm based on the Discrete Wavelet Transforms

NASA Astrophysics Data System (ADS)

Bouganssa, Issam; Sbihi, Mohamed; Zaim, Mounia

2017-07-01

The 2D Discrete Wavelet Transform (DWT) is a computationally intensive task that is usually implemented on specific architectures in many imaging systems in real time. In this paper, a high throughput edge or contour detection algorithm is proposed based on the discrete wavelet transform. A technique for applying the filters on the three directions (Horizontal, Vertical and Diagonal) of the image is used to present the maximum of the existing contours. The proposed architectures were designed in VHDL and mapped to a Xilinx Sparten6 FPGA. The results of the synthesis show that the proposed architecture has a low area cost and can operate up to 100 MHz, which can perform 2D wavelet analysis for a sequence of images while maintaining the flexibility of the system to support an adaptive algorithm.

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

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

PubMed

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

2015-11-17

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.

Application of the wavelet transform for speech processing

NASA Technical Reports Server (NTRS)

Maes, Stephane

1994-01-01

Speaker identification and word spotting will shortly play a key role in space applications. An approach based on the wavelet transform is presented that, in the context of the 'modulation model,' enables extraction of speech features which are used as input for the classification process.

Design and application of discrete wavelet packet transform based multiresolution controller for liquid level system.

PubMed

Paul, Rimi; Sengupta, Anindita

2017-11-01

A new controller based on discrete wavelet packet transform (DWPT) for liquid level system (LLS) has been presented here. This controller generates control signal using node coefficients of the error signal which interprets many implicit phenomena such as process dynamics, measurement noise and effect of external disturbances. Through simulation results on LLS problem, this controller is shown to perform faster than both the discrete wavelet transform based controller and conventional proportional integral controller. Also, it is more efficient in terms of its ability to provide better noise rejection. To overcome the wind up phenomenon by considering the saturation due to presence of actuator, anti-wind up technique is applied to the conventional PI controller and compared to the wavelet packet transform based controller. In this case also, packet controller is found better than the other ones. This similar work has been extended for analogous first order RC plant as well as second order plant also. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Koren, Iztok; Yang, Wuhai; Taylor, Fred J.

1995-04-01

This paper describes two approaches for accomplishing interactive feature analysis by overcomplete multiresolution representations. We show quantitatively that transform coefficients, modified by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a filter bank representing a steerable dyadic wavelet transform that can be used for multiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicion) areas (lesions). Features extracted from multiscale representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology can improve changes of early detection while requiring less time to evaluate mammograms for most patients.

The whole number axis integer linear transformation reversible information hiding algorithm on wavelet domain

NASA Astrophysics Data System (ADS)

Jiang, Zhuo; Xie, Chengjun

2013-12-01

This paper improved the algorithm of reversible integer linear transform on finite interval [0,255], which can realize reversible integer linear transform in whole number axis shielding data LSB (least significant bit). Firstly, this method use integer wavelet transformation based on lifting scheme to transform the original image, and select the transformed high frequency areas as information hiding area, meanwhile transform the high frequency coefficients blocks in integer linear way and embed the secret information in LSB of each coefficient, then information hiding by embedding the opposite steps. To extract data bits and recover the host image, a similar reverse procedure can be conducted, and the original host image can be lossless recovered. The simulation experimental results show that this method has good secrecy and concealment, after conducted the CDF (m, n) and DD (m, n) series of wavelet transformed. This method can be applied to information security domain, such as medicine, law and military.

UV Spectrophotometric Simultaneous Determination of Paracetamol and Ibuprofen in Combined Tablets by Derivative and Wavelet Transforms

PubMed Central

Hoang, Vu Dang; Ly, Dong Thi Ha; Tho, Nguyen Huu; Minh Thi Nguyen, Hue

2014-01-01

The application of first-order derivative and wavelet transforms to UV spectra and ratio spectra was proposed for the simultaneous determination of ibuprofen and paracetamol in their combined tablets. A new hybrid approach on the combined use of first-order derivative and wavelet transforms to spectra was also discussed. In this application, DWT (sym6 and haar), CWT (mexh), and FWT were optimized to give the highest spectral recoveries. Calibration graphs in the linear concentration ranges of ibuprofen (12–32 mg/L) and paracetamol (20–40 mg/L) were obtained by measuring the amplitudes of the transformed signals. Our proposed spectrophotometric methods were statistically compared to HPLC in terms of precision and accuracy. PMID:24949492

UV spectrophotometric simultaneous determination of paracetamol and ibuprofen in combined tablets by derivative and wavelet transforms.

PubMed

Hoang, Vu Dang; Ly, Dong Thi Ha; Tho, Nguyen Huu; Nguyen, Hue Minh Thi

2014-01-01

The application of first-order derivative and wavelet transforms to UV spectra and ratio spectra was proposed for the simultaneous determination of ibuprofen and paracetamol in their combined tablets. A new hybrid approach on the combined use of first-order derivative and wavelet transforms to spectra was also discussed. In this application, DWT (sym6 and haar), CWT (mexh), and FWT were optimized to give the highest spectral recoveries. Calibration graphs in the linear concentration ranges of ibuprofen (12-32 mg/L) and paracetamol (20-40 mg/L) were obtained by measuring the amplitudes of the transformed signals. Our proposed spectrophotometric methods were statistically compared to HPLC in terms of precision and accuracy.

Rejection of the maternal electrocardiogram in the electrohysterogram signal.

PubMed

Leman, H; Marque, C

2000-08-01

The electrohysterogram (EHG) signal is mainly corrupted by the mother's electrocardiogram (ECG), which remains present despite analog filtering during acquisition. Wavelets are a powerful denoising tool and have already proved their efficiency on the EHG. In this paper, we propose a new method that employs the redundant wavelet packet transform. We first study wavelet packet coefficient histograms and propose an algorithm to automatically detect the histogram mode number. Using a new criterion, we compute a best basis adapted to the denoising. After EHG wavelet packet coefficient thresholding in the selected basis, the inverse transform is applied. The ECG seems to be very efficiently removed.

Structural health monitoring approach for detecting ice accretion on bridge cable using the Haar Wavelet Transform

NASA Astrophysics Data System (ADS)

Andre, Julia; Kiremidjian, Anne; Liao, Yizheng; Georgakis, Christos; Rajagopal, Ram

2016-04-01

Ice accretion on cables of bridge structures poses serious risk to the structure as well as to vehicular traffic when the ice falls onto the road. Detection of ice formation, quantification of the amount of ice accumulated, and prediction of icefalls will increase the safety and serviceability of the structure. In this paper, an ice accretion detection algorithm is presented based on the Continuous Wavelet Transform (CWT). In the proposed algorithm, the acceleration signals obtained from bridge cables are transformed using wavelet method. The damage sensitive features (DSFs) are defined as a function of the wavelet energy at specific wavelet scales. It is found that as ice accretes on the cables, the mass of cable increases, thus changing the wavelet energies. Hence, the DSFs can be used to track the change of cables mass. To validate the proposed algorithm, we use the data collected from a laboratory experiment conducted at the Technical University of Denmark (DTU). In this experiment, a cable was placed in a wind tunnel as ice volume grew progressively. Several accelerometers were installed at various locations along the testing cable to collect vibration signals.

A wavelet and least square filter based spatial-spectral denoising approach of hyperspectral imagery

NASA Astrophysics Data System (ADS)

Li, Ting; Chen, Xiao-Mei; Chen, Gang; Xue, Bo; Ni, Guo-Qiang

2009-11-01

Noise reduction is a crucial step in hyperspectral imagery pre-processing. Based on sensor characteristics, the noise of hyperspectral imagery represents in both spatial and spectral domain. However, most prevailing denosing techniques process the imagery in only one specific domain, which have not utilized multi-domain nature of hyperspectral imagery. In this paper, a new spatial-spectral noise reduction algorithm is proposed, which is based on wavelet analysis and least squares filtering techniques. First, in the spatial domain, a new stationary wavelet shrinking algorithm with improved threshold function is utilized to adjust the noise level band-by-band. This new algorithm uses BayesShrink for threshold estimation, and amends the traditional soft-threshold function by adding shape tuning parameters. Comparing with soft or hard threshold function, the improved one, which is first-order derivable and has a smooth transitional region between noise and signal, could save more details of image edge and weaken Pseudo-Gibbs. Then, in the spectral domain, cubic Savitzky-Golay filter based on least squares method is used to remove spectral noise and artificial noise that may have been introduced in during the spatial denoising. Appropriately selecting the filter window width according to prior knowledge, this algorithm has effective performance in smoothing the spectral curve. The performance of the new algorithm is experimented on a set of Hyperion imageries acquired in 2007. The result shows that the new spatial-spectral denoising algorithm provides more significant signal-to-noise-ratio improvement than traditional spatial or spectral method, while saves the local spectral absorption features better.

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

2015-12-07

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 .

Method for determining formation quality factor from seismic data

DOEpatents

Taner, M. Turhan; Treitel, Sven

2005-08-16

A method is disclosed for calculating the quality factor Q from a seismic data trace. The method includes calculating a first and a second minimum phase inverse wavelet at a first and a second time interval along the seismic data trace, synthetically dividing the first wavelet by the second wavelet, Fourier transforming the result of the synthetic division, calculating the logarithm of this quotient of Fourier transforms and determining the slope of a best fit line to the logarithm of the quotient.

A study of renal blood flow regulation using the discrete wavelet transform

NASA Astrophysics Data System (ADS)

Pavlov, Alexey N.; Pavlova, Olga N.; Mosekilde, Erik; Sosnovtseva, Olga V.

2010-02-01

In this paper we provide a way to distinguish features of renal blood flow autoregulation mechanisms in normotensive and hypertensive rats based on the discrete wavelet transform. Using the variability of the wavelet coefficients we show distinctions that occur between the normal and pathological states. A reduction of this variability in hypertension is observed on the microscopic level of the blood flow in efferent arteriole of single nephrons. This reduction is probably associated with higher flexibility of healthy cardiovascular system.

Phase retrieval of singular scalar light fields using a two-dimensional directional wavelet transform and a spatial carrier.

PubMed

Federico, Alejandro; Kaufmann, Guillermo H

2008-10-01

We evaluate a method based on the two-dimensional directional wavelet transform and the introduction of a spatial carrier to retrieve optical phase distributions in singular scalar light fields. The performance of the proposed phase-retrieval method is compared with an approach based on Fourier transform. The advantages and limitations of the proposed method are discussed.

Karyotyping of Chromosomes in Human Bronchial Epithelial Cells Transformed by High Energy Fe Ions

NASA Technical Reports Server (NTRS)

Yeshitla, Samrawit; Zhang, Ye; Park, Seongmi; Story, Michael D.; Wilson, Bobby; Wu, Honglu

2015-01-01

Lung cancer induced from exposures to space radiation is one of the most significant health risks for long-term space travels. Evidences show that low- and high- Linear energy transfer (LET)-induced transformation of normal human bronchial epithelial cells (HBEC) that are immortalized through the expression of Cdk4 and hTERT. The cells were exposed to gamma rays and high-energy Fe ions for the selection of transformed clones. Transformed HBEC are identified and analyzed chromosome aberrations (i.e. genomic instability) using the multi-color fluorescent in situ hybridization (mFISH), as well as the multi-banding in situ hybridization (mBAND) techniques. Our results show chromosomal translocations between different chromosomes and several of the breaks occurred in the q-arm of chromosome 3. We also identified copy number variations between the transformed and the parental HBEC regardless of the exposure conditions. We observed chromosomal aberrations in the lowand high-LET radiation-induced transformed clones and they are imperfectly different from clones obtain in spontaneous soft agar growth.

Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT)

PubMed Central

Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao

2013-01-01

[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems. PMID:24259846

Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT).

PubMed

Murugappan, Murugappan; Murugappan, Subbulakshmi; Zheng, Bong Siao

2013-07-01

[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness - 50.28%; happiness - 79.03%; fear - 77.78%; disgust - 88.69%; and neutral - 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.

The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks

NASA Astrophysics Data System (ADS)

Chiu, Alan W. L.; Jahromi, Shokrollah S.; Khosravani, Houman; Carlen, Peter L.; Bardakjian, Berj L.

2006-03-01

The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42 Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 ± 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.

Wavelet filtered shifted phase-encoded joint transform correlation for face recognition

NASA Astrophysics Data System (ADS)

Moniruzzaman, Md.; Alam, Mohammad S.

2017-05-01

A new wavelet-filtered-based Shifted- phase-encoded Joint Transform Correlation (WPJTC) technique has been proposed for efficient face recognition. The proposed technique uses discrete wavelet decomposition for preprocessing and can effectively accommodate various 3D facial distortions, effects of noise, and illumination variations. After analyzing different forms of wavelet basis functions, an optimal method has been proposed by considering the discrimination capability and processing speed as performance trade-offs. The proposed technique yields better correlation discrimination compared to alternate pattern recognition techniques such as phase-shifted phase-encoded fringe-adjusted joint transform correlator. The performance of the proposed WPJTC has been tested using the Yale facial database and extended Yale facial database under different environments such as illumination variation, noise, and 3D changes in facial expressions. Test results show that the proposed WPJTC yields better performance compared to alternate JTC based face recognition techniques.

Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

NASA Astrophysics Data System (ADS)

Syahputra, M. F.; Situmeang, S. I. G.; Rahmat, R. F.; Budiarto, R.

2017-04-01

The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.

Single Channel EEG Artifact Identification Using Two-Dimensional Multi-Resolution Analysis.

PubMed

Taherisadr, Mojtaba; Dehzangi, Omid; Parsaei, Hossein

2017-12-13

As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded by signal processing methodologies for various health monitoring purposes. However, EEG recordings are contaminated by other interferences, particularly facial and ocular artifacts generated by the user. This is specifically an issue during continuous EEG recording sessions, and is therefore a key step in using EEG signals for either physiological monitoring and diagnosis or brain-computer interface to identify such artifacts from useful EEG components. In this study, we aim to design a new generic framework in order to process and characterize EEG recording as a multi-component and non-stationary signal with the aim of localizing and identifying its component (e.g., artifact). In the proposed method, we gather three complementary algorithms together to enhance the efficiency of the system. Algorithms include time-frequency (TF) analysis and representation, two-dimensional multi-resolution analysis (2D MRA), and feature extraction and classification. Then, a combination of spectro-temporal and geometric features are extracted by combining key instantaneous TF space descriptors, which enables the system to characterize the non-stationarities in the EEG dynamics. We fit a curvelet transform (as a MRA method) to 2D TF representation of EEG segments to decompose the given space to various levels of resolution. Such a decomposition efficiently improves the analysis of the TF spaces with different characteristics (e.g., resolution). Our experimental results demonstrate that the combination of expansion to TF space, analysis using MRA, and extracting a set of suitable features and applying a proper predictive model is effective in enhancing the EEG artifact identification performance. We also compare the performance of the designed system with another common EEG signal processing technique-namely, 1D wavelet transform. Our experimental results reveal that the proposed method outperforms 1D wavelet.

Analysis of autostereoscopic three-dimensional images using multiview wavelets.

PubMed

Saveljev, Vladimir; Palchikova, Irina

2016-08-10

We propose that multiview wavelets can be used in processing multiview images. The reference functions for the synthesis/analysis of multiview images are described. The synthesized binary images were observed experimentally as three-dimensional visual images. The symmetric multiview B-spline wavelets are proposed. The locations recognized in the continuous wavelet transform correspond to the layout of the test objects. The proposed wavelets can be applied to the multiview, integral, and plenoptic images.

Determination of secondary flow morphologies by wavelet analysis in a curved artery model with physiological inflow

NASA Astrophysics Data System (ADS)

Bulusu, Kartik V.; Hussain, Shadman; Plesniak, Michael W.

2014-11-01

Secondary flow vortical patterns in arterial curvatures have the potential to affect several cardiovascular phenomena, e.g., progression of atherosclerosis by altering wall shear stresses, carotid atheromatous disease, thoracic aortic aneurysms and Marfan's syndrome. Temporal characteristics of secondary flow structures vis-à-vis physiological (pulsatile) inflow waveform were explored by continuous wavelet transform (CWT) analysis of phase-locked, two-component, two-dimensional particle image velocimeter data. Measurements were made in a 180° curved artery test section upstream of the curvature and at the 90° cross-sectional plane. Streamwise, upstream flow rate measurements were analyzed using a one-dimensional antisymmetric wavelet. Cross-stream measurements at the 90° location of the curved artery revealed interesting multi-scale, multi-strength coherent secondary flow structures. An automated process for coherent structure detection and vortical feature quantification was applied to large ensembles of PIV data. Metrics such as the number of secondary flow structures, their sizes and strengths were generated at every discrete time instance of the physiological inflow waveform. An autonomous data post-processing method incorporating two-dimensional CWT for coherent structure detection was implemented. Loss of coherence in secondary flow structures during the systolic deceleration phase is observed in accordance with previous research. The algorithmic approach presented herein further elucidated the sensitivity and dependence of morphological changes in secondary flow structures on quasiperiodicity and magnitude of temporal gradients in physiological inflow conditions.

A Wavelet Neural Network Optimal Control Model for Traffic-Flow Prediction in Intelligent Transport Systems

NASA Astrophysics Data System (ADS)

Huang, Darong; Bai, Xing-Rong

Based on wavelet transform and neural network theory, a traffic-flow prediction model, which was used in optimal control of Intelligent Traffic system, is constructed. First of all, we have extracted the scale coefficient and wavelet coefficient from the online measured raw data of traffic flow via wavelet transform; Secondly, an Artificial Neural Network model of Traffic-flow Prediction was constructed and trained using the coefficient sequences as inputs and raw data as outputs; Simultaneous, we have designed the running principium of the optimal control system of traffic-flow Forecasting model, the network topological structure and the data transmitted model; Finally, a simulated example has shown that the technique is effectively and exactly. The theoretical results indicated that the wavelet neural network prediction model and algorithms have a broad prospect for practical application.

Feature Extraction for Bearing Prognostics and Health Management (PHM) - A Survey (Preprint)

DTIC Science & Technology

2008-05-01

Envelope analysis • Cepstrum analysis • Higher order spectrum • Short-time Fourier Transform (STFT) • Wigner - Ville distribution ( WVD ) • Empirical mode...techniques are the short-time Fourier transform (STFT), the Wigner - Ville distribution , and the wavelet transform. In this paper we categorize wavelets...diagnosis have shown in many publications, for example, [22]. b) Wigner – Ville distribution : The afore-mentioned STFT is conceptually simple. However

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

Simultaneous F 0-F 1 modifications of Arabic for the improvement of natural-sounding

NASA Astrophysics Data System (ADS)

Ykhlef, F.; Bensebti, M.

2013-03-01

Pitch (F 0) modification is one of the most important problems in the area of speech synthesis. Several techniques have been developed in the literature to achieve this goal. The main restrictions of these techniques are in the modification range and the synthesised speech quality, intelligibility and naturalness. The control of formants in a spoken language can significantly improve the naturalness of the synthesised speech. This improvement is mainly dependent on the control of the first formant (F 1). Inspired by this observation, this article proposes a new approach that modifies both F 0 and F 1 of Arabic voiced sounds in order to improve the naturalness of the pitch shifted speech. The developed strategy takes a parallel processing approach, in which the analysis segments are decomposed into sub-bands in the wavelet domain, modified in the desired sub-band by using a resampling technique and reconstructed without affecting the remained sub-bands. Pitch marking and voicing detection are performed in the frequency decomposition step based on the comparison of the multi-level approximation and detail signals. The performance of the proposed technique is evaluated by listening tests and compared to the pitch synchronous overlap and add (PSOLA) technique in the third approximation level. Experimental results have shown that the manipulation in the wavelet domain of F 0 in conjunction with F 1 guarantees natural-sounding of the synthesised speech compared to the classical pitch modification technique. This improvement was appropriate for high pitch modifications.

A simple structure wavelet transform circuit employing function link neural networks and SI filters

NASA Astrophysics Data System (ADS)

Mu, Li; Yigang, He

2016-12-01

Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

Wavelet processing techniques for digital mammography

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu

1992-09-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through multiresolution representations. We show that efficient (nonredundant) representations may be identified from digital mammography and used to enhance specific mammographic features within a continuum of scale space. The multiresolution decomposition of wavelet transforms provides a natural hierarchy in which to embed an interactive paradigm for accomplishing scale space feature analysis. Similar to traditional coarse to fine matching strategies, the radiologist may first choose to look for coarse features (e.g., dominant mass) within low frequency levels of a wavelet transform and later examine finer features (e.g., microcalcifications) at higher frequency levels. In addition, features may be extracted by applying geometric constraints within each level of the transform. Choosing wavelets (or analyzing functions) that are simultaneously localized in both space and frequency, results in a powerful methodology for image analysis. Multiresolution and orientation selectivity, known biological mechanisms in primate vision, are ingrained in wavelet representations and inspire the techniques presented in this paper. Our approach includes local analysis of complete multiscale representations. Mammograms are reconstructed from wavelet representations, enhanced by linear, exponential and constant weight functions through scale space. By improving the visualization of breast pathology we can improve the chances of early detection of breast cancers (improve quality) while requiring less time to evaluate mammograms for most patients (lower costs).

Super-resolution algorithm based on sparse representation and wavelet preprocessing for remote sensing imagery

NASA Astrophysics Data System (ADS)

Ren, Ruizhi; Gu, Lingjia; Fu, Haoyang; Sun, Chenglin

2017-04-01

An effective super-resolution (SR) algorithm is proposed for actual spectral remote sensing images based on sparse representation and wavelet preprocessing. The proposed SR algorithm mainly consists of dictionary training and image reconstruction. Wavelet preprocessing is used to establish four subbands, i.e., low frequency, horizontal, vertical, and diagonal high frequency, for an input image. As compared to the traditional approaches involving the direct training of image patches, the proposed approach focuses on the training of features derived from these four subbands. The proposed algorithm is verified using different spectral remote sensing images, e.g., moderate-resolution imaging spectroradiometer (MODIS) images with different bands, and the latest Chinese Jilin-1 satellite images with high spatial resolution. According to the visual experimental results obtained from the MODIS remote sensing data, the SR images using the proposed SR algorithm are superior to those using a conventional bicubic interpolation algorithm or traditional SR algorithms without preprocessing. Fusion algorithms, e.g., standard intensity-hue-saturation, principal component analysis, wavelet transform, and the proposed SR algorithms are utilized to merge the multispectral and panchromatic images acquired by the Jilin-1 satellite. The effectiveness of the proposed SR algorithm is assessed by parameters such as peak signal-to-noise ratio, structural similarity index, correlation coefficient, root-mean-square error, relative dimensionless global error in synthesis, relative average spectral error, spectral angle mapper, and the quality index Q4, and its performance is better than that of the standard image fusion algorithms.

Information retrieval system utilizing wavelet transform

DOEpatents

Brewster, Mary E.; Miller, Nancy E.

2000-01-01

A method for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.

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.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

WaveJava: Wavelet-based network computing

NASA Astrophysics Data System (ADS)

Ma, Kun; Jiao, Licheng; Shi, Zhuoer

1997-04-01

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

Code generator for implementing dual tree complex wavelet transform on reconfigurable architectures for mobile applications

PubMed Central

Canbay, Ferhat; Levent, Vecdi Emre; Serbes, Gorkem; Ugurdag, H. Fatih; Goren, Sezer

2016-01-01

The authors aimed to develop an application for producing different architectures to implement dual tree complex wavelet transform (DTCWT) having near shift-invariance property. To obtain a low-cost and portable solution for implementing the DTCWT in multi-channel real-time applications, various embedded-system approaches are realised. For comparison, the DTCWT was implemented in C language on a personal computer and on a PIC microcontroller. However, in the former approach portability and in the latter desired speed performance properties cannot be achieved. Hence, implementation of the DTCWT on a reconfigurable platform such as field programmable gate array, which provides portable, low-cost, low-power, and high-performance computing, is considered as the most feasible solution. At first, they used the system generator DSP design tool of Xilinx for algorithm design. However, the design implemented by using such tools is not optimised in terms of area and power. To overcome all these drawbacks mentioned above, they implemented the DTCWT algorithm by using Verilog Hardware Description Language, which has its own difficulties. To overcome these difficulties, simplify the usage of proposed algorithms and the adaptation procedures, a code generator program that can produce different architectures is proposed. PMID:27733925

A Time-Frequency Analysis of the Effects of Solar Activities on Tropospheric Thermodynamics

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Kyle, H. Lee; Wharton, Stephen W. (Technical Monitor)

2001-01-01

Whether the Sun has significantly influenced the climate during the last century has been under extensive debates for almost two decades. Since the solar irradiance varies very little in a solar cycle, it is puzzling that some geophysical parameters show proportionally large variations which appear to be responding to the solar cycles. For example, variation in low altitude clouds is shown correlated with solar cycle, and the onset of Forbush decrease is shown correlated with the reduction of the vorticity area index. A possible sun-climate connection is that galactic cosmic rays modulated by solar activities influence cloud formation. In this paper, we apply wavelet transform to satellite and surface data to examine this hypothesis. Data analyzed include the time series for solar irradiance, sunspots, UV index, temperature, cloud coverage, and neutron counter measurements. The interactions among the elements in the Earth System under the external and internal forcings give out very complex signals.The periodicity of the forcings or signals could range widely. Since wavelet transforms can analyze multi-scale phenomena that are both localized in frequency and time, it is a very useful technique for detecting, understanding and monitoring climate changes.

Code generator for implementing dual tree complex wavelet transform on reconfigurable architectures for mobile applications.

PubMed

Canbay, Ferhat; Levent, Vecdi Emre; Serbes, Gorkem; Ugurdag, H Fatih; Goren, Sezer; Aydin, Nizamettin

2016-09-01

The authors aimed to develop an application for producing different architectures to implement dual tree complex wavelet transform (DTCWT) having near shift-invariance property. To obtain a low-cost and portable solution for implementing the DTCWT in multi-channel real-time applications, various embedded-system approaches are realised. For comparison, the DTCWT was implemented in C language on a personal computer and on a PIC microcontroller. However, in the former approach portability and in the latter desired speed performance properties cannot be achieved. Hence, implementation of the DTCWT on a reconfigurable platform such as field programmable gate array, which provides portable, low-cost, low-power, and high-performance computing, is considered as the most feasible solution. At first, they used the system generator DSP design tool of Xilinx for algorithm design. However, the design implemented by using such tools is not optimised in terms of area and power. To overcome all these drawbacks mentioned above, they implemented the DTCWT algorithm by using Verilog Hardware Description Language, which has its own difficulties. To overcome these difficulties, simplify the usage of proposed algorithms and the adaptation procedures, a code generator program that can produce different architectures is proposed.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring.

PubMed

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-05-21

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

Identification of unusual events in multichannel bridge monitoring data using wavelet transform and outlier analysis

NASA Astrophysics Data System (ADS)

Omenzetter, Piotr; Brownjohn, James M. W.; Moyo, Pilate

2003-08-01

Continuously operating instrumented structural health monitoring (SHM) systems are becoming a practical alternative to replace visual inspection for assessment of condition and soundness of civil infrastructure. However, converting large amount of data from an SHM system into usable information is a great challenge to which special signal processing techniques must be applied. This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure in Singapore and operating continuously for a long time. Such events may result, among other causes, from sudden settlement of foundation, ground movement, excessive traffic load or failure of post-tensioning cables. A method of outlier detection in multivariate data has been applied to the problem of finding and localizing sudden events in the strain data. For sharp discrimination of abrupt strain changes from slowly varying ones wavelet transform has been used. The proposed method has been successfully tested using known events recorded during construction of the bridge, and later effectively used for detection of anomalous post-construction events.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

PubMed Central

Dong, Shaopeng; Yuan, Mei; Wang, Qiusheng; Liang, Zhiling

2018-01-01

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures. PMID:29883411

The Brera Multiscale Wavelet ROSAT HRI Source Catalog. I. The Algorithm

NASA Astrophysics Data System (ADS)

Lazzati, Davide; Campana, Sergio; Rosati, Piero; Panzera, Maria Rosa; Tagliaferri, Gianpiero

1999-10-01

We present a new detection algorithm based on the wavelet transform for the analysis of high-energy astronomical images. The wavelet transform, because of its multiscale structure, is suited to the optimal detection of pointlike as well as extended sources, regardless of any loss of resolution with the off-axis angle. Sources are detected as significant enhancements in the wavelet space, after the subtraction of the nonflat components of the background. Detection thresholds are computed through Monte Carlo simulations in order to establish the expected number of spurious sources per field. The source characterization is performed through a multisource fitting in the wavelet space. The procedure is designed to correctly deal with very crowded fields, allowing for the simultaneous characterization of nearby sources. To obtain a fast and reliable estimate of the source parameters and related errors, we apply a novel decimation technique that, taking into account the correlation properties of the wavelet transform, extracts a subset of almost independent coefficients. We test the performance of this algorithm on synthetic fields, analyzing with particular care the characterization of sources in poor background situations, where the assumption of Gaussian statistics does not hold. In these cases, for which standard wavelet algorithms generally provide underestimated errors, we infer errors through a procedure that relies on robust basic statistics. Our algorithm is well suited to the analysis of images taken with the new generation of X-ray instruments equipped with CCD technology, which will produce images with very low background and/or high source density.

Digital transceiver implementation for wavelet packet modulation

NASA Astrophysics Data System (ADS)

Lindsey, Alan R.; Dill, Jeffrey C.

1998-03-01

Current transceiver designs for wavelet-based communication systems are typically reliant on analog waveform synthesis, however, digital processing is an important part of the eventual success of these techniques. In this paper, a transceiver implementation is introduced for the recently introduced wavelet packet modulation scheme which moves the analog processing as far as possible toward the antenna. The transceiver is based on the discrete wavelet packet transform which incorporates level and node parameters for generalized computation of wavelet packets. In this transform no particular structure is imposed on the filter bank save dyadic branching, and a maximum level which is specified a priori and dependent mainly on speed and/or cost considerations. The transmitter/receiver structure takes a binary sequence as input and, based on the desired time- frequency partitioning, processes the signal through demultiplexing, synthesis, analysis, multiplexing and data determination completely in the digital domain - with exception of conversion in and out of the analog domain for transmission.

Multi-temporal Scale Analysis of Environmental Control on Net Ecosystem Exchange of CO2 in Forest Ecosystems

NASA Astrophysics Data System (ADS)

Zhang, Mi; Yu, Guirui; Zhuang, Jie; Gentry, Randy; Koirala, Shesh; Zhang, Leiming; Sun, Xiaomin; Han, Shijie; Yan, Junhua

2013-04-01

Multi-temporal scale analysis of environmental control on forest ecosystem carbon budget is a basis for understanding the responses and adaptation of forest carbon cycle to climate change. In this study, we chose two typical forest ecosystems, Changbaishan temperate mixed forest (CBS) in northeastern China and Dinghushan subtropical evergreen broad-leaved forest (DHS) in southern China to identify the changes in environmental control on net ecosystem exchange of carbon dioxide (NEE) with the temporal scales. The analysis was made based on the flux and routine meteorological data measured during the period from 2005 to 2008. These time series data were analyzed using wavelet and cross wavelet transform. The results showed that NEE had significant daily and annual periodic variation in the two types of forest ecosystem. NEE at CBS and DHS showed semi-annual (176 days) and seasonal (88-104 days) periodic variations, respectively. Photosynthetically active radiation (PAR), vapor pressure deficient (VPD), air temperature (Ta), soil temperature (Ts, at 5-cm depth) controlled daily variation of NEE as indicated by the significant high common power of cross wavelet transform spectrums between NEE and these factors. Similarly, Ta, VPD, and precipitation (P) controlled annual variation of NEE at CBS. However, Ta, PAR, and soil water content (SWC, at 5-cm depth) dominated the annual variation of NEE at DHS. An anti-phase between NEE and PAR at daily scale in the two forest ecosystems demonstrated an agreement of the variation of NEE with PAR, with rising sunlight corresponding with increased net carbon uptake. At annual scale, phase angles between NEE and Ta and between NEE and P were -170° and 176°, respectively at CBS. At DHS, phase angle between NEE and VPD was smallest at annual scale. The results indicated that the peak of net carbon uptake seasonal variation and the peaks of P and Ta seasonal variations occurred at the same month at CBS. But, at DHS, seasonal variation of net carbon uptake was in agreement with that of VPD at annual scale. This study showed that wavelet analysis was an effective approach to identifying the temporal pattern of environmental control on carbon exchange between ecosystem and the atmosphere.

A Lossless hybrid wavelet-fractal compression for welding radiographic images.

PubMed

Mekhalfa, Faiza; Avanaki, Mohammad R N; Berkani, Daoud

2016-01-01

In this work a lossless wavelet-fractal image coder is proposed. The process starts by compressing and decompressing the original image using wavelet transformation and fractal coding algorithm. The decompressed image is removed from the original one to obtain a residual image which is coded by using Huffman algorithm. Simulation results show that with the proposed scheme, we achieve an infinite peak signal to noise ratio (PSNR) with higher compression ratio compared to typical lossless method. Moreover, the use of wavelet transform speeds up the fractal compression algorithm by reducing the size of the domain pool. The compression results of several welding radiographic images using the proposed scheme are evaluated quantitatively and compared with the results of Huffman coding algorithm.

Content Based Image Retrieval based on Wavelet Transform coefficients distribution

PubMed Central

Lamard, Mathieu; Cazuguel, Guy; Quellec, Gwénolé; Bekri, Lynda; Roux, Christian; Cochener, Béatrice

2007-01-01

In this paper we propose a content based image retrieval method for diagnosis aid in medical fields. We characterize images without extracting significant features by using distribution of coefficients obtained by building signatures from the distribution of wavelet transform. The research is carried out by computing signature distances between the query and database images. Several signatures are proposed; they use a model of wavelet coefficient distribution. To enhance results, a weighted distance between signatures is used and an adapted wavelet base is proposed. Retrieval efficiency is given for different databases including a diabetic retinopathy, a mammography and a face database. Results are promising: the retrieval efficiency is higher than 95% for some cases using an optimization process. PMID:18003013

Multi-focus image fusion algorithm using NSCT and MPCNN

NASA Astrophysics Data System (ADS)

Liu, Kang; Wang, Lianli

2018-04-01

Based on nonsubsampled contourlet transform (NSCT) and modified pulse coupled neural network (MPCNN), the paper proposes an effective method of image fusion. Firstly, the paper decomposes the source image into the low-frequency components and high-frequency components using NSCT, and then processes the low-frequency components by regional statistical fusion rules. For high-frequency components, the paper calculates the spatial frequency (SF), which is input into MPCNN model to get relevant coefficients according to the fire-mapping image of MPCNN. At last, the paper restructures the final image by inverse transformation of low-frequency and high-frequency components. Compared with the wavelet transformation (WT) and the traditional NSCT algorithm, experimental results indicate that the method proposed in this paper achieves an improvement both in human visual perception and objective evaluation. It indicates that the method is effective, practical and good performance.

Optical phase distribution evaluation by using zero order Generalized Morse Wavelet

NASA Astrophysics Data System (ADS)

Kocahan, Özlem; Elmas, Merve Naz; Durmuş, ćaǧla; Coşkun, Emre; Tiryaki, Erhan; Özder, Serhat

2017-02-01

When determining the phase from the projected fringes by using continuous wavelet transform (CWT), selection of wavelet is an important step. A new wavelet for phase retrieval from the fringe pattern with the spatial carrier frequency in the x direction is presented. As a mother wavelet, zero order generalized Morse wavelet (GMW) is chosen because of the flexible spatial and frequency localization property, and it is exactly analytic. In this study, GMW method is explained and numerical simulations are carried out to show the validity of this technique for finding the phase distributions. Results for the Morlet and Paul wavelets are compared with the results of GMW analysis.

Scaling properties of foreign exchange volatility

NASA Astrophysics Data System (ADS)

Gençay, Ramazan; Selçuk, Faruk; Whitcher, Brandon

2001-01-01

In this paper, we investigate the scaling properties of foreign exchange volatility. Our methodology is based on a wavelet multi-scaling approach which decomposes the variance of a time series and the covariance between two time series on a scale by scale basis through the application of a discrete wavelet transformation. It is shown that foreign exchange rate volatilities follow different scaling laws at different horizons. Particularly, there is a smaller degree of persistence in intra-day volatility as compared to volatility at one day and higher scales. Therefore, a common practice in the risk management industry to convert risk measures calculated at shorter horizons into longer horizons through a global scaling parameter may not be appropriate. This paper also demonstrates that correlation between the foreign exchange volatilities is the lowest at the intra-day scales but exhibits a gradual increase up to a daily scale. The correlation coefficient stabilizes at scales one day and higher. Therefore, the benefit of currency diversification is the greatest at the intra-day scales and diminishes gradually at higher scales (lower frequencies). The wavelet cross-correlation analysis also indicates that the association between two volatilities is stronger at lower frequencies.

Fabric wrinkle characterization and classification using modified wavelet coefficients and optimized support-vector-machine classifier

USDA-ARS?s Scientific Manuscript database

This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and an optimized support-vector-machine (SVM) classification scheme to characterize and classify wrinkle appearance of fabric. Fabric images were decomposed with the wavelet transform (WT), and five parame...

Research on the Characteristics of Alzheimer's Disease Using EEG

NASA Astrophysics Data System (ADS)

Ueda, Taishi; Musha, Toshimitsu; Yagi, Tohru

In this paper, we proposed a new method for diagnosing Alzheimer's disease (AD) on the basis of electroencephalograms (EEG). The method, which is termed Power Variance Function (PVF) method, indicates the variance of the power at each frequency. By using the proposed method, the power of EEG at each frequency was calculated using Wavelet transform, and the corresponding variances were defined as PVF. After the PVF histogram of 55 healthy people was approximated as a Generalized Extreme Value (GEV) distribution, we evaluated the PVF of 22 patients with AD and 25 patients with mild cognitive impairment (MCI). As a result, the values for all AD and MCI subjects were abnormal. In particular, the PVF in the θ band for MCI patients was abnormally high, and the PVF in the α band for AD patients was low.

Analysis of skin blood microflow oscillations in patients with rheumatic diseases

NASA Astrophysics Data System (ADS)

Mizeva, Irina; Makovik, Irina; Dunaev, Andrey; Krupatkin, Alexander; Meglinski, Igor

2017-07-01

Laser Doppler flowmetry (LDF) has been applied for the assessment of variation in blood microflows in patients with rheumatic diseases and healthy volunteers. Oscillations of peripheral blood microcirculation observed by LDF have been analyzed utilizing a wavelet transform. A higher amplitude of blood microflow oscillations has been observed in a high frequency band (over 0.1 Hz) in patients with rheumatic diseases. Oscillations in the high frequency band decreased in healthy volunteers in response to the cold pressor test, whereas lower frequency pulsations prevailed in patients with rheumatic diseases. A higher perfusion rate at normal conditions was observed in patients, and a weaker response to cold stimulation was observed in healthy volunteers. Analysis of blood microflow oscillations has a high potential for evaluation of mechanisms of blood flow regulation and diagnosis of vascular abnormalities associated with rheumatic diseases.

ECG signal performance de-noising assessment based on threshold tuning of dual-tree wavelet transform.

PubMed

El B'charri, Oussama; Latif, Rachid; Elmansouri, Khalifa; Abenaou, Abdenbi; Jenkal, Wissam

2017-02-07

Since the electrocardiogram (ECG) signal has a low frequency and a weak amplitude, it is sensitive to miscellaneous mixed noises, which may reduce the diagnostic accuracy and hinder the physician's correct decision on patients. The dual tree wavelet transform (DT-WT) is one of the most recent enhanced versions of discrete wavelet transform. However, threshold tuning on this method for noise removal from ECG signal has not been investigated yet. In this work, we shall provide a comprehensive study on the impact of the choice of threshold algorithm, threshold value, and the appropriate wavelet decomposition level to evaluate the ECG signal de-noising performance. A set of simulations is performed on both synthetic and real ECG signals to achieve the promised results. First, the synthetic ECG signal is used to observe the algorithm response. The evaluation results of synthetic ECG signal corrupted by various types of noise has showed that the modified unified threshold and wavelet hyperbolic threshold de-noising method is better in realistic and colored noises. The tuned threshold is then used on real ECG signals from the MIT-BIH database. The results has shown that the proposed method achieves higher performance than the ordinary dual tree wavelet transform into all kinds of noise removal from ECG signal. The simulation results indicate that the algorithm is robust for all kinds of noises with varying degrees of input noise, providing a high quality clean signal. Moreover, the algorithm is quite simple and can be used in real time ECG monitoring.

Information retrieval system utilizing wavelet transform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brewster, M.E.; Miller, N.E.

A method is disclosed for automatically partitioning an unstructured electronically formatted natural language document into its sub-topic structure. Specifically, the document is converted to an electronic signal and a wavelet transform is then performed on the signal. The resultant signal may then be used to graphically display and interact with the sub-topic structure of the document.

Quantifying the correlation between photoplethysmography and laser Doppler flowmetry microvascular low-frequency oscillations

NASA Astrophysics Data System (ADS)

Mizeva, Irina; Di Maria, Costanzo; Frick, Peter; Podtaev, Sergey; Allen, John

2015-03-01

Photoplethysmography (PPG) and laser Doppler flowmetry (LDF) are two recognized optical techniques that can track low-frequency perfusion changes in microcirculation. The aim of this study was to determine, in healthy subjects, the correlation between the techniques for specific low-frequency bands previously defined for microcirculation. Twelve healthy male subjects (age range 18 to 50 years) were studied, with PPG and LDF signals recorded for 20 min from their right and left index (PPG) and middle (LDF) fingers. Wavelet analysis comprised dividing the low-frequency integral wavelet spectrum (IWS) into five established physiological bands relating to cardiac, respiratory, myogenic, neurogenic, and endothelial activities. The correlation between PPG and LDF was quantified using wavelet correlation analysis and Spearman correlation analysis of the median IWS amplitude. The median wavelet correlation between signals (right-left side average) was 0.45 (cardiac), 0.49 (respiratory), 0.86 (myogenic), 0.91 (neurogenic), and 0.91 (endothelial). The correlation of IWS amplitude values (right-left side average) was statistically significant for the cardiac (ρ=0.64, p<0.05) and endothelial (ρ=0.62, p<0.05) bands. This pilot study has shown good correlation between PPG and LDF for specific physiological frequency bands. In particular, the results suggest that PPG has the potential to be a low-cost replacement for LDF for endothelial activity assessments.

Seismic instantaneous frequency extraction based on the SST-MAW

NASA Astrophysics Data System (ADS)

Liu, Naihao; Gao, Jinghuai; Jiang, Xiudi; Zhang, Zhuosheng; Wang, Ping

2018-06-01

The instantaneous frequency (IF) extraction of seismic data has been widely applied to seismic exploration for decades, such as detecting seismic absorption and characterizing depositional thicknesses. Based on the complex-trace analysis, the Hilbert transform (HT) can extract the IF directly, which is a traditional method and susceptible to noise. In this paper, a robust approach based on the synchrosqueezing transform (SST) is proposed to extract the IF from seismic data. In this process, a novel analytical wavelet is developed and chosen as the basic wavelet, which is called the modified analytical wavelet (MAW) and comes from the three parameter wavelet. After transforming the seismic signal into a sparse time-frequency domain via the SST taking the MAW (SST-MAW), an adaptive threshold is introduced to improve the noise immunity and accuracy of the IF extraction in a noisy environment. Note that the SST-MAW reconstructs a complex trace to extract seismic IF. To demonstrate the effectiveness of the proposed method, we apply the SST-MAW to synthetic data and field seismic data. Numerical experiments suggest that the proposed procedure yields the higher resolution and the better anti-noise performance compared to the conventional IF extraction methods based on the HT method and continuous wavelet transform. Moreover, geological features (such as the channels) are well characterized, which is insightful for further oil/gas reservoir identification.

Representation and design of wavelets using unitary circuits

NASA Astrophysics Data System (ADS)

Evenbly, Glen; White, Steven R.

2018-05-01

The representation of discrete, compact wavelet transformations (WTs) as circuits of local unitary gates is discussed. We employ a similar formalism as used in the multiscale representation of quantum many-body wave functions using unitary circuits, further cementing the relation established in the literature between classical and quantum multiscale methods. An algorithm for constructing the circuit representation of known orthogonal, dyadic, discrete WTs is presented, and the explicit representation for Daubechies wavelets, coiflets, and symlets is provided. Furthermore, we demonstrate the usefulness of the circuit formalism in designing WTs, including various classes of symmetric wavelets and multiwavelets, boundary wavelets, and biorthogonal wavelets.

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.

Application of wavelet packet transform to compressing Raman spectra data

NASA Astrophysics Data System (ADS)

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

2008-12-01

Abstract The 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. Raman spectroscopy (RS) is characterized by the frequency excursion that can show the information of molecule. Every substance has its own feature Raman spectroscopy, which can analyze the structure, components, concentrations and some other properties of samples easily. RS is a powerful analytical tool for detection and identification. There are many databases of RS. But the data of Raman spectrum needs large space to storing and long time to searching. In this paper, Wavelet packet is chosen to compress Raman spectra data of some benzene series. The obtained results show that the energy retained is as high as 99.9% after compression, while the percentage for number of zeros is 87.50%. It was concluded that the Wavelet packet has significance in compressing the RS data.

Simulation study and experimental results for detection and classification of the transient capacitor inrush current using discrete wavelet transform and artificial intelligence

NASA Astrophysics Data System (ADS)

Patcharoen, Theerasak; Yoomak, Suntiti; Ngaopitakkul, Atthapol; Pothisarn, Chaichan

2018-04-01

This paper describes the combination of discrete wavelet transforms (DWT) and artificial intelligence (AI), which are efficient techniques to identify the type of inrush current, analyze the origin and possible cause on the capacitor bank switching. The experiment setup used to verify the proposed techniques can be detected and classified the transient inrush current from normal capacitor rated current. The discrete wavelet transforms are used to detect and classify the inrush current. Then, output from wavelet is acted as input of fuzzy inference system for discriminating the type of switching transient inrush current. The proposed technique shows enhanced performance with a discrimination accuracy of 90.57%. Both simulation study and experimental results are quite satisfactory with providing the high accuracy and reliability which can be developed and implemented into a numerical overcurrent (50/51) and unbalanced current (60C) protection relay for an application of shunt capacitor bank protection in the future.

Continuous time wavelet entropy of auditory evoked potentials.

PubMed

Cek, M Emre; Ozgoren, Murat; Savaci, F Acar

2010-01-01

In this paper, the continuous time wavelet entropy (CTWE) of auditory evoked potentials (AEP) has been characterized by evaluating the relative wavelet energies (RWE) in specified EEG frequency bands. Thus, the rapid variations of CTWE due to the auditory stimulation could be detected in post-stimulus time interval. This approach removes the probability of missing the information hidden in short time intervals. The discrete time and continuous time wavelet based wavelet entropy variations were compared on non-target and target AEP data. It was observed that CTWE can also be an alternative method to analyze entropy as a function of time. 2009 Elsevier Ltd. All rights reserved.

S2LET: A code to perform fast wavelet analysis on the sphere

NASA Astrophysics Data System (ADS)

Leistedt, B.; McEwen, J. D.; Vandergheynst, P.; Wiaux, Y.

2013-10-01

We describe S2LET, a fast and robust implementation of the scale-discretised wavelet transform on the sphere. Wavelets are constructed through a tiling of the harmonic line and can be used to probe spatially localised, scale-dependent features of signals on the sphere. The reconstruction of a signal from its wavelets coefficients is made exact here through the use of a sampling theorem on the sphere. Moreover, a multiresolution algorithm is presented to capture all information of each wavelet scale in the minimal number of samples on the sphere. In addition S2LET supports the HEALPix pixelisation scheme, in which case the transform is not exact but nevertheless achieves good numerical accuracy. The core routines of S2LET are written in C and have interfaces in Matlab, IDL and Java. Real signals can be written to and read from FITS files and plotted as Mollweide projections. The S2LET code is made publicly available, is extensively documented, and ships with several examples in the four languages supported. At present the code is restricted to axisymmetric wavelets but will be extended to directional, steerable wavelets in a future release.

Why the soliton wavelet transform is useful for nonlinear dynamic phenomena

NASA Astrophysics Data System (ADS)

Szu, Harold H.

1992-10-01

If signal analyses were perfect without noise and clutters, then any transform can be equally chosen to represent the signal without any loss of information. However, if the analysis using Fourier transform (FT) happens to be a nonlinear dynamic phenomenon, the effect of nonlinearity must be postponed until a later time when a complicated mode-mode coupling is attempted without the assurance of any convergence. Alternatively, there exists a new paradigm of linear transforms called wavelet transform (WT) developed for French oil explorations. Such a WT enjoys the linear superposition principle, the computational efficiency, and the signal/noise ratio enhancement for a nonsinusoidal and nonstationary signal. Our extensions to a dynamic WT and furthermore to an adaptive WT are possible due to the fact that there exists a large set of square-integrable functions that are special solutions of the nonlinear dynamic medium and could be adopted for the WT. In order to analyze nonlinear dynamics phenomena in ocean, we are naturally led to the construction of a soliton mother wavelet. This common sense of 'pay the nonlinear price now and enjoy the linearity later' is certainly useful to probe any nonlinear dynamics. Research directions in wavelets, such as adaptivity, and neural network implementations are indicated, e.g., tailoring an active sonar profile for explorations.

Noise adaptive wavelet thresholding for speckle noise removal in optical coherence tomography.

PubMed

Zaki, Farzana; Wang, Yahui; Su, Hao; Yuan, Xin; Liu, Xuan

2017-05-01

Optical coherence tomography (OCT) is based on coherence detection of interferometric signals and hence inevitably suffers from speckle noise. To remove speckle noise in OCT images, wavelet domain thresholding has demonstrated significant advantages in suppressing noise magnitude while preserving image sharpness. However, speckle noise in OCT images has different characteristics in different spatial scales, which has not been considered in previous applications of wavelet domain thresholding. In this study, we demonstrate a noise adaptive wavelet thresholding (NAWT) algorithm that exploits the difference of noise characteristics in different wavelet sub-bands. The algorithm is simple, fast, effective and is closely related to the physical origin of speckle noise in OCT image. Our results demonstrate that NAWT outperforms conventional wavelet thresholding.

Can P wave wavelet analysis predict atrial fibrillation after coronary artery bypass grafting?

PubMed

Vassilikos, Vassilios; Dakos, George; Chouvarda, Ioanna; Karagounis, Labros; Karvounis, Haralambos; Maglaveras, Nikolaos; Mochlas, Sotirios; Spanos, Panagiotis; Louridas, George

2003-01-01

The purpose of this study was the evaluation of Morlet wavelet analysis of the P wave as a means of predicting the development of atrial fibrillation (AF) in patients who undergo coronary artery bypass grafting (CABG). The P wave was analyzed using the Morlet wavelet in 50 patients who underwent successful CABG. Group A consisted of 17 patients, 12 men and 5 women, of mean age 66.9 +/- 5.9 years, who developed AF postoperatively. Group B consisted of 33 patients, 29 men and 4 women, mean age 62.4 +/- 7.8 years, who remained arrhythmid-free. Using custom-designed software, P wave duration and wavelet parameters expressing the mean and maximum energy of the P wave were calculated from 3-channel digital recordings derived from orthogonal ECG leads (X, Y, and Z), and the vector magnitude (VM) was determined in each of 3 frequency bands (200-160 Hz, 150-100 Hz and 90-50 Hz). Univariate logistic-regression analysis identified a history of hypertension, the mean and maximum energies in all frequency bands along the Z axis, the mean and maximum energies (expressed by the VM) in the 200-160 Hz frequency band, and the mean energy in the 150-100 Hz frequency band along the Y axis as predictors for post-CABG AF. Multivariate analysis identified hypertension, ejection fraction, and the maximum energies in the 90-50 Hz frequency band along the Z and composite-vector axes as independent predictors. This multivariate model had a sensitivity of 91% and a specificity of 65%. We conclude that the Morlet wavelet analysis of the P wave is a very sensitive method of identifying patients who are likely to develop AF after CABG. The occurrence of post-CABG AF can be explained by a different activation pattern along the Z axis.

(Multi)fractality of Earthquakes by use of Wavelet Analysis

NASA Astrophysics Data System (ADS)

Enescu, B.; Ito, K.; Struzik, Z. R.

2002-12-01

The fractal character of earthquakes' occurrence, in time, space or energy, has by now been established beyond doubt and is in agreement with modern models of seismicity. Moreover, the cascade-like generation process of earthquakes -with one "main" shock followed by many aftershocks, having their own aftershocks- may well be described through multifractal analysis, well suited for dealing with such multiplicative processes. The (multi)fractal character of seismicity has been analysed so far by using traditional techniques, like the box-counting and correlation function algorithms. This work introduces a new approach for characterising the multifractal patterns of seismicity. The use of wavelet analysis, in particular of the wavelet transform modulus maxima, to multifractal analysis was pioneered by Arneodo et al. (1991, 1995) and applied successfully in diverse fields, such as the study of turbulence, the DNA sequences or the heart rate dynamics. The wavelets act like a microscope, revealing details about the analysed data at different times and scales. We introduce and perform such an analysis on the occurrence time of earthquakes and show its advantages. In particular, we analyse shallow seismicity, characterised by a high aftershock "productivity", as well as intermediate and deep seismic activity, known for its scarcity of aftershocks. We examine as well declustered (aftershocks removed) versions of seismic catalogues. Our preliminary results show some degree of multifractality for the undeclustered, shallow seismicity. On the other hand, at large scales, we detect a monofractal scaling behaviour, clearly put in evidence for the declustered, shallow seismic activity. Moreover, some of the declustered sequences show a long-range dependent (LRD) behaviour, characterised by a Hurst exponent, H > 0.5, in contrast with the memory-less, Poissonian model. We demonstrate that the LRD is a genuine characteristic and is not an effect of the time series probability distribution function. One of the most attractive features of wavelet analysis is its ability to determine a local Hurst exponent. We show that this feature together with the possibility of extending the analysis to spatial patterns may constitute a valuable approach to search for anomalous (precursory?) patterns of seismic activity.

Fast Fourier and discrete wavelet transforms applied to sensorless vector control induction motor for rotor bar faults diagnosis.

PubMed

Talhaoui, Hicham; Menacer, Arezki; Kessal, Abdelhalim; Kechida, Ridha

2014-09-01

This paper presents new techniques to evaluate faults in case of broken rotor bars of induction motors. Procedures are applied with closed-loop control. Electrical and mechanical variables are treated using fast Fourier transform (FFT), and discrete wavelet transform (DWT) at start-up and steady state. The wavelet transform has proven to be an excellent mathematical tool for the detection of the faults particularly broken rotor bars type. As a performance, DWT can provide a local representation of the non-stationary current signals for the healthy machine and with fault. For sensorless control, a Luenberger observer is applied; the estimation rotor speed is analyzed; the effect of the faults in the speed pulsation is compensated; a quadratic current appears and used for fault detection. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Phase-recovery improvement using analytic wavelet transform analysis of a noisy interferogram cepstrum.

PubMed

Etchepareborda, Pablo; Vadnjal, Ana Laura; Federico, Alejandro; Kaufmann, Guillermo H

2012-09-15

We evaluate the extension of the exact nonlinear reconstruction technique developed for digital holography to the phase-recovery problems presented by other optical interferometric methods, which use carrier modulation. It is shown that the introduction of an analytic wavelet analysis in the ridge of the cepstrum transformation corresponding to the analyzed interferogram can be closely related to the well-known wavelet analysis of the interferometric intensity. Subsequently, the phase-recovery process is improved. The advantages and limitations of this framework are analyzed and discussed using numerical simulations in singular scalar light fields and in temporal speckle pattern interferometry.

Study on stress-strain response of multi-phase TRIP steel under cyclic loading

NASA Astrophysics Data System (ADS)

Dan, W. J.; Hu, Z. G.; Zhang, W. G.; Li, S. H.; Lin, Z. Q.

2013-12-01

The stress-strain response of multi-phase TRIP590 sheet steel is studied in cyclic loading condition at room temperature based on a cyclic phase transformation model and a multi-phase mixed kinematic hardening model. The cyclic martensite transformation model is proposed based on the shear-band intersection, where the repeat number, strain amplitude and cyclic frequency are used to control the phase transformation process. The multi-phase mixed kinematic hardening model is developed based on the non-linear kinematic hardening rule of per-phase. The parameters of transformation model are identified with the relationship between the austenite volume fraction and the repeat number. The parameters in Kinematic hardening model are confirmed by the experimental hysteresis loops in different strain amplitude conditions. The responses of hysteresis loop and stress amplitude are evaluated by tension-compression data.

An efficient method for the fusion of light field refocused images

NASA Astrophysics Data System (ADS)

Wang, Yingqian; Yang, Jungang; Xiao, Chao; An, Wei

2018-04-01

Light field cameras have drawn much attention due to the advantage of post-capture adjustments such as refocusing after exposure. The depth of field in refocused images is always shallow because of the large equivalent aperture. As a result, a large number of multi-focus images are obtained and an all-in-focus image is demanded. Consider that most multi-focus image fusion algorithms do not particularly aim at large numbers of source images and traditional DWT-based fusion approach has serious problems in dealing with lots of multi-focus images, causing color distortion and ringing effect. To solve this problem, this paper proposes an efficient multi-focus image fusion method based on stationary wavelet transform (SWT), which can deal with a large quantity of multi-focus images with shallow depth of fields. We compare SWT-based approach with DWT-based approach on various occasions. And the results demonstrate that the proposed method performs much better both visually and quantitatively.

A unified framework for physical print quality

NASA Astrophysics Data System (ADS)

Eid, Ahmed; Cooper, Brian; Rippetoe, Ed

2007-01-01

In this paper we present a unified framework for physical print quality. This framework includes a design for a testbed, testing methodologies and quality measures of physical print characteristics. An automatic belt-fed flatbed scanning system is calibrated to acquire L* data for a wide range of flat field imagery. Testing methodologies based on wavelet pre-processing and spectral/statistical analysis are designed. We apply the proposed framework to three common printing artifacts: banding, jitter, and streaking. Since these artifacts are directional, wavelet based approaches are used to extract one artifact at a time and filter out other artifacts. Banding is characterized as a medium-to-low frequency, vertical periodic variation down the page. The same definition is applied to the jitter artifact, except that the jitter signal is characterized as a high-frequency signal above the banding frequency range. However, streaking is characterized as a horizontal aperiodic variation in the high-to-medium frequency range. Wavelets at different levels are applied to the input images in different directions to extract each artifact within specified frequency bands. Following wavelet reconstruction, images are converted into 1-D signals describing the artifact under concern. Accurate spectral analysis using a DFT with Blackman-Harris windowing technique is used to extract the power (strength) of periodic signals (banding and jitter). Since streaking is an aperiodic signal, a statistical measure is used to quantify the streaking strength. Experiments on 100 print samples scanned at 600 dpi from 10 different printers show high correlation (75% to 88%) between the ranking of these samples by the proposed metrologies and experts' visual ranking.

Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network.

PubMed

Ceylan, Murat; Ceylan, Rahime; Ozbay, Yüksel; Kara, Sadik

2008-09-01

In biomedical signal classification, due to the huge amount of data, to compress the biomedical waveform data is vital. This paper presents two different structures formed using feature extraction algorithms to decrease size of feature set in training and test data. The proposed structures, named as wavelet transform-complex-valued artificial neural network (WT-CVANN) and complex wavelet transform-complex-valued artificial neural network (CWT-CVANN), use real and complex discrete wavelet transform for feature extraction. The aim of using wavelet transform is to compress data and to reduce training time of network without decreasing accuracy rate. In this study, the presented structures were applied to the problem of classification in carotid arterial Doppler ultrasound signals. Carotid arterial Doppler ultrasound signals were acquired from left carotid arteries of 38 patients and 40 healthy volunteers. The patient group included 22 males and 16 females with an established diagnosis of the early phase of atherosclerosis through coronary or aortofemoropopliteal (lower extremity) angiographies (mean age, 59 years; range, 48-72 years). Healthy volunteers were young non-smokers who seem to not bear any risk of atherosclerosis, including 28 males and 12 females (mean age, 23 years; range, 19-27 years). Sensitivity, specificity and average detection rate were calculated for comparison, after training and test phases of all structures finished. These parameters have demonstrated that training times of CVANN and real-valued artificial neural network (RVANN) were reduced using feature extraction algorithms without decreasing accuracy rate in accordance to our aim.

Wavelet Analyses and Applications

ERIC Educational Resources Information Center

Bordeianu, Cristian C.; Landau, Rubin H.; Paez, Manuel J.

2009-01-01

It is shown how a modern extension of Fourier analysis known as wavelet analysis is applied to signals containing multiscale information. First, a continuous wavelet transform is used to analyse the spectrum of a nonstationary signal (one whose form changes in time). The spectral analysis of such a signal gives the strength of the signal in each…

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.

A modified multiscale peak alignment method combined with trilinear decomposition to study the volatile/heat-labile components in Ligusticum chuanxiong Hort - Cyperus rotundus rhizomes by HS-SPME-GC/MS.

PubMed

He, Min; Yan, Pan; Yang, Zhi-Yu; Zhang, Zhi-Min; Yang, Tian-Biao; Hong, Liang

2018-03-15

Head Space/Solid Phase Micro-Extraction (HS-SPME) coupled with Gas Chromatography/Mass Spectrometer (GC/MS) was used to determine the volatile/heat-labile components in Ligusticum chuanxiong Hort - Cyperus rotundus rhizomes. Facing co-eluting peaks in k samples, a trilinear structure was reconstructed to obtain the second-order advantage. The retention time (RT) shift with multi-channel detection signals for different samples has been vital in maintaining the trilinear structure, thus a modified multiscale peak alignment (mMSPA) method was proposed in this paper. The peak position and peak width of representative ion profile were firstly detected by mMSPA using Continuous Wavelet Transform with Haar wavelet as the mother wavelet (Haar CWT). Then, the raw shift was confirmed by Fast Fourier Transform (FFT) cross correlation calculation. To obtain the optimal shift, Haar CWT was again used to detect the subtle deviations and be amalgamated in calculation. Here, to ensure there is no peaks shape alternation, the alignment was performed in local domains of data matrices, and all data points in the peak zone were moved via linear interpolation in non-peak parts. Finally, chemical components of interest in Ligusticum chuanxiong Hort - Cyperus rotundus rhizomes were analyzed by HS-SPME-GCMS and mMSPA-alternating trilinear decomposition (ATLD) resolution. As a result, the concentration variation between herbs and their pharmaceutical products can provide a scientific basic for the quality standard establishment of traditional Chinese medicines. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

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. 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. 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.

Improvement of electrocardiogram by empirical wavelet transform

NASA Astrophysics Data System (ADS)

Chanchang, Vikanda; Kumchaiseemak, Nakorn; Sutthiopad, Malee; Luengviriya, Chaiya

2017-09-01

Electrocardiogram (ECG) is a crucial tool in the detection of cardiac arrhythmia. It is also often used in a routine physical exam, especially, for elderly people. This graphical representation of electrical activity of heart is obtained by a measurement of voltage at the skin; therefore, the signal is always contaminated by noise from various sources. For a proper interpretation, the quality of the ECG should be improved by a noise reduction. In this article, we present a study of a noise filtration in the ECG by using an empirical wavelet transform (EWT). Unlike the traditional wavelet method, EWT is adaptive since the frequency spectrum of the ECG is taken into account in the construction of the wavelet basis. We show that the signal-to-noise ratio increases after the noise filtration for different noise artefacts.

The dynamic system corresponding to LOD and AAM.

NASA Astrophysics Data System (ADS)

Liu, Shida; Liu, Shikuo; Chen, Jiong

2000-02-01

Using wavelet transform, the authors can reconstruct the 1-D map of a multifractal object. The wavelet transform of LOD and AAM shows that at 20 years scale, annual scale and 2 - 3 years scale, the jump points of LOD and AAM accord with each other very well, and their reconstructing 1-D mapping dynamic system are also very similar.

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope

PubMed Central

2017-01-01

Rapid automatic detection of the fiducial points—namely, the P wave, QRS complex, and T wave—is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs. PMID:29065613

R Peak Detection Method Using Wavelet Transform and Modified Shannon Energy Envelope.

PubMed

Park, Jeong-Seon; Lee, Sang-Woong; Park, Unsang

2017-01-01

Rapid automatic detection of the fiducial points-namely, the P wave, QRS complex, and T wave-is necessary for early detection of cardiovascular diseases (CVDs). In this paper, we present an R peak detection method using the wavelet transform (WT) and a modified Shannon energy envelope (SEE) for rapid ECG analysis. The proposed WTSEE algorithm performs a wavelet transform to reduce the size and noise of ECG signals and creates SEE after first-order differentiation and amplitude normalization. Subsequently, the peak energy envelope (PEE) is extracted from the SEE. Then, R peaks are estimated from the PEE, and the estimated peaks are adjusted from the input ECG. Finally, the algorithm generates the final R features by validating R-R intervals and updating the extracted R peaks. The proposed R peak detection method was validated using 48 first-channel ECG records of the MIT-BIH arrhythmia database with a sensitivity of 99.93%, positive predictability of 99.91%, detection error rate of 0.16%, and accuracy of 99.84%. Considering the high detection accuracy and fast processing speed due to the wavelet transform applied before calculating SEE, the proposed method is highly effective for real-time applications in early detection of CVDs.

Comparison between wavelet transform and moving average as filter method of MODIS imagery to recognize paddy cropping pattern in West Java

NASA Astrophysics Data System (ADS)

Dwi Nugroho, Kreshna; Pebrianto, Singgih; Arif Fatoni, Muhammad; Fatikhunnada, Alvin; Liyantono; Setiawan, Yudi

2017-01-01

Information on the area and spatial distribution of paddy field are needed to support sustainable agricultural and food security program. Mapping or distribution of cropping pattern paddy field is important to obtain sustainability paddy field area. It can be done by direct observation and remote sensing method. This paper discusses remote sensing for paddy field monitoring based on MODIS time series data. In time series MODIS data, difficult to direct classified of data, because of temporal noise. Therefore wavelet transform and moving average are needed as filter methods. The Objective of this study is to recognize paddy cropping pattern with wavelet transform and moving average in West Java using MODIS imagery (MOD13Q1) from 2001 to 2015 then compared between both of methods. The result showed the spatial distribution almost have the same cropping pattern. The accuracy of wavelet transform (75.5%) is higher than moving average (70.5%). Both methods showed that the majority of the cropping pattern in West Java have pattern paddy-fallow-paddy-fallow with various time planting. The difference of the planting schedule was occurs caused by the availability of irrigation water.

Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment.

PubMed

Terraneo, M; Georgeot, B; Shepelyansky, D L

2005-06-01

We study the efficiency of quantum algorithms which aim at obtaining phase-space distribution functions of quantum systems. Wigner and Husimi functions are considered. Different quantum algorithms are envisioned to build these functions, and compared with the classical computation. Different procedures to extract more efficiently information from the final wave function of these algorithms are studied, including coarse-grained measurements, amplitude amplification, and measure of wavelet-transformed wave function. The algorithms are analyzed and numerically tested on a complex quantum system showing different behavior depending on parameters: namely, the kicked rotator. The results for the Wigner function show in particular that the use of the quantum wavelet transform gives a polynomial gain over classical computation. For the Husimi distribution, the gain is much larger than for the Wigner function and is larger with the help of amplitude amplification and wavelet transforms. We discuss the generalization of these results to the simulation of other quantum systems. We also apply the same set of techniques to the analysis of real images. The results show that the use of the quantum wavelet transform allows one to lower dramatically the number of measurements needed, but at the cost of a large loss of information.

Detection of combustion start in the controlled auto ignition engine by wavelet transform of the engine block vibration signal

NASA Astrophysics Data System (ADS)

Kim, Seonguk; Min, Kyoungdoug

2008-08-01

The CAI (controlled auto ignition) engine ignites fuel and air mixture by trapping high temperature burnt gas using a negative valve overlap. Due to auto ignition in CAI combustion, efficiency improvements and low level NOx emission can be obtained. Meanwhile, the CAI combustion regime is restricted and control parameters are limited. The start of combustion data in the compressed ignition engine are most critical for controlling the overall combustion. In this research, the engine block vibration signal is transformed by the Meyer wavelet to analyze CAI combustion more easily and accurately. Signal acquisition of the engine block vibration is a more suitable method for practical use than measurement of in-cylinder pressure. A new method for detecting combustion start in CAI engines through wavelet transformation of the engine block vibration signal was developed and results indicate that it is accurate enough to analyze the start of combustion. Experimental results show that wavelet transformation of engine block vibration can track the start of combustion in each cycle. From this newly developed method, the start of combustion data in CAI engines can be detected more easily and used as input data for controlling CAI combustion.

3-D optical profilometry at micron scale with multi-frequency fringe projection using modified fibre optic Lloyd's mirror technique

NASA Astrophysics Data System (ADS)

Inanç, Arda; Kösoğlu, Gülşen; Yüksel, Heba; Naci Inci, Mehmet

2018-06-01

A new fibre optic Lloyd's mirror method is developed for extracting 3-D height distribution of various objects at the micron scale with a resolution of 4 μm. The fibre optic assembly is elegantly integrated to an optical microscope and a CCD camera. It is demonstrated that the proposed technique is quite suitable and practical to produce an interference pattern with an adjustable frequency. By increasing the distance between the fibre and the mirror with a micrometre stage in the Lloyd's mirror assembly, the separation between the two bright fringes is lowered down to the micron scale without using any additional elements as part of the optical projection unit. A fibre optic cable, whose polymer jacket is partially stripped, and a microfluidic channel are used as test objects to extract their surface topographies. Point by point sensitivity of the method is found to be around 8 μm, changing a couple of microns depending on the fringe frequency and the measured height. A straightforward calibration procedure for the phase to height conversion is also introduced by making use of the vertical moving stage of the optical microscope. The phase analysis of the acquired image is carried out by One Dimensional Continuous Wavelet Transform for which the chosen wavelet is the Morlet wavelet and the carrier removal of the projected fringe patterns is achieved by reference subtraction. Furthermore, flexible multi-frequency property of the proposed method allows measuring discontinuous heights where there are phase ambiguities like 2π by lowering the fringe frequency and eliminating the phase ambiguity.

Automatic detection of anomalies in screening mammograms

PubMed Central

2013-01-01

Background Diagnostic performance in breast screening programs may be influenced by the prior probability of disease. Since breast cancer incidence is roughly half a percent in the general population there is a large probability that the screening exam will be normal. That factor may contribute to false negatives. Screening programs typically exhibit about 83% sensitivity and 91% specificity. This investigation was undertaken to determine if a system could be developed to pre-sort screening-images into normal and suspicious bins based on their likelihood to contain disease. Wavelets were investigated as a method to parse the image data, potentially removing confounding information. The development of a classification system based on features extracted from wavelet transformed mammograms is reported. Methods In the multi-step procedure images were processed using 2D discrete wavelet transforms to create a set of maps at different size scales. Next, statistical features were computed from each map, and a subset of these features was the input for a concerted-effort set of naïve Bayesian classifiers. The classifier network was constructed to calculate the probability that the parent mammography image contained an abnormality. The abnormalities were not identified, nor were they regionalized. The algorithm was tested on two publicly available databases: the Digital Database for Screening Mammography (DDSM) and the Mammographic Images Analysis Society’s database (MIAS). These databases contain radiologist-verified images and feature common abnormalities including: spiculations, masses, geometric deformations and fibroid tissues. Results The classifier-network designs tested achieved sensitivities and specificities sufficient to be potentially useful in a clinical setting. This first series of tests identified networks with 100% sensitivity and up to 79% specificity for abnormalities. This performance significantly exceeds the mean sensitivity reported in literature for the unaided human expert. Conclusions Classifiers based on wavelet-derived features proved to be highly sensitive to a range of pathologies, as a result Type II errors were nearly eliminated. Pre-sorting the images changed the prior probability in the sorted database from 37% to 74%. PMID:24330643

Measurement of in-plane displacements using the phase singularities generated by directional wavelet transforms of speckle pattern images.

PubMed

Vadnjal, Ana Laura; Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H

2013-03-20

We present a method to determine micro and nano in-plane displacements based on the phase singularities generated by application of directional wavelet transforms to speckle pattern images. The spatial distribution of the obtained phase singularities by the wavelet transform configures a network, which is characterized by two quasi-orthogonal directions. The displacement value is determined by identifying the intersection points of the network before and after the displacement produced by the tested object. The performance of this method is evaluated using simulated speckle patterns and experimental data. The proposed approach is compared with the optical vortex metrology and digital image correlation methods in terms of performance and noise robustness, and the advantages and limitations associated to each method are also discussed.

[A method to estimate the short-term fractal dimension of heart rate variability based on wavelet transform].

PubMed

Zhonggang, Liang; Hong, Yan

2006-10-01

A new method of calculating fractal dimension of short-term heart rate variability signals is presented. The method is based on wavelet transform and filter banks. The implementation of the method is: First of all we pick-up the fractal component from HRV signals using wavelet transform. Next, we estimate the power spectrum distribution of fractal component using auto-regressive model, and we estimate parameter 7 using the least square method. Finally according to formula D = 2- (gamma-1)/2 estimate fractal dimension of HRV signal. To validate the stability and reliability of the proposed method, using fractional brown movement simulate 24 fractal signals that fractal value is 1.6 to validate, the result shows that the method has stability and reliability.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

Fault feature extraction of planet gear in wind turbine gearbox based on spectral kurtosis and time wavelet energy spectrum

NASA Astrophysics Data System (ADS)

Kong, Yun; Wang, Tianyang; Li, Zheng; Chu, Fulei

2017-09-01

Planetary transmission plays a vital role in wind turbine drivetrains, and its fault diagnosis has been an important and challenging issue. Owing to the complicated and coupled vibration source, time-variant vibration transfer path, and heavy background noise masking effect, the vibration signal of planet gear in wind turbine gearboxes exhibits several unique characteristics: Complex frequency components, low signal-to-noise ratio, and weak fault feature. In this sense, the periodic impulsive components induced by a localized defect are hard to extract, and the fault detection of planet gear in wind turbines remains to be a challenging research work. Aiming to extract the fault feature of planet gear effectively, we propose a novel feature extraction method based on spectral kurtosis and time wavelet energy spectrum (SK-TWES) in the paper. Firstly, the spectral kurtosis (SK) and kurtogram of raw vibration signals are computed and exploited to select the optimal filtering parameter for the subsequent band-pass filtering. Then, the band-pass filtering is applied to extrude periodic transient impulses using the optimal frequency band in which the corresponding SK value is maximal. Finally, the time wavelet energy spectrum analysis is performed on the filtered signal, selecting Morlet wavelet as the mother wavelet which possesses a high similarity to the impulsive components. The experimental signals collected from the wind turbine gearbox test rig demonstrate that the proposed method is effective at the feature extraction and fault diagnosis for the planet gear with a localized defect.

Wavelets and distributed approximating functionals

NASA Astrophysics Data System (ADS)

Wei, G. W.; Kouri, D. J.; Hoffman, D. K.

1998-07-01

A general procedure is proposed for constructing father and mother wavelets that have excellent time-frequency localization and can be used to generate entire wavelet families for use as wavelet transforms. One interesting feature of our father wavelets (scaling functions) is that they belong to a class of generalized delta sequences, which we refer to as distributed approximating functionals (DAFs). We indicate this by the notation wavelet-DAFs. Correspondingly, the mother wavelets generated from these wavelet-DAFs are appropriately called DAF-wavelets. Wavelet-DAFs can be regarded as providing a pointwise (localized) spectral method, which furnishes a bridge between the traditional global methods and local methods for solving partial differential equations. They are shown to provide extremely accurate numerical solutions for a number of nonlinear partial differential equations, including the Korteweg-de Vries (KdV) equation, for which a previous method has encountered difficulties (J. Comput. Phys. 132 (1997) 233).

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

Application of wavelet multi-resolution analysis for correction of seismic acceleration records

NASA Astrophysics Data System (ADS)

Ansari, Anooshiravan; Noorzad, Assadollah; Zare, Mehdi

2007-12-01

During an earthquake, many stations record the ground motion, but only a few of them could be corrected using conventional high-pass and low-pass filtering methods and the others were identified as highly contaminated by noise and as a result useless. There are two major problems associated with these noisy records. First, since the signal to noise ratio (S/N) is low, it is not possible to discriminate between the original signal and noise either in the frequency domain or in the time domain. Consequently, it is not possible to cancel out noise using conventional filtering methods. The second problem is the non-stationary characteristics of the noise. In other words, in many cases the characteristics of the noise are varied over time and in these situations, it is not possible to apply frequency domain correction schemes. When correcting acceleration signals contaminated with high-level non-stationary noise, there is an important question whether it is possible to estimate the state of the noise in different bands of time and frequency. Wavelet multi-resolution analysis decomposes a signal into different time-frequency components, and besides introducing a suitable criterion for identification of the noise among each component, also provides the required mathematical tool for correction of highly noisy acceleration records. In this paper, the characteristics of the wavelet de-noising procedures are examined through the correction of selected real and synthetic acceleration time histories. It is concluded that this method provides a very flexible and efficient tool for the correction of very noisy and non-stationary records of ground acceleration. In addition, a two-step correction scheme is proposed for long period correction of the acceleration records. This method has the advantage of stable results in displacement time history and response spectrum.

ICER-3D Hyperspectral Image Compression Software

NASA Technical Reports Server (NTRS)

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

2010-01-01

Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.

Wavelets in Physics

NASA Astrophysics Data System (ADS)

van den Berg, J. C.

2004-03-01

A guided tour J. C. van den Berg; 1. Wavelet analysis, a new tool in physics J.-P. Antoine; 2. The 2-D wavelet transform, physical applications J.-P. Antoine; 3. Wavelets and astrophysical applications A. Bijaoui; 4. Turbulence analysis, modelling and computing using wavelets M. Farge, N. K.-R. Kevlahan, V. Perrier and K. Schneider; 5. Wavelets and detection of coherent structures in fluid turbulence L. Hudgins and J. H. Kaspersen; 6. Wavelets, non-linearity and turbulence in fusion plasmas B. Ph. van Milligen; 7. Transfers and fluxes of wind kinetic energy between orthogonal wavelet components during atmospheric blocking A. Fournier; 8. Wavelets in atomic physics and in solid state physics J.-P. Antoine, Ph. Antoine and B. Piraux; 9. The thermodynamics of fractals revisited with wavelets A. Arneodo, E. Bacry and J. F. Muzy; 10. Wavelets in medicine and physiology P. Ch. Ivanov, A. L. Goldberger, S. Havlin, C.-K. Peng, M. G. Rosenblum and H. E. Stanley; 11. Wavelet dimension and time evolution Ch.-A. Guérin and M. Holschneider.

Wavelets in Physics

NASA Astrophysics Data System (ADS)

van den Berg, J. C.

1999-08-01

A guided tour J. C. van den Berg; 1. Wavelet analysis, a new tool in physics J.-P. Antoine; 2. The 2-D wavelet transform, physical applications J.-P. Antoine; 3. Wavelets and astrophysical applications A. Bijaoui; 4. Turbulence analysis, modelling and computing using wavelets M. Farge, N. K.-R. Kevlahan, V. Perrier and K. Schneider; 5. Wavelets and detection of coherent structures in fluid turbulence L. Hudgins and J. H. Kaspersen; 6. Wavelets, non-linearity and turbulence in fusion plasmas B. Ph. van Milligen; 7. Transfers and fluxes of wind kinetic energy between orthogonal wavelet components during atmospheric blocking A. Fournier; 8. Wavelets in atomic physics and in solid state physics J.-P. Antoine, Ph. Antoine and B. Piraux; 9. The thermodynamics of fractals revisited with wavelets A. Arneodo, E. Bacry and J. F. Muzy; 10. Wavelets in medicine and physiology P. Ch. Ivanov, A. L. Goldberger, S. Havlin, C.-K. Peng, M. G. Rosenblum and H. E. Stanley; 11. Wavelet dimension and time evolution Ch.-A. Guérin and M. Holschneider.

Feature extraction for ultrasonic sensor based defect detection in ceramic components

NASA Astrophysics Data System (ADS)

Kesharaju, Manasa; Nagarajah, Romesh

2014-02-01

High density silicon carbide materials are commonly used as the ceramic element of hard armour inserts used in traditional body armour systems to reduce their weight, while providing improved hardness, strength and elastic response to stress. Currently, armour ceramic tiles are inspected visually offline using an X-ray technique that is time consuming and very expensive. In addition, from X-rays multiple defects are also misinterpreted as single defects. Therefore, to address these problems the ultrasonic non-destructive approach is being investigated. Ultrasound based inspection would be far more cost effective and reliable as the methodology is applicable for on-line quality control including implementation of accept/reject criteria. This paper describes a recently developed methodology to detect, locate and classify various manufacturing defects in ceramic tiles using sub band coding of ultrasonic test signals. The wavelet transform is applied to the ultrasonic signal and wavelet coefficients in the different frequency bands are extracted and used as input features to an artificial neural network (ANN) for purposes of signal classification. Two different classifiers, using artificial neural networks (supervised) and clustering (un-supervised) are supplied with features selected using Principal Component Analysis(PCA) and their classification performance compared. This investigation establishes experimentally that Principal Component Analysis(PCA) can be effectively used as a feature selection method that provides superior results for classifying various defects in the context of ultrasonic inspection in comparison with the X-ray technique.

Blind separation of overlapping partials in harmonic musical notes using amplitude and phase reconstruction

NASA Astrophysics Data System (ADS)

de León, Jesús Ponce; Beltrán, José Ramón

2012-12-01

In this study, a new method of blind audio source separation (BASS) of monaural musical harmonic notes is presented. The input (mixed notes) signal is processed using a flexible analysis and synthesis algorithm (complex wavelet additive synthesis, CWAS), which is based on the complex continuous wavelet transform. When the harmonics from two or more sources overlap in a certain frequency band (or group of bands), a new technique based on amplitude similarity criteria is used to obtain an approximation to the original partial information. The aim is to show that the CWAS algorithm can be a powerful tool in BASS. Compared with other existing techniques, the main advantages of the proposed algorithm are its accuracy in the instantaneous phase estimation, its synthesis capability and that the only input information needed is the mixed signal itself. A set of synthetically mixed monaural isolated notes have been analyzed using this method, in eight different experiments: the same instrument playing two notes within the same octave and two harmonically related notes (5th and 12th intervals), two different musical instruments playing 5th and 12th intervals, two different instruments playing non-harmonic notes, major and minor chords played by the same musical instrument, three different instruments playing non-harmonically related notes and finally the mixture of a inharmonic instrument (piano) and one harmonic instrument. The results obtained show the strength of the technique.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simultaneous spectrophotometric determination of four metals by two kinds of partial least squares methods

NASA Astrophysics Data System (ADS)

Gao, Ling; Ren, Shouxin

2005-10-01

Simultaneous determination of Ni(II), Cd(II), Cu(II) and Zn(II) was studied by two methods, kernel partial least squares (KPLS) and wavelet packet transform partial least squares (WPTPLS), with xylenol orange and cetyltrimethyl ammonium bromide as reagents in the medium pH = 9.22 borax-hydrochloric acid buffer solution. Two programs, PKPLS and PWPTPLS, were designed to perform the calculations. Data reduction was performed using kernel matrices and wavelet packet transform, respectively. In the KPLS method, the size of the kernel matrix is only dependent on the number of samples, thus the method was suitable for the data matrix with many wavelengths and fewer samples. Wavelet packet representations of signals provide a local time-frequency description, thus in the wavelet packet domain, the quality of the noise removal can be improved. In the WPTPLS by optimization, wavelet function and decomposition level were selected as Daubeches 12 and 5, respectively. Experimental results showed both methods to be successful even where there was severe overlap of spectra.

Doppler radar fall activity detection using the wavelet transform.

PubMed

Su, Bo Yu; Ho, K C; Rantz, Marilyn J; Skubic, Marjorie

2015-03-01

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

Wavelets and Multifractal Analysis

DTIC Science & Technology

2004-07-01

distribution unlimited 13. SUPPLEMENTARY NOTES See also ADM001750, Wavelets and Multifractal Analysis (WAMA) Workshop held on 19-31 July 2004., The original...f)] . . . 16 2.5.4 Detrended Fluctuation Analysis [DFA(m)] . . . . . . . . . . . . . . . 17 2.6 Scale-Independent Measures...18 2.6.1 Detrended -Fluctuation- Analysis Power-Law Exponent (αD) . . . . . . 18 2.6.2 Wavelet-Transform Power-Law Exponent

Simultaneous determination of chloramphenicol, dexamethasone and naphazoline in ternary and quaternary mixtures by RP-HPLC, derivative and wavelet transforms of UV ratio spectra

NASA Astrophysics Data System (ADS)

Hoang, Vu Dang; Hue, Nguyen Thu; Tho, Nguyen Huu; Nguyen, Hue Minh Thi

2015-03-01

The application of chemometrics-assisted UV spectrophotometry and RP-HPLC to the simultaneous determination of chloramphenicol, dexamethasone and naphazoline in ternary and quaternary mixtures is presented. The spectrophotometric procedure is based on the first-order derivative and wavelet transforms of ratio spectra using single, double and successive divisors. The ratio spectra were differentiated and smoothed using Savitzky-Golay filter; whereas wavelet transform realized with wavelet functions (i.e. db6, gaus5 and coif3) to obtain highest spectral recoveries. For the RP-HPLC procedure, the separation was achieved on a ZORBAX SB-C18 (150 × 4.6 mm; 5 μm) column at ambient temperature and the total run time was less than 7 min. A mixture of acetonitrile - 25 mM phosphate buffer pH 3 (27:73, v/v) was used as the mobile phase at a flow rate of 1.0 mL/min and the effluent monitored by measuring absorbance at 220 nm. Calibration graphs were established in the range 20-70 mg/L for chloramphenicol, 6-14 mg/L for dexamethasone and 3-8 mg/L for naphazoline (R2 > 0.990). The RP-HPLC and ratio spectra transformed by a combination of derivative-wavelet algorithms proved to be able to successfully determine all analytes in commercial eye drop formulations without sample matrix interference (mean percent recoveries, 97.4-104.3%).

[FREQUENCY-TEMPORAL STRUCTURE OF HUMAN ELECTROENCEPHALOGRAM IN THE CONDITION OF ARTIFICIAL HYPOGRAVITY: DRY IMMERSION MODEL].

PubMed

Kuznetsova, G D; Gabova, A V; Lazarev, I E; Obukhov, Iu V; Obukhov, K Iu; Morozov, A A; Kulikov, M A; Shchatskova, A B; Vasil'eva, O N; Tomilovskaia, E S

2015-01-01

Frequency-temporal electroencephalogram (EEG) reactions to hypogravity were studied in 7 male subjects at the age of 20 to 27 years. The experiment was conducted using dry immersion (DI) as the best known method of simulating the space microgravity effects on the Earth. This hypogravity model reproduces hypokinesia, i.e. the weight-bearing and mechanic load removal, which is typical of microgravity. EEG was recorded by Neuroscan-2 (Compumedics) before the experiment (baseline data) and at the end of day 2 in DI. Comparative analysis of the EEG frequency-temporal structure was performed with the use of 2 techniques: Fourier transform and modified wavelet analysis. The Fourier transform elicited that after 2 days in DI the main shifts occurring to the EEG spectral composition are a decline in the alpha power and a slight though reliable growth of theta power. Similar frequency shifts were detected in the same records analyzed using the wavelet transform. According to wavelet analysis, during DI shifts in EEG frequency spectrum are accompanied by frequency desorganization of the EEG dominant rhythm and gross impairment of total stability of the electrical activity with time. Wavelet transform provides an opportunity to quantify changes in the frequency-temporal structure of the electrical activity of the brain. Quantitative evidence of frequency desorganization and temporal instability of EEG wavelet spectrograms may be the key to the understanding of mechanisms that drive functional disorders in the brain cortex in the conditions of hypogravity.

Basic Investigation on Medical Ultrasonic Echo Image Compression by JPEG2000 - Availability of Wavelet Transform and ROI Method

DTIC Science & Technology

2001-10-25

Table III. In spite of the same quality in ROI, it is decided that the images in the cases where QF is 1.3, 1.5 or 2.0 are not good for diagnosis. Of...but (b) is not good for diagnosis by decision of ultrasonographer. Results reveal that wavelet transform achieves higher quality of image compared

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.

PubMed

Madero Orozco, Hiram; Vergara Villegas, Osslan Osiris; Cruz Sánchez, Vianey Guadalupe; Ochoa Domínguez, Humberto de Jesús; Nandayapa Alfaro, Manuel de Jesús

2015-02-12

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool. The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules. The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%. The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods.

PubMed

Oczeretko, Edward; Swiatecka, Jolanta; Kitlas, Agnieszka; Laudanski, Tadeusz; Pierzynski, Piotr

2006-01-01

In physiological research, we often study multivariate data sets, containing two or more simultaneously recorded time series. The aim of this paper is to present the cross-correlation and the wavelet cross-correlation methods to assess synchronization between contractions in different topographic regions of the uterus. From a medical point of view, it is important to identify time delays between contractions, which may be of potential diagnostic significance in various pathologies. The cross-correlation was computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running cross-correlation function was obtained. The propagation% parameter assessed from this function allows quantitative description of synchronization in bivariate time series. In general, the uterine contraction signals are very complicated. Wavelet transforms provide insight into the structure of the time series at various frequencies (scales). To show the changes of the propagation% parameter along scales, a wavelet running cross-correlation was used. At first, the continuous wavelet transforms as the uterine contraction signals were received and afterwards, a running cross-correlation analysis was conducted for each pair of transformed time series. The findings show that running functions are very useful in the analysis of uterine contractions.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Espinosa-Paredes, Gilberto; Prieto-Guerrero, Alfonso; Nunez-Carrera, Alejandro

This paper introduces a wavelet-based method to analyze instability events in a boiling water reactor (BWR) during transient phenomena. The methodology to analyze BWR signals includes the following: (a) the short-time Fourier transform (STFT) analysis, (b) decomposition using the continuous wavelet transform (CWT), and (c) application of multiresolution analysis (MRA) using discrete wavelet transform (DWT). STFT analysis permits the study, in time, of the spectral content of analyzed signals. The CWT provides information about ruptures, discontinuities, and fractal behavior. To detect these important features in the signal, a mother wavelet has to be chosen and applied at several scales tomore » obtain optimum results. MRA allows fast implementation of the DWT. Features like important frequencies, discontinuities, and transients can be detected with analysis at different levels of detail coefficients. The STFT was used to provide a comparison between a classic method and the wavelet-based method. The damping ratio, which is an important stability parameter, was calculated as a function of time. The transient behavior can be detected by analyzing the maximum contained in detail coefficients at different levels in the signal decomposition. This method allows analysis of both stationary signals and highly nonstationary signals in the timescale plane. This methodology has been tested with the benchmark power instability event of Laguna Verde nuclear power plant (NPP) Unit 1, which is a BWR-5 NPP.« less

Wavelet library for constrained devices

NASA Astrophysics Data System (ADS)

Ehlers, Johan Hendrik; Jassim, Sabah A.

2007-04-01

The wavelet transform is a powerful tool for image and video processing, useful in a range of applications. This paper is concerned with the efficiency of a certain fast-wavelet-transform (FWT) implementation and several wavelet filters, more suitable for constrained devices. Such constraints are typically found on mobile (cell) phones or personal digital assistants (PDA). These constraints can be a combination of; limited memory, slow floating point operations (compared to integer operations, most often as a result of no hardware support) and limited local storage. Yet these devices are burdened with demanding tasks such as processing a live video or audio signal through on-board capturing sensors. In this paper we present a new wavelet software library, HeatWave, that can be used efficiently for image/video processing/analysis tasks on mobile phones and PDA's. We will demonstrate that HeatWave is suitable for realtime applications with fine control and range to suit transform demands. We shall present experimental results to substantiate these claims. Finally this library is intended to be of real use and applied, hence we considered several well known and common embedded operating system platform differences; such as a lack of common routines or functions, stack limitations, etc. This makes HeatWave suitable for a range of applications and research projects.

A new multiscale noise tuning stochastic resonance for enhanced fault diagnosis in wind turbine drivetrains

NASA Astrophysics Data System (ADS)

Hu, Bingbing; Li, Bing

2016-02-01

It is very difficult to detect weak fault signatures due to the large amount of noise in a wind turbine system. Multiscale noise tuning stochastic resonance (MSTSR) has proved to be an effective way to extract weak signals buried in strong noise. However, the MSTSR method originally based on discrete wavelet transform (DWT) has disadvantages such as shift variance and the aliasing effects in engineering application. In this paper, the dual-tree complex wavelet transform (DTCWT) is introduced into the MSTSR method, which makes it possible to further improve the system output signal-to-noise ratio and the accuracy of fault diagnosis by the merits of DTCWT (nearly shift invariant and reduced aliasing effects). Moreover, this method utilizes the relationship between the two dual-tree wavelet basis functions, instead of matching the single wavelet basis function to the signal being analyzed, which may speed up the signal processing and be employed in on-line engineering monitoring. The proposed method is applied to the analysis of bearing outer ring and shaft coupling vibration signals carrying fault information. The results confirm that the method performs better in extracting the fault features than the original DWT-based MSTSR, the wavelet transform with post spectral analysis, and EMD-based spectral analysis methods.

Improving the quality of the ECG signal by filtering in wavelet transform domain

NASA Astrophysics Data System (ADS)

DzierŻak, RóŻa; Surtel, Wojciech; Dzida, Grzegorz; Maciejewski, Marcin

2016-09-01

The article concerns the research methods of noise reduction occurring in the ECG signals. The method is based on the use of filtration in wavelet transform domain. The study was conducted on two types of signal - received during the rest of the patient and obtained during physical activity. For each of the signals 3 types of filtration were used. The study was designed to determine the effectiveness of various wavelets for de-noising signals obtained in both cases. The results confirm the suitability of the method for improving the quality of the electrocardiogram in case of both types of signals.

Long memory analysis by using maximal overlapping discrete wavelet transform

NASA Astrophysics Data System (ADS)

Shafie, Nur Amalina binti; Ismail, Mohd Tahir; Isa, Zaidi

2015-05-01

Long memory process is the asymptotic decay of the autocorrelation or spectral density around zero. The main objective of this paper is to do a long memory analysis by using the Maximal Overlapping Discrete Wavelet Transform (MODWT) based on wavelet variance. In doing so, stock market of Malaysia, China, Singapore, Japan and United States of America are used. The risk of long term and short term investment are also being looked into. MODWT can be analyzed with time domain and frequency domain simultaneously and decomposing wavelet variance to different scales without loss any information. All countries under studied show that they have long memory. Subprime mortgage crisis in 2007 is occurred in the United States of America are possible affect to the major trading countries. Short term investment is more risky than long term investment.

A study of stationarity in time series by using wavelet transform

NASA Astrophysics Data System (ADS)

Dghais, Amel Abdoullah Ahmed; Ismail, Mohd Tahir

2014-07-01

In this work the core objective is to apply discrete wavelet transform (DWT) functions namely Haar, Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets in non-stationary financial time series data from US stock market (DJIA30). The data consists of 2048 daily data of closing index starting from December 17, 2004 until October 23, 2012. From the unit root test the results show that the data is non stationary in the level. In order to study the stationarity of a time series, the autocorrelation function (ACF) is used. Results indicate that, Haar function is the lowest function to obtain noisy series as compared to Daubechies, Symmlet, Coiflet and discrete approximation of the meyer wavelets. In addition, the original data after decomposition by DWT is less noisy series than decomposition by DWT for return time series.

A data-driven wavelet-based approach for generating jumping loads

NASA Astrophysics Data System (ADS)

Chen, Jun; Li, Guo; Racic, Vitomir

2018-06-01

This paper suggests an approach to generate human jumping loads using wavelet transform and a database of individual jumping force records. A total of 970 individual jumping force records of various frequencies were first collected by three experiments from 147 test subjects. For each record, every jumping pulse was extracted and decomposed into seven levels by wavelet transform. All the decomposition coefficients were stored in an information database. Probability distributions of jumping cycle period, contact ratio and energy of the jumping pulse were statistically analyzed. Inspired by the theory of DNA recombination, an approach was developed by interchanging the wavelet coefficients between different jumping pulses. To generate a jumping force time history with N pulses, wavelet coefficients were first selected randomly from the database at each level. They were then used to reconstruct N pulses by the inverse wavelet transform. Jumping cycle periods and contract ratios were then generated randomly based on their probabilistic functions. These parameters were assigned to each of the N pulses which were in turn scaled by the amplitude factors βi to account for energy relationship between successive pulses. The final jumping force time history was obtained by linking all the N cycles end to end. This simulation approach can preserve the non-stationary features of the jumping load force in time-frequency domain. Application indicates that this approach can be used to generate jumping force time history due to single people jumping and also can be extended further to stochastic jumping loads due to groups and crowds.

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Probing Mantle Heterogeneity Across Spatial Scales

NASA Astrophysics Data System (ADS)

Hariharan, A.; Moulik, P.; Lekic, V.

2017-12-01

Inferences of mantle heterogeneity in terms of temperature, composition, grain size, melt and crystal structure may vary across local, regional and global scales. Probing these scale-dependent effects require quantitative comparisons and reconciliation of tomographic models that vary in their regional scope, parameterization, regularization and observational constraints. While a range of techniques like radial correlation functions and spherical harmonic analyses have revealed global features like the dominance of long-wavelength variations in mantle heterogeneity, they have limited applicability for specific regions of interest like subduction zones and continental cratons. Moreover, issues like discrepant 1-D reference Earth models and related baseline corrections have impeded the reconciliation of heterogeneity between various regional and global models. We implement a new wavelet-based approach that allows for structure to be filtered simultaneously in both the spectral and spatial domain, allowing us to characterize heterogeneity on a range of scales and in different geographical regions. Our algorithm extends a recent method that expanded lateral variations into the wavelet domain constructed on a cubed sphere. The isolation of reference velocities in the wavelet scaling function facilitates comparisons between models constructed with arbitrary 1-D reference Earth models. The wavelet transformation allows us to quantify the scale-dependent consistency between tomographic models in a region of interest and investigate the fits to data afforded by heterogeneity at various dominant wavelengths. We find substantial and spatially varying differences in the spectrum of heterogeneity between two representative global Vp models constructed using different data and methodologies. Applying the orthonormality of the wavelet expansion, we isolate detailed variations in velocity from models and evaluate additional fits to data afforded by adding such complexities to long-wavelength variations. Our method provides a way to probe and evaluate localized features in a multi-scale description of mantle heterogeneity.

LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

NASA Astrophysics Data System (ADS)

Cheng, Jun; Zhang, Jun; Tian, Jinwen

2015-12-01

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.

The identification of multi-cave combinations in carbonate reservoirs based on sparsity constraint inverse spectral decomposition

NASA Astrophysics Data System (ADS)

Li, Qian; Di, Bangrang; Wei, Jianxin; Yuan, Sanyi; Si, Wenpeng

2016-12-01

Sparsity constraint inverse spectral decomposition (SCISD) is a time-frequency analysis method based on the convolution model, in which minimizing the l1 norm of the time-frequency spectrum of the seismic signal is adopted as a sparsity constraint term. The SCISD method has higher time-frequency resolution and more concentrated time-frequency distribution than the conventional spectral decomposition methods, such as short-time Fourier transformation (STFT), continuous-wavelet transform (CWT) and S-transform. Due to these good features, the SCISD method has gradually been used in low-frequency anomaly detection, horizon identification and random noise reduction for sandstone and shale reservoirs. However, it has not yet been used in carbonate reservoir prediction. The carbonate fractured-vuggy reservoir is the major hydrocarbon reservoir in the Halahatang area of the Tarim Basin, north-west China. If reasonable predictions for the type of multi-cave combinations are not made, it may lead to an incorrect explanation for seismic responses of the multi-cave combinations. Furthermore, it will result in large errors in reserves estimation of the carbonate reservoir. In this paper, the energy and phase spectra of the SCISD are applied to identify the multi-cave combinations in carbonate reservoirs. The examples of physical model data and real seismic data illustrate that the SCISD method can detect the combination types and the number of caves of multi-cave combinations and can provide a favourable basis for the subsequent reservoir prediction and quantitative estimation of the cave-type carbonate reservoir volume.

Rapid limit tests for metal impurities in pharmaceutical materials by X-ray fluorescence spectroscopy using wavelet transform filtering.

PubMed

Arzhantsev, Sergey; Li, Xiang; Kauffman, John F

2011-02-01

We introduce a new method for analysis of X-ray fluorescence (XRF) spectra based on continuous wavelet transform filters, and the method is applied to the determination of toxic metals in pharmaceutical materials using hand-held XRF spectrometers. The method uses the continuous wavelet transform to filter the signal and noise components of the spectrum. We present a limit test that compares the wavelet domain signal-to-noise ratios at the energies of the elements of interest to an empirically determined signal-to-noise decision threshold. The limit test is advantageous because it does not require the user to measure calibration samples prior to measurement, though system suitability tests are still recommended. The limit test was evaluated in a collaborative study that involved five different hand-held XRF spectrometers used by multiple analysts in six separate laboratories across the United States. In total, more than 1200 measurements were performed. The detection limits estimated for arsenic, lead, mercury, and chromium were 8, 14, 20, and 150 μg/g, respectively.

EIT Imaging Regularization Based on Spectral Graph Wavelets.

PubMed

Gong, Bo; Schullcke, Benjamin; Krueger-Ziolek, Sabine; Vauhkonen, Marko; Wolf, Gerhard; Mueller-Lisse, Ullrich; Moeller, Knut

2017-09-01

The objective of electrical impedance tomographic reconstruction is to identify the distribution of tissue conductivity from electrical boundary conditions. This is an ill-posed inverse problem usually solved under the finite-element method framework. In previous studies, standard sparse regularization was used for difference electrical impedance tomography to achieve a sparse solution. However, regarding elementwise sparsity, standard sparse regularization interferes with the smoothness of conductivity distribution between neighboring elements and is sensitive to noise. As an effect, the reconstructed images are spiky and depict a lack of smoothness. Such unexpected artifacts are not realistic and may lead to misinterpretation in clinical applications. To eliminate such artifacts, we present a novel sparse regularization method that uses spectral graph wavelet transforms. Single-scale or multiscale graph wavelet transforms are employed to introduce local smoothness on different scales into the reconstructed images. The proposed approach relies on viewing finite-element meshes as undirected graphs and applying wavelet transforms derived from spectral graph theory. Reconstruction results from simulations, a phantom experiment, and patient data suggest that our algorithm is more robust to noise and produces more reliable images.